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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. 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

  3. Variability of single trial brain activation predicts fluctuations in reaction time.

    PubMed

    Bender, Stephan; Banaschewski, Tobias; Roessner, Veit; Klein, Christoph; Rietschel, Marcella; Feige, Bernd; Brandeis, Daniel; Laucht, Manfred

    2015-03-01

    Brain activation stability is crucial to understanding attention lapses. EEG methods could provide excellent markers to assess neuronal response variability with respect to temporal (intertrial coherence) and spatial variability (topographic consistency) as well as variations in activation intensity (low frequency variability of single trial global field power). We calculated intertrial coherence, topographic consistency and low frequency amplitude variability during target P300 in a continuous performance test in 263 15-year-olds from a cohort with psychosocial and biological risk factors. Topographic consistency and low frequency amplitude variability predicted reaction time fluctuations (RTSD) in a linear model. Higher RTSD was only associated with higher psychosocial adversity in the presence of the homozygous 6R-10R dopamine transporter haplotype. We propose that topographic variability of single trial P300 reflects noise as well as variability in evoked cortical activation patterns. Dopaminergic neuromodulation interacted with environmental and biological risk factors to predict behavioural reaction time variability.

  4. 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

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

    PubMed

    Ohata, Ryu; Ogawa, Kenji; Imamizu, Hiroshi

    2016-06-02

    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.

  6. 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

  7. 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

  8. 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.

  9. 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

  10. 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

  11. 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.

  12. 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

  13. 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.

  14. Preparatory delay activity in the monkey parietal reach region predicts reach reaction times.

    PubMed

    Snyder, Lawrence H; Dickinson, Anthony R; Calton, Jeffrey L

    2006-10-01

    To acquire something that we see, visual spatial information must ultimately result in the activation of the appropriate set of muscles. This sensory to motor transformation requires an interaction between information coding target location and information coding which effector will be moved. Activity in the monkey parietal reach region (PRR) reflects both spatial information and the effector (arm or eye) that will be used in an upcoming reach or saccade task. To further elucidate the functional role of PRR in visually guided movement tasks and to obtain evidence that PRR signals are used to drive arm movements, we tested the hypothesis that increased neuronal activity during a preparatory delay period would lead to faster reach reaction times but would not be correlated with saccade reaction times. This proved to be the case only when the type of movement and not the spatial goal of that movement was known in advance. The correlation was strongest in cells that showed significantly more activity on arm reach compared with saccade trials. No significant correlations were found during delay periods in which spatial information was provided in advance. These data support the idea that PRR constitutes a bottleneck in the processing of spatial information for an upcoming arm reach. The lack of a correlation with saccadic reaction time also supports the idea that PRR processing is effector specific, that is, it is involved in specifying targets for arm movements but not targets for eye movements.

  15. 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.

  16. 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

  17. 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

  18. 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

  19. 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

  20. Automatic Earthquake Shear Stress Measurement Method Developed for Accurate Time- Prediction Analysis of Forthcoming Major Earthquakes Along Shallow Active Faults

    NASA Astrophysics Data System (ADS)

    Serata, S.

    2006-12-01

    The Serata Stressmeter has been developed to measure and monitor earthquake shear stress build-up along shallow active faults. The development work made in the past 25 years has established the Stressmeter as an automatic stress measurement system to study timing of forthcoming major earthquakes in support of the current earthquake prediction studies based on statistical analysis of seismological observations. In early 1982, a series of major Man-made earthquakes (magnitude 4.5-5.0) suddenly occurred in an area over deep underground potash mine in Saskatchewan, Canada. By measuring underground stress condition of the mine, the direct cause of the earthquake was disclosed. The cause was successfully eliminated by controlling the stress condition of the mine. The Japanese government was interested in this development and the Stressmeter was introduced to the Japanese government research program for earthquake stress studies. In Japan the Stressmeter was first utilized for direct measurement of the intrinsic lateral tectonic stress gradient G. The measurement, conducted at the Mt. Fuji Underground Research Center of the Japanese government, disclosed the constant natural gradients of maximum and minimum lateral stresses in an excellent agreement with the theoretical value, i.e., G = 0.25. All the conventional methods of overcoring, hydrofracturing and deformation, which were introduced to compete with the Serata method, failed demonstrating the fundamental difficulties of the conventional methods. The intrinsic lateral stress gradient determined by the Stressmeter for the Japanese government was found to be the same with all the other measurements made by the Stressmeter in Japan. The stress measurement results obtained by the major international stress measurement work in the Hot Dry Rock Projects conducted in USA, England and Germany are found to be in good agreement with the Stressmeter results obtained in Japan. Based on this broad agreement, a solid geomechanical

  1. Predictive coding of multisensory timing

    PubMed Central

    Shi, Zhuanghua; Burr, David

    2016-01-01

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

  2. Predictive coding of multisensory timing

    PubMed Central

    Shi, Zhuanghua; Burr, David

    2016-01-01

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

  3. Normalized Elution Time Prediction Utility

    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.

  4. 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

  5. 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.

  6. 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

  7. 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.

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

    PubMed

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

    2011-01-01

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

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

    PubMed

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

    2012-12-01

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

  10. 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

  11. 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

  12. Interactions of timing and prediction error learning.

    PubMed

    Kirkpatrick, Kimberly

    2014-01-01

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

  13. Interactions of timing and prediction error learning.

    PubMed

    Kirkpatrick, Kimberly

    2014-01-01

    Timing and prediction error learning have historically been treated as independent processes, but growing evidence has indicated that they are not orthogonal. Timing emerges at the earliest time point when conditioned responses are observed, and temporal variables modulate prediction error learning in both simple conditioning and cue competition paradigms. In addition, prediction errors, through changes in reward magnitude or value alter timing of behavior. Thus, there appears to be a bi-directional interaction between timing and prediction error learning. Modern theories have attempted to integrate the two processes with mixed success. A neurocomputational approach to theory development is espoused, which draws on neurobiological evidence to guide and constrain computational model development. Heuristics for future model development are presented with the goal of sparking new approaches to theory development in the timing and prediction error fields. PMID:23962670

  14. Dynamic visuomotor synchronization: quantification of predictive timing.

    PubMed

    Maruta, Jun; Heaton, Kristin J; Kryskow, Elisabeth M; Maule, Alexis L; Ghajar, Jamshid

    2013-03-01

    When a moving target is tracked visually, spatial and temporal predictions are used to circumvent the neural delay required for the visuomotor processing. In particular, the internally generated predictions must be synchronized with the external stimulus during continuous tracking. We examined the utility of a circular visual-tracking paradigm for assessment of predictive timing, using normal human subjects. Disruptions of gaze-target synchronization were associated with anticipatory saccades that caused the gaze to be temporarily ahead of the target along the circular trajectory. These anticipatory saccades indicated preserved spatial prediction but suggested impaired predictive timing. We quantified gaze-target synchronization with several indices, whose distributions across subjects were such that instances of extremely poor performance were identifiable outside the margin of error determined by test-retest measures. Because predictive timing is an important element of attention functioning, the visual-tracking paradigm and dynamic synchronization indices described here may be useful for attention assessment.

  15. 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.

  16. 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.

  17. 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.

  18. Predicting Pointing Time from Hand Strength

    NASA Astrophysics Data System (ADS)

    Biswas, Pradipta; Robinson, Peter

    Pointing tasks form a significant part of human-computer interaction in graphical user interfaces. We have developed a model to predict the task completion time for pointing tasks for people with motor-impairment. As part of the model, we have also developed a new scale of characterizing the extent of disability of users by measuring their grip strength. We have validated the model by conducting two trials involving people with motor-impairment and in both trials the model has predicted pointing time with statistically significant accuracy.

  19. Time Horizon in Students' Predictions of Grades.

    ERIC Educational Resources Information Center

    Manger, Terje; Teigen, Karl Halvor

    1988-01-01

    Eight and two months before their final exam, 252 undergraduates in Norway stated their expectations and hopes for examination grades. Correlations between expected and obtained grades were low. A shift from optimism to pessimism occurred. Results confirm the time horizon's crucial role in the prediction of academic achievement. (TJH)

  20. 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

  1. Time Will Show: Real Time Predictions during Interpersonal Action Perception

    PubMed Central

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

    2013-01-01

    Predictive processes are crucial not only for interpreting the actions of individual agents, but also to predict how, in the context of a social interaction between two agents, the actions of one agent relate to the actions of a second agent. In the present study we investigated whether, in the context of a communicative interaction between two agents, observers can use the actions of one agent to predict when the action of a second agent will take place. Participants observed point-light displays of two agents (A and B) performing separate actions. In the communicative condition, the action performed by agent B responded to a communicative gesture performed by agent A. In the individual condition, agent A's communicative action was substituted with a non-communicative action. For each condition, we manipulated the temporal coupling of the actions of the two agents, by varying the onset of agent A's action. Using a simultaneous masking detection task, we demonstrated that the timing manipulation had a critical effect on the communicative condition, with the visual discrimination of agent B increasing linearly while approaching the original interaction timing. No effect of the timing manipulation was found for the individual condition. Our finding complements and extends previous evidence for interpersonal predictive coding, suggesting that the communicative gestures of one agent can serve not only to predict what the second agent will do, but also when his/her action will take place. PMID:23349992

  2. 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.

  3. 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.

  4. 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

  5. Time-dependence between upper arm muscles activity during rapid movements: observation of the proportional effects predicted by the kinematic theory.

    PubMed

    Plamondon, Réjean; Djioua, Moussa; Mathieu, Pierre A

    2013-10-01

    Rapid human movements can be assimilated to the output of a neuromuscular system with an impulse response modeled by a Delta-Lognormal equation. In such a model, the main assumption concerns the cumulative time delays of the response as it propagates toward the effector following a command. To verify the validity of this assumption, delays between bursts in electromyographic (EMG) signals of agonist and antagonist muscles activated during a rapid hand movement were investigated. Delays were measured between the surface EMG signals of six muscles of the upper limb during single rapid handwriting strokes. From EMG envelopes, regressions were obtained between the timing of the burst of activity produced by each monitored muscle. High correlation coefficients were obtained supporting the proportionality of the cumulative time delays, the basic hypothesis of the Delta-Lognormal model. A paradigm governing the sequence of muscle activities in a rapid movement could, in the long run, be useful for applications dealing with the analysis and synthesis of human movements.

  6. 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

  7. Prediction of Human Activity by Discovering Temporal Sequence Patterns.

    PubMed

    Li, Kang; Fu, Yun

    2014-08-01

    Early prediction of ongoing human activity has become more valuable in a large variety of time-critical applications. To build an effective representation for prediction, human activities can be characterized by a complex temporal composition of constituent simple actions and interacting objects. Different from early detection on short-duration simple actions, we propose a novel framework for long -duration complex activity prediction by discovering three key aspects of activity: Causality, Context-cue, and Predictability. The major contributions of our work include: (1) a general framework is proposed to systematically address the problem of complex activity prediction by mining temporal sequence patterns; (2) probabilistic suffix tree (PST) is introduced to model causal relationships between constituent actions, where both large and small order Markov dependencies between action units are captured; (3) the context-cue, especially interactive objects information, is modeled through sequential pattern mining (SPM), where a series of action and object co-occurrence are encoded as a complex symbolic sequence; (4) we also present a predictive accumulative function (PAF) to depict the predictability of each kind of activity. The effectiveness of our approach is evaluated on two experimental scenarios with two data sets for each: action-only prediction and context-aware prediction. Our method achieves superior performance for predicting global activity classes and local action units. PMID:26353344

  8. 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.

  9. 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.

  10. Structure-Based Predictions of Activity Cliffs

    PubMed Central

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

    2015-01-01

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

  11. 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.

  12. 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.

  13. 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.

  14. 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

  15. Time, Not Size, Matters for Striatal Reward Predictions to Dopamine.

    PubMed

    Burke, Christopher J; Tobler, Philippe N

    2016-07-01

    Midbrain dopamine neurons encode reward prediction errors. In this issue of Neuron, Takahashi et al. (2016) show that the ventral striatum provides dopamine neurons with prediction information specific to the timing, but not the quantity, of reward, suggesting a surprisingly nuanced neural implementation of reward prediction errors. PMID:27387646

  16. 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.

  17. Individualized Course Completion Time Predictions: Development of Instruments and Techniques.

    ERIC Educational Resources Information Center

    Wagner, Harold; And Others

    Research was undertaken to develop a system for predicting completion time in a self-paced training course. The hypotheses were developed that: 1) course content-related instruments would be better predictors of completion time than general aptitude measures; and that 2) a linear predictive function would provide the best description of the…

  18. 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.

  19. 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

  20. Real-time prediction of intense magnetic storms

    NASA Astrophysics Data System (ADS)

    Vieira, L. E.; Chen, J.; Gonzalez, W. D.

    In this paper a feature-based method for predicting geoeffective solar wind structures and intense geomagnetic storms is shown. The geoeffective structures were identified from observations of the plasma parameters and interplanetary magnetic field measurements by ACE satellite in the internal Lagrangean point (L1). The magnetospheric state was monitored by the Dst index. The proposed algorithm is based on the Chen IMF Prediction Model [Chen et al., 1996; Chen et al., 1997], which attempts to predict the occurrence of intense geomagnetic storms and how long they will last. It does this by looking for a change from positive or zero values of the Z-component of the interplanetary field, to negative values which often produce a geomagnetic storm. When it finds such a signature in ACE data, it models the strength of this component as a sinusoid and uses that to estimate the length of time the Z-component will remain negative. The longer the time, the more likely a storm will occur. Also, one can estimate the length of time that the storm will last. A detailed study of this algorithm using the ACE data for 1998-2001 period showed that this algorithm is capable to predict whether the magnetosphere will reach a certain activity level (Dst < -80nT) with a precision of approximately 80% and antecedence from some hours up to 10 hours. It was verified that the occurrence of false alarms and of periods in that the algorithm is not capable to predict the occurrence of magnetic storms are due mainly to three factors: (1) the rotation rate of the magnetic field during geoeffective events is not constant, as assumed; (2) the southward component of the interplanetary magnetic field is the most important parameter in the solar wind - magnetosphere coupling, but it is not the only one that have to be considered; and, (3) the algorithm doesn't take into account the previous magnetospheric conditions, that is, it is not capable to track the occurrence of multiple injections of energy

  1. 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) ...

  2. 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.

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

    NASA Astrophysics Data System (ADS)

    Hinrichs, Brant Eric

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

  4. Model-free quantification of time-series predictability

    NASA Astrophysics Data System (ADS)

    Garland, Joshua; James, Ryan; Bradley, Elizabeth

    2014-11-01

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

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

    PubMed

    Garland, Joshua; James, Ryan; Bradley, Elizabeth

    2014-11-01

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

  6. 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…

  7. 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

  8. Place cell activation predicts subsequent memory.

    PubMed

    Robitsek, R Jonathan; White, John A; Eichenbaum, Howard

    2013-10-01

    A major quandary in memory research is how hippocampal place cells, widely recognized as elements of a spatial map, contribute to episodic memory, our capacity to remember unique experiences that depends on hippocampal function. Here we recorded from hippocampal neurons as rats performed a T-maze alternation task in which they were required to remember a preceding experience over a delay in order to make a subsequent spatial choice. As it has been reported previously in other variations of this task, we observed differential firing that predicted correct subsequent choices, even as the animal traversed identical locations prior to the choice. Here we also observed that most place cells also fired differently on correct as compared to error trials. Among these cells, a large majority fired strongly before the delay or during the retrieval phase but were less active or failed to activate when the animal subsequently made an error. These findings join the place cell phenomenon with episodic memory performance dependent on the hippocampus, revealing that memory accuracy can be predicted by the activation of single place cells in the hippocampus.

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

    NASA Technical Reports Server (NTRS)

    Hutsell, Steven T.

    1996-01-01

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

  10. 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.

  11. Resting alpha activity predicts learning ability in alpha neurofeedback

    PubMed Central

    Wan, Feng; Nan, Wenya; Vai, Mang I.; Rosa, Agostinho

    2014-01-01

    Individuals differ in their ability to learn how to regulate the brain activity by neurofeedback. This study aimed to investigate whether the resting alpha activity can predict the learning ability in alpha neurofeedback. A total of 25 subjects performed 20 sessions of individualized alpha neurofeedback and the learning ability was assessed by three indices respectively: the training parameter changes between two periods, within a short period and across the whole training time. It was found that the resting alpha amplitude measured before training had significant positive correlations with all learning indices and could be used as a predictor for the learning ability prediction. This finding would help the researchers in not only predicting the training efficacy in individuals but also gaining further insight into the mechanisms of alpha neurofeedback. PMID:25071528

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

    ERIC Educational Resources Information Center

    Costa, Crist H.

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

  13. 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.

  14. Prospects for eruption prediction in near real-time

    USGS Publications Warehouse

    Voight, B.; Cornelius, R.R.

    1991-01-01

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

  15. The phantom robot - Predictive displays for teleoperation with time delay

    NASA Technical Reports Server (NTRS)

    Bejczy, Antal K.; Kim, Won S.; Venema, Steven C.

    1990-01-01

    An enhanced teleoperation technique for time-delayed bilateral teleoperator control is discussed. The control technique selected for time delay is based on the use of a high-fidelity graphics phantom robot that is being controlled in real time (without time delay) against the static task image. Thus, the motion of the phantom robot image on the monitor predicts the motion of the real robot. The real robot's motion will follow the phantom robot's motion on the monitor with the communication time delay implied in the task. Real-time high-fidelity graphics simulation of a PUMA arm is generated and overlaid on the actual camera view of the arm. A simple camera calibration technique is used for calibrated graphics overlay. A preliminary experiment is performed with the predictive display by using a very simple tapping task. The results with this simple task indicate that predictive display enhances the human operator's telemanipulation task performance significantly during free motion when there is a long time delay. It appears, however, that either two-view or stereoscopic predictive displays are necessary for general three-dimensional tasks.

  16. Predicting the timing of dynamic events through sound: Bouncing balls.

    PubMed

    Gygi, Brian; Giordano, Bruno L; Shafiro, Valeriy; Kharkhurin, Anatoliy; Zhang, Peter Xinya

    2015-07-01

    Dynamic information in acoustical signals produced by bouncing objects is often used by listeners to predict the objects' future behavior (e.g., hitting a ball). This study examined factors that affect the accuracy of motor responses to sounds of real-world dynamic events. In experiment 1, listeners heard 2-5 bounces from a tennis ball, ping-pong, basketball, or wiffle ball, and would tap to indicate the time of the next bounce in a series. Across ball types and number of bounces, listeners were extremely accurate in predicting the correct bounce time (CT) with a mean prediction error of only 2.58% of the CT. Prediction based on a physical model of bouncing events indicated that listeners relied primarily on temporal cues when estimating the timing of the next bounce, and to a lesser extent on the loudness and spectral cues. In experiment 2, the timing of each bounce pattern was altered to correspond to the bounce timing pattern of another ball, producing stimuli with contradictory acoustic cues. Nevertheless, listeners remained highly accurate in their estimates of bounce timing. This suggests that listeners can adopt their estimates of bouncing-object timing based on acoustic cues that provide most veridical information about dynamic aspects of object behavior.

  17. Predicting travel time to limit congestion at a highway bottleneck

    NASA Astrophysics Data System (ADS)

    Davis, L. C.

    2010-09-01

    A new method is proposed to predict the travel time on a highway route with a bottleneck caused by an on-ramp. The method takes advantage of the slow variation of the bottleneck throughput when congestion exists. The predicted travel time for a vehicle leaving the origin is given by the current number of vehicles on the route divided by the estimated throughput. The latter is an average of N/T recorded as each vehicle reaches the destination where N is the number of vehicles at the start of the trip and T is the time to complete the trip. Drivers divert to an off-ramp when the predicted travel time exceeds a target value. The target could be historical average travel times of alternative routes or chosen to limit the amount of congestion. Simulations employing three-phase traffic theory show that the travel time converges to the target value and remains close to or below it with the proposed prediction strategy. Strong oscillations in travel time obtained when other strategies are used for diversion do not develop with the new method because the inherent delay is effectively removed.

  18. Predicting the timing of dynamic events through sound: Bouncing balls.

    PubMed

    Gygi, Brian; Giordano, Bruno L; Shafiro, Valeriy; Kharkhurin, Anatoliy; Zhang, Peter Xinya

    2015-07-01

    Dynamic information in acoustical signals produced by bouncing objects is often used by listeners to predict the objects' future behavior (e.g., hitting a ball). This study examined factors that affect the accuracy of motor responses to sounds of real-world dynamic events. In experiment 1, listeners heard 2-5 bounces from a tennis ball, ping-pong, basketball, or wiffle ball, and would tap to indicate the time of the next bounce in a series. Across ball types and number of bounces, listeners were extremely accurate in predicting the correct bounce time (CT) with a mean prediction error of only 2.58% of the CT. Prediction based on a physical model of bouncing events indicated that listeners relied primarily on temporal cues when estimating the timing of the next bounce, and to a lesser extent on the loudness and spectral cues. In experiment 2, the timing of each bounce pattern was altered to correspond to the bounce timing pattern of another ball, producing stimuli with contradictory acoustic cues. Nevertheless, listeners remained highly accurate in their estimates of bounce timing. This suggests that listeners can adopt their estimates of bouncing-object timing based on acoustic cues that provide most veridical information about dynamic aspects of object behavior. PMID:26233044

  19. 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.

  20. Time Series Analysis and Prediction of AE and Dst Data

    NASA Astrophysics Data System (ADS)

    Takalo, J.; Lohikiski, R.; Timonen, J.; Lehtokangas, M.; Kaski, K.

    1996-12-01

    A new method to analyse the structure function has been constructed and used in the analysis of the AE time series for the years 1978-85 and Dst time series for 1957-84. The structure function (SF) was defined by S(l) = <|x(ti + lDt) - x(ti)|>, where Dt is the sampling time, l is an integer, and <|.|> denotes the average of absolute values. If a time series is self-affine its SF should scale for small values of l as S(l) is proportional to lH, where 0 < H < 1 is called the scaling exponent. It is known that for power-law (coloured) noise, which has P ~ f-a, a ~ 2H + 1 for 1 < a < 3. In this work the scaling exponent H was analysed by considering the local slopes dlog(S(l))/dlog(l) between two adjacent points as a function of l. For self-affine time series the local slopes should stay constant, at least for small values of l. The AE time series was found to be affine such that the scaling exponent changes at a time scale of 113 (+/-9) minutes. On the other hand, in the SF function analysis, the Dst data were dominated by the 24-hour and 27-day periods. The 27-day period was further modulated by the annual variation. These differences between the two time series arise from the difference in their periodicities in relation to their respective characteristic time scales. In the AE data the dominating periods are longer than that related to the characteristic time scale, i.e. they appear in the flatter part of the power spectrum. This is why the affinity is the dominating feature of the AE time series. In contrast with this the dominating periods of the Dst data are shorter than the characteristic time scale, and appear in the steeper part of the spectrum. Consequently periodicity is the dominating feature of the Dst data. Because of their different dynamic characteristics, prediction of Dst and AE time series appear to presuppose rather different approaches. In principle it is easier to produce the gross features of the Dst time series correctly as it is periodicity

  1. 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.

  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. 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

  4. 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

  5. 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).

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

    PubMed Central

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

    2016-01-01

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

  7. Predicting mining activity with parallel genetic algorithms

    USGS Publications Warehouse

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

    2005-01-01

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

  8. 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.

  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. 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…

  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. 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.

  13. Navy Global Predictions for the Dynamo Time Period

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

  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.

  15. 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.

  16. 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

  17. A study of predictability of SST at different time scales based on satellite time

    NASA Astrophysics Data System (ADS)

    Ding, Youzhuan; Fu, Dongyang; Wei, Zhihui; He, Xianqiang; Huang, Haiqing; Pan, Delu

    2008-12-01

    Sea surface temperature (SST) is both an important variable for weather and ocean forecasting, but also a key indicator of climate change. Predicting future SST at different time scales constitutes an important scientific problem. The traditional approach to prediction is achieved through numerical simulation, but it is difficult to obtain a detailed knowledge of ocean initial conditions and forcing. This paper proposes a improved prediction system based on SOFT proposed by Alvarez et al and studies the predictability of SST at different time scales, i.e., 5 day, 10 day, 15 day, 20 day and month ahead. This method is used to forecast the SST in the Yangtze River estuary and its adjacent areas. The period of time ranging from Jan 1st 2000 to Dec 31st 2005 is employed to build the prediction system and the period of time ranging from Jan 1st 2006 to Dec 31st 2007 is employed to validate the performance of this prediction system. Results indicate: The prediction errors of 5 day,10 day,15 day, 20 day and monthly ahead are 0.78°C,0.86°C,0.90°C,1.00°C and 1.45°C respectively. The longer of time scales prediction, the worse of prediction capability. Compared with the SOFT system proposed by Alvarez et al, the improved prediction system is more robust. Merging more satellite data and trying to better reflect the real state of ocean variables, we can greatly improve the predictive precision of long time scale.

  18. Mathematical models for predicting indoor air quality from smoking activity.

    PubMed Central

    Ott, W R

    1999-01-01

    Much progress has been made over four decades in developing, testing, and evaluating the performance of mathematical models for predicting pollutant concentrations from smoking in indoor settings. Although largely overlooked by the regulatory community, these models provide regulators and risk assessors with practical tools for quantitatively estimating the exposure level that people receive indoors for a given level of smoking activity. This article reviews the development of the mass balance model and its application to predicting indoor pollutant concentrations from cigarette smoke and derives the time-averaged version of the model from the basic laws of conservation of mass. A simple table is provided of computed respirable particulate concentrations for any indoor location for which the active smoking count, volume, and concentration decay rate (deposition rate combined with air exchange rate) are known. Using the indoor ventilatory air exchange rate causes slightly higher indoor concentrations and therefore errs on the side of protecting health, since it excludes particle deposition effects, whereas using the observed particle decay rate gives a more accurate prediction of indoor concentrations. This table permits easy comparisons of indoor concentrations with air quality guidelines and indoor standards for different combinations of active smoking counts and air exchange rates. The published literature on mathematical models of environmental tobacco smoke also is reviewed and indicates that these models generally give good agreement between predicted concentrations and actual indoor measurements. PMID:10350523

  19. Regional Travel-Time Predictions, Uncertainty and Location Improvement

    SciTech Connect

    Flanagan, M; Myers, S

    2004-07-15

    We investigate our ability to improve regional travel-time prediction and seismic event location using an a priori three-dimensional (3D) velocity model of Western Eurasia and North Africa (WENA 1.0). Three principal results are presented. First, the 3D WENA 1.0 velocity model improves travel-time prediction over the IASPI91 model, as measured by variance reduction, for regional phases recorded at 22 stations throughout the modeled region, including aseismic areas. Second, a distance-dependent uncertainty model is developed and tested for the WENA 1.0 model. Third, relocation using WENA 1.0 and the associated uncertainty model provides an end-to-end validation test. Model validation is based on a comparison of approximately 10,000 Pg, Pn, and P travel-time predictions and empirical observations from ground truth (GT) events. Ray coverage for the validation dataset provides representative, regional-distances sampling across Eurasia and North Africa. The WENA 1.0 model markedly improves travel-time predictions for most stations with an average variance reduction of 14% for all ray paths. We find that improvement is station dependent, with some stations benefiting greatly from WENA predictions (25% at OBN, and 16% at BKR), some stations showing moderate improvement (12% at ARU, and 17% at NIL), and some stations benefiting only slightly (7% at AAE, and 8% at TOL). We further test WENA 1.0 by relocating five calibration events. Again, relocation of these events is dependent on ray paths that evenly sample WENA 1.0 and therefore provide an unbiased assessment of location performance. These results highlight the importance of accurate GT datasets in assessing regional travel-time models and demonstrate that an a priori 3D model can markedly improve our ability to locate small magnitude events in a regional monitoring context.

  20. Parent Modeling: Perceptions of Parents’ Physical Activity Predict Girls’ Activity throughout Adolescence

    PubMed Central

    Madsen, Kristine A.; McCulloch, Charles; Crawford, Patricia B.

    2009-01-01

    Objectives To determine if parent modeling of physical activity has a differential impact on girls’ physical activity (PA) by race, if the association declines over time, and to assess the contribution of parent modeling to girls’ activity relative to other potential predictors. Study design Longitudinal examination of parent modeling’s impact on future log transformed metabolic equivalents (log METs) of leisure-time PA among 1213 African-American and 1166 Caucasian girls in the NHLBI Growth and Health Study, from age 9-10 through 18-19 years, using linear regression. Race interaction terms and time trends were examined. Results Girls’ perceptions of parent modeling significantly predicted future log METs in each study year; associations remained stable over time and were similar by race. Girls’ perception of parent PA better predicted girl log METs than did parent self-report. On average, girls reporting that their parents exercised ≥3x/week were about 50% more active than girls with sedentary parents. Conclusions Girls’ perception of parent activity predicts PA for girls throughout adolescence, despite age-associated decreases in PA. We did not find differences in this association by race. Interventions designed to increase parental activity may improve parent health, positively influence daughters’ activity, and begin to address disparities in cardiovascular health. PMID:18789455

  1. 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.

  2. 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

  3. Individual differences in time perspective predict autonoetic experience.

    PubMed

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

    2011-09-01

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

  4. 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.

  5. 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

  6. 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

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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

  13. 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.

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

    NASA Astrophysics Data System (ADS)

    Uyeda, S.; Varotsos, P.

    2011-12-01

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

  15. 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

  16. 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.

  17. 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.

  18. Prediction of Solar Activity Based on Neuro-Fuzzy Modeling

    NASA Astrophysics Data System (ADS)

    Attia, Abdel-Fattah; Abdel-Hamid, Rabab; Quassim, Maha

    2005-03-01

    This paper presents an application of the neuro-fuzzy modeling to analyze the time series of solar activity, as measured through the relative Wolf number. The neuro-fuzzy structure is optimized based on the linear adapted genetic algorithm with controlling population size (LAGA-POP). Initially, the dimension of the time series characteristic attractor is obtained based on the smallest regularity criterion (RC) and the neuro-fuzzy model. Then the performance of the proposed approach, in forecasting yearly sunspot numbers, is favorably compared to that of other published methods. Finally, a comparison predictions for the remaining part of the 22nd and the whole 23rd cycle of the solar activity are presented.

  19. 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

  20. 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.

  1. Predicting clinical image delivery time by monitoring PACS queue behavior.

    PubMed

    King, Nelson E; Documet, Jorge; Liu, Brent

    2006-01-01

    The expectation of rapid image retrieval from PACS users contributes to increased information technology (IT) infrastructure investments to increase performance as well as continuing demands upon PACS administrators to respond to "slow" system performance. The ability to provide predicted delivery times to a PACS user may curb user expectations for "fastest" response especially during peak hours. This, in turn, could result in a PACS infrastructure tailored to more realistic performance demands. A PACS with a stand-alone architecture under peak load typically holds study requests in a queue until the DICOM C-Move command can take place. We investigate the contents of a stand-alone architecture PACS RetrieveSend queue and identified parameters and behaviors that enable a more accurate prediction of delivery time. A prediction algorithm for studies delayed in a stand-alone PACS queue can be extendible to other potential bottlenecks such as long-term storage archives. Implications of a queue monitor in other PACS architectures are also discussed.

  2. Forecasts of time averages with a numerical weather prediction model

    NASA Technical Reports Server (NTRS)

    Roads, J. O.

    1986-01-01

    Forecasts of time averages of 1-10 days in duration by an operational numerical weather prediction model are documented for the global 500 mb height field in spectral space. Error growth in very idealized models is described in order to anticipate various features of these forecasts and in order to anticipate what the results might be if forecasts longer than 10 days were carried out by present day numerical weather prediction models. The data set for this study is described, and the equilibrium spectra and error spectra are documented; then, the total error is documented. It is shown how forecasts can immediately be improved by removing the systematic error, by using statistical filters, and by ignoring forecasts beyond about a week. Temporal variations in the error field are also documented.

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

    NASA Technical Reports Server (NTRS)

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

    2014-01-01

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

  4. 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.

  5. 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.

  6. 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

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

    PubMed

    Hafele, J C; Keating, R E

    1972-07-14

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

  8. 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...

  9. 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.

  10. Data assimialation for real-time prediction and reanalysis

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

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

  11. 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

  12. 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.

  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. 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

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. Multi-scale description and prediction of financial time series

    NASA Astrophysics Data System (ADS)

    Nawroth, A. P.; Friedrich, R.; Peinke, J.

    2010-08-01

    A new method is proposed that allows a reconstruction of time series based on higher order multi-scale statistics given by a hierarchical process. This method is able to model financial time series not only on a specific scale but for a range of scales. The method itself is based on the general n-scale joint probability density, which can be extracted directly from given data. It is shown how based on this n-scale statistics, general n-point probabilities can be estimated from which predictions can be achieved. Exemplary results are shown for the German DAX index. The ability to model correctly the behaviour of the original process for different scales simultaneously and in time is demonstrated. As a main result it is shown that this method is able to reproduce the known volatility cluster, although the model contains no explicit time dependence. Thus a new mechanism is shown how, in a stationary multi-scale process, volatility clustering can emerge.

  20. 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

  1. 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

  2. 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

  3. 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

  4. 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

  5. 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

  6. Time-driven activity-based costing.

    PubMed

    Kaplan, Robert S; Anderson, Steven R

    2004-11-01

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

  7. Time-driven activity-based costing.

    PubMed

    Kaplan, Robert S; Anderson, Steven R

    2004-11-01

    In the classroom, activity-based costing (ABC) looks like a great way to manage a company's limited resources. But executives who have tried to implement ABC in their organizations on any significant scale have often abandoned the attempt in the face of rising costs and employee irritation. They should try again, because a new approach sidesteps the difficulties associated with large-scale ABC implementation. In the revised model, managers estimate the resource demands imposed by each transaction, product, or customer, rather than relying on time-consuming and costly employee surveys. This method is simpler since it requires, for each group of resources, estimates of only two parameters: how much it costs per time unit to supply resources to the business's activities (the total overhead expenditure of a department divided by the total number of minutes of employee time available) and how much time it takes to carry out one unit of each kind of activity (as estimated or observed by the manager). This approach also overcomes a serious technical problem associated with employee surveys: the fact that, when asked to estimate time spent on activities, employees invariably report percentages that add up to 100. Under the new system, managers take into account time that is idle or unused. Armed with the data, managers then construct time equations, a new feature that enables the model to reflect the complexity of real-world operations by showing how specific order, customer, and activity characteristics cause processing times to vary. This Tool Kit uses concrete examples to demonstrate how managers can obtain meaningful cost and profitability information, quickly and inexpensively. Rather than endlessly updating and maintaining ABC data,they can now spend their time addressing the deficiencies the model reveals: inefficient processes, unprofitable products and customers, and excess capacity. PMID:15559451

  8. 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

  9. 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

  10. New Model Predicts Fire Activity in South America

    NASA Video Gallery

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

  11. 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.

  12. 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

  13. 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

  14. Leisure time activities of elementary school children.

    PubMed

    Harrell, J S; Gansky, S A; Bradley, C B; McMurray, R G

    1997-01-01

    The three most common leisure time activities of 2,200 third and fourth grade children (mean age 8.8 + 0.8; 50.7% girls) and the association of the intensity levels of those activities with demographic variables and risk factors for cardiovascular disease are reported. Activities reported most often by boys were playing video games (33%), playing football (32%), bicycling (31%), watching television (28%), and playing basketball (26%). The girls reported doing homework (39%), bicycling (31%), watching television (30%), dancing (27%), and reading (23%). Overall, the children, especially girls, reported fairly sedentary activities, with an average metabolic equivalent level of 4.2 for girls and 4.8 for boys. Among boys, African Americans reported more vigorous activities than Whites, but the activities reported by White girls were somewhat more vigorous than those reported by non-White girls. Children from a higher socioeconomic status (SES), especially boys, reported a greater proportion of sedentary activities than lower SES children. The risk factors of cholesterol, blood pressure, skinfold thickness, and body mass index were not significantly associated with total activity score. However, significantly more nonobese than obese children reported a vigorous (high-intensity) activity as one of their top three activities.

  15. 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…

  16. 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.

  17. 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.

  18. Activities of the IERS Working Group on prediction

    NASA Astrophysics Data System (ADS)

    Wooden, W.

    2008-04-01

    The International Earth Rotation and Reference Systems Service (IERS) established a Working Group on Prediction (WGP) to investigate what IERS prediction products are useful to the user community in addition to making a detailed examination of the fundamental properties of the different input data sets and algorithms. The major goals and objectives of the WGP are to determine the desired Earth orientation prediction products, the importance of observational accuracy, which types of input data provide an optimal prediction, the strengths and weaknesses of various prediction algorithms, and the interactions between series and algorithms that are beneficial or harmful. To focus the research efforts of the WGP, the user community was polled to ascertain what prediction products are needed and at what level of accuracy. The current status of WGP activities and the anticipated future directions are presented.

  19. Mouse activity across time scales: fractal scenarios.

    PubMed

    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 slowwave 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 (0.01 s to 2 s: waking state and 0.01 s to 0.1 s: 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 (30 s to 300 s: waking state and 0.3 s to 5 s: 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 anticorrelation. 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

  20. 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.

  1. Recognition of Short Time-Paired Activities

    NASA Astrophysics Data System (ADS)

    Chaminda, Hapugahage Thilak; Klyuev, Vitaly; Naruse, Keitaro; Osano, Minetada

    We undertake numerous activities in our daily life and for some of those we forget to complete the action as originally intended. Significant aspects while performing most of these actions might be: “pairing of both hands simultaneously” and “short time consumption”. In this work an attempt has been made to recognize those kinds of Paired Activities (PAs), which are easy to forget, and to provide a method to remind about uncompleted PAs. To represent PAs, a study was done on opening and closing of various bottles. A model to define PAs, which simulated the paired behavior of both hands, is proposed, called “Paired Activity Model” (PAM). To recognize PAs using PAM, Paired Activity Recognition Algorithm (PARA) was implemented. Paired motion capturing was done by accelerometers, which were worn by subjects on the wrist areas of both hands. Individual and correlative behavior of both hands was used to recognize exact PA among other activities. Artificial Neural Network (ANN) algorithm was used for data categorization in PARA. ANN significantly outperformed the support vector machine algorithm in real time evaluations. In the user-independent case, PARA achieved recognition rates of 96% for only target PAs and 91% for target PAs undertaken amidst unrelated activities.

  2. 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.

  3. Global cortical activity predicts shape of hand during grasping

    PubMed Central

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

    2015-01-01

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

  4. Airport noise predicts song timing of European birds.

    PubMed

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

    2016-09-01

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

  5. Airport noise predicts song timing of European birds.

    PubMed

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

    2016-09-01

    Anthropogenic noise is of increasing concern to biologists and medical scientists. Its detrimental effects on human health have been well studied, with the high noise levels from air traffic being of particular concern. However, less is known about the effects of airport noise pollution on signal masking in wild animals. Here, we report a relationship between aircraft noise and two major features of the singing behavior of birds. We found that five of ten songbird species began singing significantly earlier in the morning in the vicinity of a major European airport than their conspecifics at a quieter control site. As birds at both sites started singing before the onset of air traffic in the morning, this suggests that the birds in the vicinity of the airport advanced their activity to gain more time for unimpaired singing before the massive plane noise set in. In addition, we found that during the day, chaffinches avoided singing during airplane takeoffs, but only when the noise exceeded a certain threshold, further suggesting that the massive noise caused by the airport can impair acoustic communication in birds. Overall, our study indicates that birds may be adjusting their mating signals and time budgets in response to aircraft noise. PMID:27648232

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

    SciTech Connect

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

    2015-10-10

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

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

    PubMed

    Mochizuki, Koji; Takayama, Kozo

    2014-01-01

    This study reports the results of applying the time-temperature superposition principle (TTSP) to the prediction of color changes in liquid formulations. A sample solution consisting of L-tryptophan and glucose was used as the model liquid formulation for the Maillard reaction. After accelerated aging treatment at elevated temperatures, the Commission Internationale de l'Eclairage (CIE) LAB color parameters (a*, b*, L*, and E*ab) of the sample solution were measured using a spectrophotometer. The TTSP was then applied to a kinetic analysis of the color changes. The calculated values of the apparent activation energy of a*, b*, L*, and ΔE*ab were 105.2, 109.8, 91.6, and 103.7 kJ/mol, respectively. The predicted values of the color parameters at 40°C were calculated using Arrhenius plots for each of the color parameters. A comparison of the relationships between the experimental and predicted values of each color parameter revealed the coefficients of determination for a*, b*, L*, and ΔE*ab to be 0.961, 0.979, 0.960, and 0.979, respectively. All the R(2) values were sufficiently high, and these results suggested that the prediction was highly reliable. Kinetic analysis using the TTSP was successfully applied to calculating the apparent activation energy and to predicting the color changes at any temperature or duration. PMID:25450630

  8. 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

  9. 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

  10. 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

  11. 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.

  12. 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

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

    NASA Astrophysics Data System (ADS)

    De Felice, Matteo; Alessandri, Andrea

    2015-04-01

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

  14. 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.

  15. 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

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

    PubMed

    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

  17. 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

  18. 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.

  19. Evaluating the uncertainty of predicting future climate time series at the hourly time scale

    NASA Astrophysics Data System (ADS)

    Caporali, E.; Fatichi, S.; Ivanov, V. Y.

    2011-12-01

    A stochastic downscaling methodology is developed to generate hourly, point-scale time series for several meteorological variables, such as precipitation, cloud cover, shortwave radiation, air temperature, relative humidity, wind speed, and atmospheric pressure. The methodology uses multi-model General Circulation Model (GCM) realizations and an hourly weather generator, AWE-GEN. Probabilistic descriptions of factors of change (a measure of climate change with respect to historic conditions) are computed for several climate statistics and different aggregation times using a Bayesian approach that weights the individual GCM contributions. The Monte Carlo method is applied to sample the factors of change from their respective distributions thereby permitting the generation of time series in an ensemble fashion, which reflects the uncertainty of climate projections of future as well as the uncertainty of the downscaling procedure. Applications of the methodology and probabilistic expressions of certainty in reproducing future climates for the periods, 2000 - 2009, 2046 - 2065 and 2081 - 2100, using the 1962 - 1992 period as the baseline, are discussed for the location of Firenze (Italy). The climate predictions for the period of 2000 - 2009 are tested against observations permitting to assess the reliability and uncertainties of the methodology in reproducing statistics of meteorological variables at different time scales.

  20. 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.

  1. 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…

  2. 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

  3. 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.

  4. 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

  5. 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

  6. 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.

  7. Visualization and Interpretation of Support Vector Machine Activity Predictions.

    PubMed

    Balfer, Jenny; Bajorath, Jürgen

    2015-06-22

    Support vector machines (SVMs) are among the preferred machine learning algorithms for virtual compound screening and activity prediction because of their frequently observed high performance levels. However, a well-known conundrum of SVMs (and other supervised learning methods) is the black box character of their predictions, which makes it difficult to understand why models succeed or fail. Herein we introduce an approach to rationalize the performance of SVM models based upon the Tanimoto kernel compared with the linear kernel. Model comparison and interpretation are facilitated by a visualization technique, making it possible to identify descriptor features that determine compound activity predictions. An implementation of the methodology has been made freely available. PMID:25988274

  8. 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.

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

    ERIC Educational Resources Information Center

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

    2005-01-01

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

  10. 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…

  11. A novel approach to predict active sites of enzyme molecules.

    PubMed

    Chou, Kuo-Chen; Cai, Yu-dong

    2004-04-01

    Enzymes are critical in many cellular signaling cascades. With many enzyme structures being solved, there is an increasing need to develop an automated method for identifying their active sites. However, given the atomic coordinates of an enzyme molecule, how can we predict its active site? This is a vitally important problem because the core of an enzyme molecule is its active site from the viewpoints of both pure scientific research and industrial application. In this article, a topological entity was introduced to characterize the enzymatic active site. Based on such a concept, the covariant discriminant algorithm was formulated for identifying the active site. As a paradigm, the serine hydrolase family was demonstrated. The overall success rate by jackknife test for a data set of 88 enzyme molecules was 99.92%, and that for a data set of 50 independent enzyme molecules was 99.91%. Meanwhile, it was shown through an example that the prediction algorithm can also be used to find any typographic error of a PDB file in annotating the constituent amino acids of catalytic triad and to suggest a possible correction. The very high success rates are due to the introduction of a covariance matrix in the prediction algorithm that makes allowance for taking into account the coupling effects among the key constituent atoms of active site. It is anticipated that the novel approach is quite promising and may become a useful high throughput tool in enzymology, proteomics, and structural bioinformatics. PMID:14997541

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

    PubMed Central

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

    2016-01-01

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

  13. 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

  14. 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…

  15. 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.

  16. 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

  17. Using theoretical descriptors in structure activity relationships: Validating toxicity predictions

    SciTech Connect

    Famini, G.R.; Wilson, L.Y.; Chester, N.A.; Sterling, P.A.

    1995-12-01

    Quantitative Structure Activity Relationships (QSAR) and Linear Free Energy Relationships (LFER) are very useful for correlating toxicological data, and in characterizating trends in terms of structural and electronic effects. Several years ago, we developed a series of equations correlating a number of toxicity tests with theoretically determined descriptors. One of these tests was the Microtox test, using the degradation in light from Photobacteriurn phosphoreum. Recently, several new compounds have been tested in our laboratory using the Microtox test, and compared against the predicted values. The agreement between experimental and theoretical results will be discussed, as will reasons for {open_quotes}good{close_quotes} or {open_quotes}poor{close_quotes} predictions.

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

    PubMed Central

    Griffith, Simon C.

    2014-01-01

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

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

    USGS Publications Warehouse

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

    2012-01-01

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

  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. 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.

  2. 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.'

  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. Timing of food intake predicts weight loss effectiveness

    PubMed Central

    Garaulet, Marta; Gómez-Abellán, Purificación; Alburquerque-Béjar, Juan J; Lee, Yu-Chi; Ordovás, Jose M; Scheer, Frank AJL

    2013-01-01

    Background 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. Objective To evaluate the role of food-timing in weight-loss effectiveness in a sample of 420 individuals who followed a 20-week weight-loss treatment. Methods Participants (49.5% females; age [mean+/−SD]: 42±11 years; BMI: 31.4±5.4 kg/m2) were grouped in early-eaters and late-eaters, according to the timing of the main meal (lunch in this Mediterranean population). 51% of the subjects were early-eaters and 49% were late-eaters (lunch time before and after 3:00 PM, respectively), energy intake and expenditure, appetite hormones, CLOCK genotype, sleep duration and chronotype were studied. Results Late lunch eaters lost less weight and displayed a slower weight-loss rate during the 20 weeks of treatment than early-eaters (P=0.002). Surprisingly, energy intake, dietary composition, estimated energy expenditure, appetite hormones and sleep duration was similar between both groups. Nevertheless, late-eaters were more evening-types, had less energetic breakfasts, and skipped breakfast more frequently that early-eaters (P<0.05). CLOCK rs4580704 SNP associated with the timing of the main meal (P=0.015) with a higher frequency of minor allele (C) carriers among the late-eaters (P=0.041). Neither sleep duration, nor CLOCK SNPs or Morning/Evening chronotype was independently associated with weight-loss (P>0.05). Conclusions Eating late may influence the success of weight-loss therapy. Novel therapeutic strategies should incorporate not only the caloric intake and macronutrient distribution—as is classically done—but also the timing of food. PMID:23357955

  5. 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

  6. 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.

  7. 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.

  8. 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).…

  9. 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.

  10. 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.

  11. 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

  12. 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

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

    PubMed

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

    2015-08-01

    Predictions of phenological development for insect biological control agents may facilitate post-release monitoring efforts by allowing land managers to optimize the timing of monitoring activities. A logistic thermal time model was tested to predict phenology of immature stem-mining weevils, Hadroplontus litura F. (Coleoptera: Curculionidae), a biological control agent for Canada thistle, Cirsium arvense L. (Asterales: Asteraceae). Weevil eggs and larvae were collected weekly from Canada thistle stems in eastern North Dakota from May through July during 2010 and 2011. Head capsule widths of sampled larvae were measured at the widest point and plotted on a frequency histogram to establish ranges of head capsule widths associated with each instar. We found head capsule width ranges for first-, second-, and third-instar H. litura larvae were 165-324 µm, 346-490 µm, and 506-736 µm, respectively. Logistic regression models were developed to estimate the proportions of H. litura eggs, first-, and second-instar larvae in the weevil population as a function of thermal time. Model estimates of median development time for eggs, first instars, and second instars ranged from 219 ± 23 degree-days (DD) to 255 ± 27 DD, 556 ± 77 DD to 595 ± 81 DD, and 595 ± 109 DD to 653 ± 108 DD, respectively. Based on model validation statistics, model estimates for development timing were the most accurate for eggs and first instars and somewhat less accurate for second instars. These model predictions will help biological control practitioners obtain more accurate estimates of weevil population densities during post-release monitoring. PMID:26314053

  14. 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.

  15. 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.

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

    NASA Astrophysics Data System (ADS)

    Gersh-Range, Jessica; Perrin, Marshall D.

    2015-01-01

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

  17. Testing Theories That Predict Time Variation of Fundamental Constants

    NASA Astrophysics Data System (ADS)

    Landau, Susana J.; Vucetich, Hector

    2002-05-01

    We consider astronomical and local bounds on the time variation of fundamental constants to test some generic Kaluza-Klein-like models and some particular cases of Beckenstein theory. Bounds on the free parameters of the different theories are obtained. Furthermore, we find that none of the proposed models is able to explain recent results (as from Webb and coworkers in 1999 and 2001) claiming an observed variation of the fine-structure constant from quasar absorption systems at redshifts 0.5

  18. 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.

  19. 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.

  20. 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.

  1. 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

  2. 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

  3. 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.

  4. 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.

  5. 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.

  6. 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.

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

    NASA Technical Reports Server (NTRS)

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

    2014-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-05-01

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

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

    PubMed

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

    2013-05-22

    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.

  11. Effective phonocardiogram segmentation using time statistics and nonlinear prediction

    NASA Astrophysics Data System (ADS)

    Sridharan, Rajeswari; Janet, J.

    2010-02-01

    In the fields of image processing, signal processing and recognition, image Segmentation is an efficient method for segmenting the phonocardiograph signals (PCG) is offered. Primarily, inter-beat segmentation is approved and carried out by means of DII lead of the ECG recording for identifying the happenings of the very first heart sound (S1). Then, the intra-beat segmentation is attained by the use of recurrence time statistics (RTS), and that is very sensitive to variations of the renovated attractor in a state space derived from nonlinear dynamic analysis. Apart from this if the segmentation with RTS is unsuccessful, a special segmentation is proposed using threshold that is extracted from the high frequency rate decomposition and the feature extraction of the disorder is classified based on the murmur sounds. In the Inter-beat segmentation process the accuracy was 100% of the over all PCG recording. Taking into account a different level of PCG beats were strongly concerned by different types of cardiac murmurs and intra-beat segmentation are give up for an accurate result.

  12. Predictive motor activation during action observation in human infants.

    PubMed

    Southgate, Victoria; Johnson, Mark H; Osborne, Tamsin; Csibra, Gergely

    2009-12-23

    Certain regions of the human brain are activated both during action execution and action observation. This so-called 'mirror neuron system' has been proposed to enable an observer to understand an action through a process of internal motor simulation. Although there has been much speculation about the existence of such a system from early in life, to date there is little direct evidence that young infants recruit brain areas involved in action production during action observation. To address this question, we identified the individual frequency range in which sensorimotor alpha-band activity was attenuated in nine-month-old infants' electroencephalographs (EEGs) during elicited reaching for objects, and measured whether activity in this frequency range was also modulated by observing others' actions. We found that observing a grasping action resulted in motor activation in the infant brain, but that this activity began prior to observation of the action, once it could be anticipated. These results demonstrate not only that infants, like adults, display overlapping neural activity during execution and observation of actions, but that this activation, rather than being directly induced by the visual input, is driven by infants' understanding of a forthcoming action. These results provide support for theories implicating the motor system in action prediction. PMID:19675001

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

    ERIC Educational Resources Information Center

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

    2013-01-01

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

  14. Factors Predicting Physical Activity Among Children With Special Needs

    PubMed Central

    Yee, Chu Tang; Chung, Paul J.

    2013-01-01

    Introduction Obesity is especially prevalent among children with special needs. Both lack of physical activity and unhealthful eating are major contributing factors. The objective of our study was to investigate barriers to physical activity among these children. Methods We surveyed parents of the 171 children attending Vista Del Mar School in Los Angeles, a nonprofit school serving a socioeconomically diverse group of children with special needs from kindergarten through 12th grade. Parents were asked about their child’s and their own physical activity habits, barriers to their child’s exercise, and demographics. The response rate was 67%. Multivariate logistic regression was used to examine predictors of children being physically active at least 3 hours per week. Results Parents reported that 45% of the children were diagnosed with attention deficit hyperactivity disorder, 38% with autism, and 34% with learning disabilities; 47% of children and 56% of parents were physically active less than 3 hours per week. The top barriers to physical activity were reported as child’s lack of interest (43%), lack of developmentally appropriate programs (33%), too many behavioral problems (32%), and parents’ lack of time (29%). However, child’s lack of interest was the only parent-reported barrier independently associated with children’s physical activity. Meanwhile, children whose parents were physically active at least 3 hours per week were 4.2 times as likely to be physically active as children whose parents were less physically active (P = .01). Conclusion In this group of students with special needs, children’s physical activity was strongly associated with parental physical activity; parent-reported barriers may have had less direct effect. Further studies should examine the importance of parental physical activity among children with special needs. PMID:23866163

  15. Motor cortex activity predicts response alternation during sensorimotor decisions

    PubMed Central

    Pape, Anna-Antonia; Siegel, Markus

    2016-01-01

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

  16. Neural activity predicts attitude change in cognitive dissonance.

    PubMed

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

    2009-11-01

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

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

    NASA Astrophysics Data System (ADS)

    Georgakakos, K. P.

    2006-05-01

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

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

    PubMed

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

    2015-01-01

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

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

    PubMed Central

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

    2015-01-01

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

  20. Predicting flow at work: investigating the activities and job characteristics that predict flow states at work.

    PubMed

    Nielsen, Karina; Cleal, Bryan

    2010-04-01

    Flow (a state of consciousness where people become totally immersed in an activity and enjoy it intensely) has been identified as a desirable state with positive effects for employee well-being and innovation at work. Flow has been studied using both questionnaires and Experience Sampling Method (ESM). In this study, we used a newly developed 9-item flow scale in an ESM study combined with a questionnaire to examine the predictors of flow at two levels: the activities (brainstorming, planning, problem solving and evaluation) associated with transient flow states and the more stable job characteristics (role clarity, influence and cognitive demands). Participants were 58 line managers from two companies in Denmark; a private accountancy firm and a public elder care organization. We found that line managers in elder care experienced flow more often than accountancy line managers, and activities such as planning, problem solving, and evaluation predicted transient flow states. The more stable job characteristics included in this study were not, however, found to predict flow at work.

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

    PubMed

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

    2016-01-01

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

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed

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

    2016-01-01

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

  4. Prediction of Antifungal Activity of Gemini Imidazolium Compounds

    PubMed Central

    Pałkowski, Łukasz; Błaszczyński, Jerzy; Skrzypczak, Andrzej; Błaszczak, Jan; Nowaczyk, Alicja; Wróblewska, Joanna; Kożuszko, Sylwia; Gospodarek, Eugenia; Słowiński, Roman; Krysiński, Jerzy

    2015-01-01

    The progress of antimicrobial therapy contributes to the development of strains of fungi resistant to antimicrobial drugs. Since cationic surfactants have been described as good antifungals, we present a SAR study of a novel homologous series of 140 bis-quaternary imidazolium chlorides and analyze them with respect to their biological activity against Candida albicans as one of the major opportunistic pathogens causing a wide spectrum of diseases in human beings. We characterize a set of features of these compounds, concerning their structure, molecular descriptors, and surface active properties. SAR study was conducted with the help of the Dominance-Based Rough Set Approach (DRSA), which involves identification of relevant features and relevant combinations of features being in strong relationship with a high antifungal activity of the compounds. The SAR study shows, moreover, that the antifungal activity is dependent on the type of substituents and their position at the chloride moiety, as well as on the surface active properties of the compounds. We also show that molecular descriptors MlogP, HOMO-LUMO gap, total structure connectivity index, and Wiener index may be useful in prediction of antifungal activity of new chemical compounds. PMID:25961015

  5. Artificial neural network prediction of antisense oligodeoxynucleotide activity.

    PubMed

    Giddings, Michael C; Shah, Atul A; Freier, Sue; Atkins, John F; Gesteland, Raymond F; Matveeva, Olga V

    2002-10-01

    An mRNA transcript contains many potential antisense oligodeoxynucleotide target sites. Identification of the most efficacious targets remains an important and challenging problem. Building on separate work that revealed a strong correlation between the inclusion of short sequence motifs and the activity level of an oligo, we have developed a predictive artificial neural network system for mapping tetranucleotide motif content to antisense oligo activity. Trained for high-specificity prediction, the system has been cross-validated against a database of 348 oligos from the literature and a larger proprietary database of 908 oligos. In cross- validation tests the system identified effective oligos (i.e. oligos capable of reducing target mRNA expression to <25% that of the control) with 53% accuracy, in contrast to the <10% success rates commonly reported for trial-and-error oligo selection, suggesting a possible 5-fold reduction in the in vivo screening required to find an active oligo. We have implemented a web interface to a trained neural network. Given an RNA transcript as input, the system identifies the most likely oligo targets and provides estimates of the probabilities that oligos targeted against these sites will be effective. PMID:12364609

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

    PubMed

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

    2016-03-15

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

  7. Visual Analysis of time-dependent 2D Uncertainties in Decadal Climate Predictions

    NASA Astrophysics Data System (ADS)

    Böttinger, Michael; Röber, Niklas; Meier-Fleischer, Karin; Pohlmann, Holger

    2016-04-01

    Climate prediction systems used today for investigating the climate predictability on a decadal time scale are based on coupled global climate models. First, ensembles of hindcast experiments are carried out in order to derive the predictive skill of the prediction system. Then, in a second step, the prediction system is initialized with observations and actual future predictions are computed. The ensemble simulation techniques applied enable issuing of probabilistic information along with the quantities predicted. Different aspects of the uncertainty can be derived: The ensemble standard deviation (or ensemble spread) is a measure for the internal variability of the simulation, while the predictive skill is an inverse measure for the uncertainty in the prediction. In this work, we focus on the concurrent visualization of three related time-dependent 2D fields: the forecast variable itself, here the 2m temperature anomaly, along with the corresponding predictive skill and the ensemble spread which is given through the ensemble standard deviation. On the basis of temporally filtered data, animations are used to visualize the mean spatio-temporal development of the three quantities. Furthermore, seasonal analyses are similarly visualized in order to identify seasonal patterns. We show exemplary solutions produced with three different visualization systems: NCL, Avizo Green and ParaView. As example data set, we have used a decadal climate prediction carried out within the German research project "MiKlip - Decadal Predictions" using the MPI-M Earth System Model (MPI-ESM) from the Max Planck Institute for Meteorology in Hamburg.

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

    PubMed

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

    2016-09-01

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

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

    PubMed

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

    2016-09-01

    Personality is known to be relatively stable throughout adulthood. Nevertheless, it has been shown that major life events with high personal significance, including experiences engendered by psychedelic drugs, can have an enduring impact on some core facets of personality. In the present, balanced-order, placebo-controlled study, we investigated biological predictors of post-lysergic acid diethylamide (LSD) changes in personality. Nineteen healthy adults underwent resting state functional MRI scans under LSD (75µg, I.V.) and placebo (saline I.V.). The Revised NEO Personality Inventory (NEO-PI-R) was completed at screening and 2 weeks after LSD/placebo. Scanning sessions consisted of three 7.5-min eyes-closed resting-state scans, one of which involved music listening. A standardized preprocessing pipeline was used to extract measures of sample entropy, which characterizes the predictability of an fMRI time-series. Mixed-effects models were used to evaluate drug-induced shifts in brain entropy and their relationship with the observed increases in the personality trait openness at the 2-week follow-up. Overall, LSD had a pronounced global effect on brain entropy, increasing it in both sensory and hierarchically higher networks across multiple time scales. These shifts predicted enduring increases in trait openness. Moreover, the predictive power of the entropy increases was greatest for the music-listening scans and when "ego-dissolution" was reported during the acute experience. These results shed new light on how LSD-induced shifts in brain dynamics and concomitant subjective experience can be predictive of lasting changes in personality. Hum Brain Mapp 37:3203-3213, 2016. © 2016 Wiley Periodicals, Inc. PMID:27151536

  10. Parahippocampal cortex activation during context reinstatement predicts item recollection

    PubMed Central

    Diana, Rachel A.; Yonelinas, Andrew P.; Ranganath, Charan

    2014-01-01

    Episodic memory is the binding of an event with information about the context in which that event (or item) was experienced. The context of an event may include its spatial and temporal location as well as goal-directed, conscious thoughts evoked during the event. We call this latter type of information “cognitive context.” The Binding of Items and Context (BIC) theory of medial temporal lobe function proposes that parahippocampal cortex (PHc) plays a key role in processing cognitive context. Therefore, we predicted that activity in PHc during reactivation of a previously experienced cognitive context would be correlated with item recollection, even when the associated item and its episodic binding had not yet been retrieved. Using a novel paradigm, we measured brain activation with fMRI in response to covert reinstatement of a cognitive context, prior to presenting an item memory probe. Contexts were studied with multiple items to ensure that spontaneous item retrieval would not occur prior to the test probe. At test, contexts were reinstated for eight seconds before the test probe was presented. We manipulated whether the reinstated context matched the encoding context of the test probe that followed. For such matching contexts, we found that increased PHc activation prior to the test probe predicted recollection following the test probe. If a context unrelated to the eventual test item probe was reinstated, there was no such association between PHc activation during context reinstatement and eventual memory judgments. These findings suggest that PHc activation is correlated with cognitive context retrieval. PMID:23937182

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

    PubMed

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

    2015-12-01

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

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

    NASA Astrophysics Data System (ADS)

    Ilonidis, Stathis; Bobra, Monica; Couvidat, Sebastien

    2015-08-01

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

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

    EPA Science Inventory

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

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

    DOE PAGESBeta

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

    2016-01-19

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2008-11-01

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

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

    USGS Publications Warehouse

    Endo, E.T.; Murray, T.

    1991-01-01

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

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

    PubMed

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

    2015-09-24

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

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

    PubMed

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

    2015-09-24

    Circadian clocks drive daily rhythms in physiology and behavior which allow organisms to anticipate predictable daily changes in the environment. In most mammals, circadian rhythms result in nocturnal activity patterns although plasticity of the circadian system allows activity patterns to shift to different times of day. Such plasticity is seen when food access is restricted to a few hours during the resting (light) phase resulting in food anticipatory activity (FAA) in the hours preceding food availability. The mechanisms underlying FAA are unknown but data suggest the involvement of the reward system and homeostatic regulation of metabolism. We previously demonstrated the isolated effect of metabolism by inducing diurnality in response to energetic challenges. Here the importance of reward timing in inducing daytime activity is assessed. The daily activity distribution of mice earning palatable chocolate at their preferred time by working in a running wheel was compared with that of mice receiving a timed palatable meal at noon. Mice working for chocolate (WFC) without being energetically challenged increased their total daily activity but this did not result in a shift to diurnality. Providing a chocolate meal at noon each day increased daytime activity, identifying food timing as a factor capable of altering the daily distribution of activity and rest. These results show that timing of food reward and energetic challenges are both independently sufficient to induce diurnality in nocturnal mammals. FAA observed following timed food restriction is likely the result of an additive effect of distinct regulatory pathways activated by energetic challenges and food reward. PMID:26215921

  20. Predictions of adsorption equilibria of nonpolar hydrocarbons onto activated carbon

    SciTech Connect

    Do, D.D.; Wang, K.

    1998-12-08

    This paper presents a new approach to analyze the adsorption equilibria of nonpolar hydrocarbons onto activated carbon. The kinetic theory of gases and the 10-4-3 potential energy were employed to describe the adsorption process inside micropores. On the basis of this theory, a general isotherm model was proposed which possesses the potential capability of predicting the adsorption equilibria of an adsorbent by using the knowledge of its microporous structure and molecular properties of adsorbates. Experimental data of gases and vapors on Ajax activated carbon were employed to examine the model. Adsorption equilibria of binary mixtures were also investigated with the model, and it is shown that the model is capable of simulating the nonideal, or azeotropic, adsorption behaviors resulting from the structural heterogeneity of the adsorbent.

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

    PubMed

    Yuval; Broday, David M

    2010-06-15

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

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

    PubMed Central

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

    2015-01-01

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

  3. Predictive Analysis of Landslide Activity Using Remote Sensing Data

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

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

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

    NASA Technical Reports Server (NTRS)

    Richon, K.; Schatten, K.

    2003-01-01

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

  5. Error criteria for cross validation in the context of chaotic time series prediction.

    PubMed

    Lim, Teck Por; Puthusserypady, Sadasivan

    2006-03-01

    The prediction of a chaotic time series over a long horizon is commonly done by iterating one-step-ahead prediction. Prediction can be implemented using machine learning methods, such as radial basis function networks. Typically, cross validation is used to select prediction models based on mean squared error. The bias-variance dilemma dictates that there is an inevitable tradeoff between bias and variance. However, invariants of chaotic systems are unchanged by linear transformations; thus, the bias component may be irrelevant to model selection in the context of chaotic time series prediction. Hence, the use of error variance for model selection, instead of mean squared error, is examined. Clipping is introduced, as a simple way to stabilize iterated predictions. It is shown that using the error variance for model selection, in combination with clipping, may result in better models.

  6. The sequential structure of brain activation predicts skill.

    PubMed

    Anderson, John R; Bothell, Daniel; Fincham, Jon M; Moon, Jungaa

    2016-01-29

    In an fMRI study, participants were trained to play a complex video game. They were scanned early and then again after substantial practice. While better players showed greater activation in one region (right dorsal striatum) their relative skill was better diagnosed by considering the sequential structure of whole brain activation. Using a cognitive model that played this game, we extracted a characterization of the mental states that are involved in playing a game and the statistical structure of the transitions among these states. There was a strong correspondence between this measure of sequential structure and the skill of different players. Using multi-voxel pattern analysis, it was possible to recognize, with relatively high accuracy, the cognitive states participants were in during particular scans. We used the sequential structure of these activation-recognized states to predict the skill of individual players. These findings indicate that important features about information-processing strategies can be identified from a model-based analysis of the sequential structure of brain activation. PMID:26707716

  7. Working memory delay activity predicts individual differences in cognitive abilities.

    PubMed

    Unsworth, Nash; Fukuda, Keisuke; Awh, Edward; Vogel, Edward K

    2015-05-01

    A great deal of prior research has examined the relation between estimates of working memory and cognitive abilities. Yet, the neural mechanisms that account for these relations are still not very well understood. The current study explored whether individual differences in working memory delay activity would be a significant predictor of cognitive abilities. A large number of participants performed multiple measures of capacity, attention control, long-term memory, working memory span, and fluid intelligence, and latent variable analyses were used to examine the data. During two working memory change detection tasks, we acquired EEG data and examined the contralateral delay activity. The results demonstrated that the contralateral delay activity was significantly related to cognitive abilities, and importantly these relations were because of individual differences in both capacity and attention control. These results suggest that individual differences in working memory delay activity predict individual differences in a broad range of cognitive abilities, and this is because of both differences in the number of items that can be maintained and the ability to control access to working memory. PMID:25436671

  8. Working Memory Delay Activity Predicts Individual Differences in Cognitive Abilities

    PubMed Central

    Unsworth, Nash; Fukuda, Keisuke; Awh, Edward; Vogel, Edward K.

    2015-01-01

    A great deal of prior research has examined the relation between estimates of working memory and cognitive abilities. Yet, the neural mechanisms that account for these relations are still not very well understood. The current study explored whether individual differences in working memory delay activity would be a significant predictor of cognitive abilities. A large number of participants performed multiple measures of capacity, attention control, long-term memory, working memory span, and fluid intelligence, and latent variable analyses were used to examine the data. During two working memory change detection tasks, we acquired EEG data and examined the contra-lateral delay activity. The results demonstrated that the contralateral delay activity was significantly related to cognitive abilities, and importantly these relations were because of individual differences in both capacity and attention control. These results suggest that individual differences in working memory delay activity predict individual differences in a broad range of cognitive abilities, and this is because of both differences in the number of items that can be maintained and the ability to control access to working memory. PMID:25436671

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

    PubMed

    Wlotko, Edward W; Federmeier, Kara D

    2015-07-01

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

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

    PubMed

    Wlotko, Edward W; Federmeier, Kara D

    2015-07-01

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

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

    PubMed Central

    Wlotko, Edward W.; Federmeier, Kara D.

    2015-01-01

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

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

    PubMed Central

    Mestyán, Márton; Yasseri, Taha; Kertész, János

    2013-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Karnauskas, Kristopher B.; Li, Laifang

    2016-07-01

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

  14. Active euthanasia--time for a decision.

    PubMed Central

    Jeffrey, D

    1994-01-01

    There has been renewed interest in the moral arguments surrounding euthanasia. Some patients are now apprehensive of advanced medical technology which they fear may result in a prolonged and undignified death. In the current situation of scarce resources for health care, both patients and doctors could be coerced into considering active euthanasia if it was legally available. In this paper it is argued that doctors now need to make a clear statement rejecting active euthanasia but affirming that in certain cases passive euthanasia, or letting die, may be morally justifiable. PMID:8204323

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

    PubMed

    Al-Sakkaf, A; Jones, G

    2014-05-01

    Predicting campylobacteriosis cases is a matter of considerable concern in New Zealand, after the number of the notified cases was the highest among the developed countries in 2006. Thus, there is a need to develop a model or a tool to predict accurately the number of campylobacteriosis cases as the Microbial Risk Assessment Model used to predict the number of campylobacteriosis cases failed to predict accurately the number of actual cases. We explore the appropriateness of classical time series modelling approaches for predicting campylobacteriosis. Finding the most appropriate time series model for New Zealand data has additional practical considerations given a possible structural change, that is, a specific and sudden change in response to the implemented interventions. A univariate methodological approach was used to predict monthly disease cases using New Zealand surveillance data of campylobacteriosis incidence from 1998 to 2009. The data from the years 1998 to 2008 were used to model the time series with the year 2009 held out of the data set for model validation. The best two models were then fitted to the full 1998-2009 data and used to predict for each month of 2010. The Holt-Winters (multiplicative) and ARIMA (additive) intervention models were considered the best models for predicting campylobacteriosis in New Zealand. It was noticed that the prediction by an additive ARIMA with intervention was slightly better than the prediction by a Holt-Winter multiplicative method for the annual total in year 2010, the former predicting only 23 cases less than the actual reported cases. It is confirmed that classical time series techniques such as ARIMA with intervention and Holt-Winters can provide a good prediction performance for campylobacteriosis risk in New Zealand. The results reported by this study are useful to the New Zealand Health and Safety Authority's efforts in addressing the problem of the campylobacteriosis epidemic. PMID:23551848

  16. Predictions of narrow-band acoustic time reversal in the shallow ocean

    NASA Astrophysics Data System (ADS)

    Dungan, Michael Robert

    2000-10-01

    A time-reversing array (TRA) can retrofocus acoustic energy, in both time and space, to the original sound- source location without any environmental information. This unique capability may be degraded in time-dependent, lossy, or noisy acoustic environments. A broad computational and analytical investigation into narrow- band acoustic time reversal in the shallow ocean has been undertaken. This includes investigating (1)variability in the water column due to dynamic linear internal waves, (2)roughness in the ocean bottom, and (3)limiting orientations of TRAs. TRA retrofocusing performance predictions are primarily determined via monochromatic propagation simulations using the wide-angle parabolic equation code RAM (Collins 1993, 1994, and 1998). Results for the influence of source-array range, source depth, channel depth, acoustic frequency, bottom absorption, bottom roughness, internal wave strength, roundtrip time delay, and array orientation and spacing are presented. For a fixed channel geometry, higher frequencies, deeper sources, and lower bottom absorption improve TRA performance and allow retrofocusing at longer ranges. After several minutes in a dynamic shallow-water channel containing a random superposition of linear internal waves, there is significant TRA retrofocus amplitude decay, and the decay rate increases with increasing internal wave activity and acoustic frequency. Randomness in the environment, either from bottom roughness or random linear internal waves, reduces the predicted azimuthal angular width of the vertical-TRA retrofocus to as little as a fraction of a degree (compared to 360° for uniform environments) for source-array ranges from 2.5 to 20 km at frequencies from 250 Hz to 2 kHz. In a sound channel with bottom roughness, the azimuthal size of the retrofocus is predicted to be proportional to the roughness correlation length divided by the wavenumber, source-array range, and roughness RMS-height all raised to the three-halves power

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

    PubMed

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

    2012-06-30

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

  18. Haida Story Telling Time with Activity Folder.

    ERIC Educational Resources Information Center

    Cogo, Robert

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

  19. Correlates of reward-predictive value in learning-related hippocampal neural activity

    PubMed Central

    Okatan, Murat

    2009-01-01

    Temporal difference learning (TD) is a popular algorithm in machine learning. Two learning signals that are derived from this algorithm, the predictive value and the prediction error, have been shown to explain changes in neural activity and behavior during learning across species. Here, the predictive value signal is used to explain the time course of learning-related changes in the activity of hippocampal neurons in monkeys performing an associative learning task. The TD algorithm serves as the centerpiece of a joint probability model for the learning-related neural activity and the behavioral responses recorded during the task. The neural component of the model consists of spiking neurons that compete and learn the reward-predictive value of task-relevant input signals. The predictive-value signaled by these neurons influences the behavioral response generated by a stochastic decision stage, which constitutes the behavioral component of the model. It is shown that the time course of the changes in neural activity and behavioral performance generated by the model exhibits key features of the experimental data. The results suggest that information about correct associations may be expressed in the hippocampus before it is detected in the behavior of a subject. In this way, the hippocampus may be among the earliest brain areas to express learning and drive the behavioral changes associated with learning. Correlates of reward-predictive value may be expressed in the hippocampus through rate remapping within spatial memory representations, they may represent reward-related aspects of a declarative or explicit relational memory representation of task contingencies, or they may correspond to reward-related components of episodic memory representations. These potential functions are discussed in connection with hippocampal cell assembly sequences and their reverse reactivation during the awake state. The results provide further support for the proposal that neural

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

    NASA Astrophysics Data System (ADS)

    Carlton, Amelie B.

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

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

    ERIC Educational Resources Information Center

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

    2012-01-01

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

  2. TSPO ligand residence time: a new parameter to predict compound neurosteroidogenic efficacy.

    PubMed

    Costa, Barbara; Da Pozzo, Eleonora; Giacomelli, Chiara; Barresi, Elisabetta; Taliani, Sabrina; Da Settimo, Federico; Martini, Claudia

    2016-01-11

    The pharmacological activation of the cholesterol-binding Translocator Protein (TSPO) leads to an increase of endogenous steroids and neurosteroids determining benefic pleiotropic effects in several pathological conditions, including anxiety disorders. The relatively poor relationship between TSPO ligand binding affinities and steroidogenic efficacies prompted us to investigate the time (Residence Time, RT) that a number of compounds with phenylindolylglyoxylamide structure (PIGAs) spends in contact with the target. Here, given the poor availability of TSPO ligand kinetic parameters, a kinetic radioligand binding assay was set up and validated for RT determination using a theoretical mathematical model successfully applied to other ligand-target systems. TSPO ligand RT was quantified and the obtained results showed a positive correlation between the period for which a drug interacts with TSPO and the compound ability to stimulate steroidogenesis. Specifically, the TSPO ligand RT significantly fitted both with steroidogenic efficacy (Emax) and with area under the dose-response curve, a parameter combining drug potency and efficacy. A positive relation between RT and anxiolytic activity of three compounds was evidenced. In conclusion, RT could be a relevant parameter to predict the steroidogenic efficacy and the in vivo anxiolytic action of new TSPO ligands.

  3. T2 relaxation time post febrile status epilepticus predicts cognitive outcome.

    PubMed

    Barry, Jeremy M; Choy, ManKin; Dube, Celine; Robbins, Ashlee; Obenaus, Andre; Lenck-Santini, Pierre Pascal; Scott, Rod C; Baram, Tallie Z; Holmes, Gregory L

    2015-07-01

    Evidence from animal models and patient data indicates that febrile status epilepticus (FSE) in early development can result in permanently diminished cognitive abilities. To understand the variability in cognitive outcome following FSE, we used MRI to measure dynamic brain metabolic responses to the induction of FSE in juvenile rats. We then compared these measurements to the ability to learn an active avoidance spatial task weeks later. T2 relaxation times were significantly lower in FSE rats that were task learners in comparison to FSE non-learners. While T2 time in whole brain held the greatest predictive power, T2 in hippocampus and basolateral amygdala were also excellent predictors. These signal differences in response to FSE indicate that rats that fail to meet metabolic and oxygen demand are more likely to develop spatial cognition deficits. Place cells from FSE non-learners had significantly larger firing fields and higher in-field firing rate than FSE learners and control animals and imply increased excitability in the pyramidal cells of FSE non-learners. These findings suggest a mechanistic cause for the spatial memory deficits in active avoidance and are relevant to other acute neurological insults in early development where cognitive outcome is a concern. PMID:25939697

  4. TSPO ligand residence time: a new parameter to predict compound neurosteroidogenic efficacy

    PubMed Central

    Costa, Barbara; Da Pozzo, Eleonora; Giacomelli, Chiara; Barresi, Elisabetta; Taliani, Sabrina; Da Settimo, Federico; Martini, Claudia

    2016-01-01

    The pharmacological activation of the cholesterol-binding Translocator Protein (TSPO) leads to an increase of endogenous steroids and neurosteroids determining benefic pleiotropic effects in several pathological conditions, including anxiety disorders. The relatively poor relationship between TSPO ligand binding affinities and steroidogenic efficacies prompted us to investigate the time (Residence Time, RT) that a number of compounds with phenylindolylglyoxylamide structure (PIGAs) spends in contact with the target. Here, given the poor availability of TSPO ligand kinetic parameters, a kinetic radioligand binding assay was set up and validated for RT determination using a theoretical mathematical model successfully applied to other ligand-target systems. TSPO ligand RT was quantified and the obtained results showed a positive correlation between the period for which a drug interacts with TSPO and the compound ability to stimulate steroidogenesis. Specifically, the TSPO ligand RT significantly fitted both with steroidogenic efficacy (Emax) and with area under the dose-response curve, a parameter combining drug potency and efficacy. A positive relation between RT and anxiolytic activity of three compounds was evidenced. In conclusion, RT could be a relevant parameter to predict the steroidogenic efficacy and the in vivo anxiolytic action of new TSPO ligands. PMID:26750656

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

    NASA Astrophysics Data System (ADS)

    Miziński, Bartłomiej; Niedzielski, Tomasz

    2014-05-01

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

  6. Testing time-predictable earthquake recurrence by direct measurement of strain accumulation and release.

    PubMed

    Murray, Jessica; Segall, Paul

    2002-09-19

    Probabilistic estimates of earthquake hazard use various models for the temporal distribution of earthquakes, including the 'time-predictable' recurrence model formulated by Shimazaki and Nakata (which incorporates the concept of elastic rebound described as early as 1910 by H. F. Reid). This model states that an earthquake occurs when the fault recovers the stress relieved in the most recent earthquake. Unlike time-independent models (for example, Poisson probability), the time-predictable model is thought to encompass some of the physics behind the earthquake cycle, in that earthquake probability increases with time. The time-predictable model is therefore often preferred when adequate data are available, and it is incorporated in hazard predictions for many earthquake-prone regions, including northern California, southern California, New Zealand and Japan. Here we show that the model fails in what should be an ideal locale for its application -- Parkfield, California. We estimate rigorous bounds on the predicted recurrence time of the magnitude approximately 6 1966 Parkfield earthquake through inversion of geodetic measurements and we show that, according to the time-predictable model, another earthquake should have occurred by 1987. The model's poor performance in a relatively simple tectonic setting does not bode well for its successful application to the many areas of the world characterized by complex fault interactions.

  7. Development and Validation of a New Air Carrier Block Time Prediction Model and Methodology

    NASA Astrophysics Data System (ADS)

    Litvay, Robyn Olson

    Commercial airline operations rely on predicted block times as the foundation for critical, successive decisions that include fuel purchasing, crew scheduling, and airport facility usage planning. Small inaccuracies in the predicted block times have the potential to result in huge financial losses, and, with profit margins for airline operations currently almost nonexistent, potentially negate any possible profit. Although optimization techniques have resulted in many models targeting airline operations, the challenge of accurately predicting and quantifying variables months in advance remains elusive. The objective of this work is the development of an airline block time prediction model and methodology that is practical, easily implemented, and easily updated. Research was accomplished, and actual U.S., domestic, flight data from a major airline was utilized, to develop a model to predict airline block times with increased accuracy and smaller variance in the actual times from the predicted times. This reduction in variance represents tens of millions of dollars (U.S.) per year in operational cost savings for an individual airline. A new methodology for block time prediction is constructed using a regression model as the base, as it has both deterministic and probabilistic components, and historic block time distributions. The estimation of the block times for commercial, domestic, airline operations requires a probabilistic, general model that can be easily customized for a specific airline’s network. As individual block times vary by season, by day, and by time of day, the challenge is to make general, long-term estimations representing the average, actual block times while minimizing the variation. Predictions of block times for the third quarter months of July and August of 2011 were calculated using this new model. The resulting, actual block times were obtained from the Research and Innovative Technology Administration, Bureau of Transportation Statistics

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

    PubMed

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

    2013-01-01

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

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

    PubMed

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

    2013-01-01

    The theory of planned behaviour (TPB) can identify cognitions that predict differences in behaviour between individuals. However, it is not clear whether the TPB can predict the behaviour of an individual person. This study employs a series of n-of-1 studies and time series analyses to examine the ability of the TPB to predict physical activity (PA) behaviours of six individuals. Six n-of-1 studies were conducted, in which TPB cognitions and up to three PA behaviours (walking, gym workout and a personally defined PA) were measured twice daily for six weeks. Walking was measured by pedometer step count, gym attendance by self-report with objective validation of gym entry and the personally defined PA behaviour by self-report. Intra-individual variability in TPB cognitions and PA behaviour was observed in all participants. The TPB showed variable predictive utility within individuals and across behaviours. The TPB predicted at least one PA behaviour for five participants but had no predictive utility for one participant. Thus, n-of-1 designs and time series analyses can be used to test theory in an individual. PMID:22943555

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

    ERIC Educational Resources Information Center

    Xu, Jianzhong

    2010-01-01

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

  11. The Updated Solar Activity Prediction during the MAVEN Mission, but Should We Believe It?

    NASA Technical Reports Server (NTRS)

    Chamberlin, Philip

    2009-01-01

    Mars atmospheric processes are very dependent not only on the absolute level of the solar irradiance but also the changes in solar irradiance. Correlated with many of these irradiance changes, especially during solar flares, are large particle events called coronal mass ejections that themselves significantly drive processes in the Martian atmosphere. The NOAA Space Weather Prediction Center has issued a consensus solar cycle activity prediction for the upcoming solar cycle 24 maximum, and this maximum period of solar activity will be during the prime MAVEN science mission. This 'consensus' prediction calls for lower activity than the previous solar cycle maximum that occurred during the years 2001-2002, but looking at the wide spread of peer-reviewed predictions there is little faith that can be taken in any one prediction. This drives the importance of real-time measurements from the LPW/EUV diodes and the measurement and modeling results that will be improved upon using results from the Solar Dynamics Observatory (SDO).

  12. Predicting decisions in human social interactions using real-time fMRI and pattern classification.

    PubMed

    Hollmann, Maurice; Rieger, Jochem W; Baecke, Sebastian; Lützkendorf, Ralf; Müller, Charles; Adolf, Daniela; Bernarding, Johannes

    2011-01-01

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

  13. Structure activity relationships: their function in biological prediction

    SciTech Connect

    Schultz, T.W.

    1982-01-01

    Quantitative structure activity relationships provide a means of ranking or predicting biological effects based on chemical structure. For each compound used to formulate a structure activity model two kinds of quantitative information are required: (1) biological activity and (2) molecular properties. Molecular properties are of three types: (1) molecular shape, (2) physiochemical parameters, and (3) abstract quantitations of molecular structure. Currently the two best descriptors are the hydrophobic parameter, log 1-octanol/water partition coefficient (log P), and the /sup 1/X/sup v/(one-chi-v) molecular connectivity index. Biological responses can be divided into three main categories: (1) non-specific effects due to membrane perturbation, (2) non-specific effects due to interaction with functional groups of proteins, and (3) specific effects due to interaction with receptors. Twenty-six synthetic fossil fuel-related nitrogen-containing aromatic compounds were examined to determine the quantitative correlation between log P and /sup 1/X/sup v/ and population growth impairment of Tetrahymena pyriformis. Nitro-containing compounds are the most active, followed by amino-containing compounds and azaarenes. Within each analog series activity increases with alkyl substitution and ring addition. The planar model log BR = 0.5564 log P + 0.3000 /sup 1/X/sup v/ -2.0138 was determined using mono-nitrogen substituted compounds. Attempts to extrapolate this model to dinitrogen-containing molecules were, for the most part, unsuccessful because of a change in mode of action from membrane perturbation to uncoupling of oxidative phosphoralation.

  14. Pallidal spiking activity reflects learning dynamics and predicts performance

    PubMed Central

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

    2016-01-01

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

  15. A Geomagnetic Precursor Technique for Predicting the Solar Activity Cycle

    NASA Astrophysics Data System (ADS)

    Sobel, E. I.; Rabin, D. M.

    2015-12-01

    The Western hemisphere has been recording sunspot numbers since Galileo discovered sunspots in the early 17th century, and the roughly 11-year solar cycle has been recognized since the 19th century. However, predicting the strength of any particular cycle remains a relatively imprecise task. This project's aim was to update and improve a forecasting technique based on geomagnetic precursors of future solar activity The model is a refinement of R. J. Thompson's 1993 paper that relates the number of geomagnetically disturbed days, as defined by the aa and Ap indices, to the sum of the sunspot number in the current and the previous cycle, Rn + Rn-1.[1] The method exploits the fact that two cycles coexist for some period on the Sun near solar minimum and therefore that the number of sunspots and disturbed days during the declining phase of one cycle gives an indication of the following cycle's strength. We wrote and updated IDL software procedures to define disturbed days with varying threshold values and graphed Rn + Rn-1 against them. The aa threshold was derived from the Ap threshold. After comparing the graphs for Ap values from 20 to 50, an Ap threshold of 30 and the corresponding aa threshold of 44 were chosen as yielding the best correlation. Confidence regions were computed to provide a quantitative uncertainty on future predictions. The 80% confidence region gives a range of ±40 in sunspot number. [1] Thompson, R. J. (1993). A technique for predicting the amplitude of the solar cycle. Solar Physics, 148, 2, 383-388.

  16. Decreased dopamine activity predicts relapse in methamphetamine abusers

    SciTech Connect

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

    2011-01-20

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

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

    PubMed

    Xu, Chengcheng; Liu, Pan; Wang, Wei

    2016-09-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

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

  19. Autonomic activity during sleep predicts memory consolidation in humans.

    PubMed

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

    2016-06-28

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

  20. Predicting above normal wildfire activity in southern Europe as a function of meteorological drought

    NASA Astrophysics Data System (ADS)

    Gudmundsson, L.; Rego, F. C.; Rocha, M.; Seneviratne, S. I.

    2014-08-01

    Wildfires are a recurrent feature of ecosystems in southern Europe, regularly causing large ecological and socio-economic damages. For efficient management of this hazard, long lead time forecasts could be valuable tools. Using logistic regression, we show that the probability of above normal summer wildfire activity in the 1985-2010 time period can be forecasted as a function of meteorological drought with significant predictability (p \\lt 0.05) several months in advance. The results show that long lead time forecasts of this natural hazard are feasible in southern Europe, which could potentially aid decision-makers in the design of strategies for forest management.

  1. Predicting earthquakes by analyzing accelerating precursory seismic activity

    USGS Publications Warehouse

    Varnes, D.J.

    1989-01-01

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

  2. Target of rapamycin activation predicts lifespan in fruit flies

    PubMed Central

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

    2015-01-01

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

  3. Procalcitonin Predicts Real-Time PCR Results in Blood Samples from Patients with Suspected Sepsis

    PubMed Central

    Mencacci, Antonella; Leli, Christian; Cardaccia, Angela; Meucci, Marta; Moretti, Amedeo; D'Alò, Francesco; Farinelli, Senia; Pagliochini, Rita; Barcaccia, Mariella; Bistoni, Francesco

    2012-01-01

    Background Early diagnosis and rapid bacterial identification are of primary importance for outcome of septic patients. SeptiFast® (SF) real-time PCR assay is of potential utility in the etiological diagnosis of sepsis, but it cannot replace blood culture (BC) for routine use in clinical laboratory. Procalcitonin (PCT) is a marker of sepsis and can predict bacteremia in septic patients. The aim of the present study was to investigate whether PCT serum levels could predict SF results, and could help screening febrile patients in which a SF assay can improve the etiological diagnosis of sepsis. Methods From 1009 febrile patients with suspected sepsis, 1009 samples for BC, SF real-time PCR, and PCT determination were obtained simultaneously, and results were compared and statistically analysed. Receiver operating characteristic (ROC) curves were generated to determine the area under the curve and to identify which cut-off of PCT value produced the best sensitivity to detect SF results. Results Mean PCT values of sera drawn simultaneously with samples SF positive (35.42±61.03 ng/ml) or BC positive (23.14±51.56 ng/ml) for a pathogen were statistically higher than those drawn simultaneously with SF negative (0.84±1.67 ng/ml) or BC negative (2.79±16.64 ng/ml) samples (p<0.0001). For SF, ROC analysis showed an area under the curve of 0.927 (95% confidence interval: 0.899–0.955, p<0.0001). The PCT cut-off value of 0.37 ng/ml showed a negative predictive value of 99%, reducing the number of SF assays of 53.9%, still identifying the 96.4% of the pathogens. Conclusion PCT can be used in febrile patients with suspected sepsis to predict SF positive or negative results. A cut-off value of 0.37 ng/ml can be considered for optimal sensitivity, so that, in the routine laboratory activity, SF assay should not be used for diagnosis of sepsis in an unselected patient population with a PCT value <0.37 ng/ml. PMID:23300907

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

    PubMed

    Gulley, Tauna

    2013-04-01

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

  5. A simple accurate method to predict time of ponding under variable intensity rainfall

    NASA Astrophysics Data System (ADS)

    Assouline, S.; Selker, J. S.; Parlange, J.-Y.

    2007-03-01

    The prediction of the time to ponding following commencement of rainfall is fundamental to hydrologic prediction of flood, erosion, and infiltration. Most of the studies to date have focused on prediction of ponding resulting from simple rainfall patterns. This approach was suitable to rainfall reported as average values over intervals of up to a day but does not take advantage of knowledge of the complex patterns of actual rainfall now commonly recorded electronically. A straightforward approach to include the instantaneous rainfall record in the prediction of ponding time and excess rainfall using only the infiltration capacity curve is presented. This method is tested against a numerical solution of the Richards equation on the basis of an actual rainfall record. The predicted time to ponding showed mean error ≤7% for a broad range of soils, with and without surface sealing. In contrast, the standard predictions had average errors of 87%, and worst-case errors exceeding a factor of 10. In addition to errors intrinsic in the modeling framework itself, errors that arise from averaging actual rainfall records over reporting intervals were evaluated. Averaging actual rainfall records observed in Israel over periods of as little as 5 min significantly reduced predicted runoff (75% for the sealed sandy loam and 46% for the silty clay loam), while hourly averaging gave complete lack of prediction of ponding in some of the cases.

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed

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

    2015-01-01

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

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

    PubMed

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

    2016-08-01

    The removal of trace organic compounds (TOrCs) is of growing interest in water research and society. Powdered activated carbon (PAC) has been proven to be an effective method of removal for TOrCs in water, with the degree of effectiveness depending on dosage, contact time, and activated carbon type. In this study, the attenuation of TOrCs in three different secondary wastewater effluents using four PAC materials was studied in order to elucidate the effectiveness and efficacy of PAC for TOrC removal. With the notable exception of hydrochlorothiazide, all 14 TOrC indicators tested in this study exhibited a positive correlation of removal rate with their log Dow values, demonstrating that the main adsorption mechanism was hydrophobic interaction. As a predictive model, the modified Chick-Watson model, often used for the prediction of microorganism inactivation by disinfectants, was applied. The applied model exhibited good predictive power for TOrC attenuation by PAC in wastewater. In addition, surrogate models based upon spectroscopic measurements including UV absorbance at 254 nm and total fluorescence were applied to predict TOrC removal by PAC. The surrogate model was found to provide an excellent prediction of TOrC attenuation for all combinations of water quality and PAC type included in this study. The success of spectrometric parameters as surrogates in predicting TOrC attenuation by PAC are particularly useful because of their potential application in real-time on-line sensor monitoring and process control at full-scale water treatment plants, which could lead to significantly reduced operator response times and PAC operational optimization. PMID:27174829

  9. Activity predicts male reproductive success in a polygynous lizard.

    PubMed

    Keogh, J Scott; Noble, Daniel W A; Wilson, Eleanor E; Whiting, Martin J

    2012-01-01

    Activity patterns and social interactions play a key role in determining reproductive success, although this is poorly understood for species that lack overt social behaviour. We used genetic paternity analysis to quantify both multiple paternity and the relative roles of activity and social behaviour in determining reproductive success in a nondescript Australian lizard. During the breeding season we intensively followed and recorded the behaviour of a group of seven males and 13 females in a naturalistic outdoor enclosure to examine the relative roles of body size, activity and social interactions in determining male fertilization success. We found multiple paternity in 42% of clutches. No single behaviour was a significant predictor of male fertilization success in isolation, but male-female association, interactions and courtship explained 41% of the variation in male fertilization success. Males with the highest number of offspring sired invested heavily in interacting with females but spent very little time in interactions with males. These same males also sired offspring from more clutches. When taken collectively, an index of overall male activity, including locomotion and all social interactions, significantly explained 81% of the variation in the total number of offspring sired and 90% of the variation in the number of clutches in which males sired offspring. We suggest that the most successful male strategy is a form of endurance rivalry in which active mate searching and interactions with females have the greatest fitness benefits.

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

    PubMed Central

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

    2013-01-01

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

  11. Healthy work revisited: do changes in time strain predict well-being?

    PubMed

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

    2013-04-01

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

  12. Healthy work revisited: do changes in time strain predict well-being?

    PubMed

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

    2013-04-01

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

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

    NASA Technical Reports Server (NTRS)

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

    2011-01-01

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

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

    ERIC Educational Resources Information Center

    Smith, Anne Emilie; Powers, Sally I.

    2009-01-01

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

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed

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

    2016-06-01

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

  17. Pharmacokinetic and pharmacogenetic predictive markers of irinotecan activity and toxicity.

    PubMed

    Di Paolo, Antonello; Bocci, Guido; Polillo, Marialuisa; Del Re, Marzia; Di Desidero, Teresa; Lastella, Marianna; Danesi, Romano

    2011-12-01

    After the rapid development of new classes of antineoplastic drugs, research activities have focused their efforts to the identification of predictive markers of drug activity and tolerability. Irinotecan (CPT-11) may induce severe toxicities (diarrhea, neutropenia) that limit its clinical use, but the increasing knowledge of its pharmacokinetics offered a potential approach to treatment optimization. Pharmacokinetics, the first area of investigation, has identified markers such as biliary index, the relative extent of conversion and the glucuronidation ratio, which are capable to define the risk for severe adverse effects. Because of the existence of some issues concerning the adoption of pharmacokinetic strategies to optimize CPT-11 dose and schedule, analyses of genetic polymorphisms seemed to offer a more reliable and safer approach for the identification of patients at risk than pharmacokinetics. In this view, the uridine diphosphate glucuronosil transferase isoform 1A1 (UGT1A1) was associated with significant changes in disposition of CPT-11 and its metabolites, and consequently with treatment-induced toxicities. However, the complex pharmacokinetics of irinotecan and the involvement of several enzymes other than UGT (i.e., carboxyl estherases, CYP450 isoforms), and transmembrane transporters (ABCB1, ABCC1, ABCG2, SLCO1B1) make difficult the identification of patients with an optimal sensitivity and specificity, and a large part of variability among patients still remains unexplained. Furthermore, prospective clinical studies that should demonstrate the reliability of those pharmacokinetic and pharmacogenetic markers are still lacking. In the present review, pharmacokinetic and pharmacogenetic markers will be discussed. PMID:21787264

  18. Bi-Directional SIFT Predicts a Subset of Activating Mutations

    PubMed Central

    Lee, William; Lazarus, Robert A.; Zhang, Zemin

    2009-01-01

    Advancements in sequencing technologies have empowered recent efforts to identify polymorphisms and mutations on a global scale. The large number of variations and mutations found in these projects requires high-throughput tools to identify those that are most likely to have an impact on function. Numerous computational tools exist for predicting which mutations are likely to be functional, but none that specifically attempt to identify mutations that result in hyperactivation or gain-of-function. Here we present a modified version of the SIFT (Sorting Intolerant from Tolerant) algorithm that utilizes protein sequence alignments with homologous sequences to identify functional mutations based on evolutionary fitness. We show that this bi-directional SIFT (B-SIFT) is capable of identifying experimentally verified activating mutants from multiple datasets. B-SIFT analysis of large-scale cancer genotyping data identified potential activating mutations, some of which we have provided detailed structural evidence to support. B-SIFT could prove to be a valuable tool for efforts in protein engineering as well as in identification of functional mutations in cancer. PMID:20011534

  19. Innovation diffusion on time-varying activity driven networks

    NASA Astrophysics Data System (ADS)

    Rizzo, Alessandro; Porfiri, Maurizio

    2016-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Munasinghe, Lankeshwara; Ichise, Ryutaro

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

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

    ERIC Educational Resources Information Center

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

    1999-01-01

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

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

    PubMed Central

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

    2013-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Peterson, E. J.; Izad, O.; Tyler, D. J.

    2011-08-01

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

  4. Real-time seismic intensity prediction using frequency-dependent site amplification factors

    NASA Astrophysics Data System (ADS)

    Ogiso, Masashi; Aoki, Shigeki; Hoshiba, Mitsuyuki

    2016-05-01

    A promising approach for the next generation of earthquake early warning system is based on predicting ground motion directly from observed ground motion, without any information of hypocenter. In this study, we predicted seismic intensity at the target stations from the observed ground motion at adjacent stations, employing two different methods of correction for site amplification factors. The first method was frequency-dependent correction prediction, in which we used a digital causal filter to correct the site amplification for the observed waveform in the time domain. The second method was scalar correction, in which we used average differences in seismic intensity between two stations for the site amplification correction. Results from thousands of station pairs that covered almost all of Japan showed that seismic intensity prediction with frequency-dependent correction prediction was more accurate than prediction with scalar correction. Frequency-dependent correction for site amplification in the time domain may lead to more accurate prediction of ground motion in real time.

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

    PubMed

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

    2014-08-01

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

  6. Testing forecasts of a new Bayesian time-predictable model of eruption occurrence

    NASA Astrophysics Data System (ADS)

    Passarelli, L.; Sansò, B.; Sandri, L.; Marzocchi, W.

    2010-12-01

    In this paper we propose a model to forecast eruptions in a real forward perspective. Specifically, the model provides a forecast of the next eruption after the end of the last one, using only the data available up to that time. We focus our attention on volcanoes with open conduit regime and high eruption frequency. We assume a generalization of the classical time predictable model to describe the eruptive behavior of open conduit volcanoes and we use a Bayesian hierarchical model to make probabilistic forecasts. We apply the model to Kilauea volcano eruptive data and Mount Etna volcano flank eruption data. The aims of the proposed model are: (1) to test whether or not the Kilauea and Mount Etna volcanoes follow a time predictable behavior; (2) to discuss the volcanological implications of the time predictable model parameters inferred; (3) to compare the forecast capabilities of this model with other models present in literature. The results obtained using the MCMC sampling algorithm show that both volcanoes follow a time predictable behavior. The numerical values inferred for the parameters of the time predictable model suggest that the amount of the erupted volume could change the dynamics of the magma chamber refilling process during the repose period. The probability gain of this model compared with other models already present in literature is appreciably greater than zero. This means that our model provides better forecast than previous models and it could be used in a probabilistic volcanic hazard assessment scheme.

  7. Healing times and prediction of wound healing in neuropathic diabetic foot ulcers: a prospective study.

    PubMed

    Zimny, S; Pfohl, M

    2005-02-01

    Time line of wound healing and prediction of healing times in diabetic foot ulcers is an important issue. Usually, the percentage of wounds healed within a defined period is used for characterization of wound healing. R=sqrtA/pi (R, radius; A, planimetric wound area; pi, constant 3.14), and the wound radius reduction was 0.39 mm/week which was previously established. The initial average wound area was 96.9+/-13.1 mm2 (mean+/-SEM), and 3.61+/-1.6 mm 2 after ten weeks with an average healing time of 75.9 (95 %-CI 71-81) days. Using the equation mentioned above and the calculated weekly wound radius reduction, the predicted healing time in the test group was 86.9 (95 %-CI 73-101) days. The predicted and the observed healing times were significantly correlated with each other (r=0.55, p=0.0002). Providing standard care, the time needed for wound healing can reliably be predicted in neuropathic diabetic foot ulcers. This may be a useful tool in daily clinical practice to predict wound healing and recognize ulcers who do not respond adequately to the treatment. PMID:15772900

  8. A maintenance time prediction method considering ergonomics through virtual reality simulation.

    PubMed

    Zhou, Dong; Zhou, Xin-Xin; Guo, Zi-Yue; Lv, Chuan

    2016-01-01

    Maintenance time is a critical quantitative index in maintainability prediction. An efficient maintenance time measurement methodology plays an important role in early stage of the maintainability design. While traditional way to measure the maintenance time ignores the differences between line production and maintenance action. This paper proposes a corrective MOD method considering several important ergonomics factors to predict the maintenance time. With the help of the DELMIA analysis tools, the influence coefficient of several factors are discussed to correct the MOD value and the designers can measure maintenance time by calculating the sum of the corrective MOD time of each maintenance therbligs. Finally a case study is introduced, by maintaining the virtual prototype of APU motor starter in DELMIA, designer obtains the actual maintenance time by the proposed method, and the result verifies the effectiveness and accuracy of the proposed method. PMID:27536522

  9. A maintenance time prediction method considering ergonomics through virtual reality simulation.

    PubMed

    Zhou, Dong; Zhou, Xin-Xin; Guo, Zi-Yue; Lv, Chuan

    2016-01-01

    Maintenance time is a critical quantitative index in maintainability prediction. An efficient maintenance time measurement methodology plays an important role in early stage of the maintainability design. While traditional way to measure the maintenance time ignores the differences between line production and maintenance action. This paper proposes a corrective MOD method considering several important ergonomics factors to predict the maintenance time. With the help of the DELMIA analysis tools, the influence coefficient of several factors are discussed to correct the MOD value and the designers can measure maintenance time by calculating the sum of the corrective MOD time of each maintenance therbligs. Finally a case study is introduced, by maintaining the virtual prototype of APU motor starter in DELMIA, designer obtains the actual maintenance time by the proposed method, and the result verifies the effectiveness and accuracy of the proposed method.

  10. LPTA: location predictive and time adaptive data gathering scheme with mobile sink for wireless sensor networks.

    PubMed

    Zhu, Chuan; Wang, Yao; Han, Guangjie; Rodrigues, Joel J P C; Lloret, Jaime

    2014-01-01

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

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed

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

    2014-12-01

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

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

    ERIC Educational Resources Information Center

    Bolsinova, Maria; Tijmstra, Jesper

    2016-01-01

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

  14. A novel trajectory prediction control for proximate time-optimal digital control DC—DC converters

    NASA Astrophysics Data System (ADS)

    Qing, Wang; Ning, Chen; Shen, Xu; Weifeng, Sun; Longxing, Shi

    2014-09-01

    The purpose of this paper is to present a novel trajectory prediction method for proximate time-optimal digital control DC—DC converters. The control method provides pre-estimations of the duty ratio in the next several switching cycles, so as to compensate the computational time delay of the control loop and increase the control loop bandwidth, thereby improving the response speed. The experiment results show that the fastest transient response time of the digital DC—DC with the proposed prediction is about 8 μs when the load current changes from 0.6 to 0.1 A.

  15. Semiparametric Models of Time-dependent Predictive Values of Prognostic Biomarkers

    PubMed Central

    Zheng, Yingye; Cai, Tianxi; Stanford, Janet L.; Feng, Ziding

    2009-01-01

    Summary Rigorous statistical evaluation of the predictive values of novel biomarkers is critical prior to applying novel biomarkers into routine standard care. It is important to identify factors that influence the performance of a biomarker in order to determine the optimal conditions for test performance. We propose a covariate-specific time-dependent PPV curve to quantify the predictive accuracy of a prognostic marker measured on a continuous scale and with censored failure time outcome. The covariate effect is accommodated with a semiparametric regression model framework. In particular we adopt a smoothed survival time regression technique (Dabrowska, 1997) to account for the situation where risk for the disease occurrence and progression is likely to change over time. In addition, we provide asymptotic distribution theory and resampling-based procedures for making statistical inference on the covariate specific positive predictive values. We illustrate our approach with numerical studies and a dataset from a prostate cancer study. PMID:19397579

  16. CDRUG: a web server for predicting anticancer activity of chemical compounds.

    PubMed

    Li, Gong-Hua; Huang, Jing-Fei

    2012-12-15

    Cancer is the leading cause of death worldwide. Screening anticancer candidates from tens of millions of chemical compounds is expensive and time-consuming. A rapid and user-friendly web server, known as CDRUG, is described here to predict the anticancer activity of chemical compounds. In CDRUG, a hybrid score was developed to measure the similarity of different compounds. The performance analysis shows that CDRUG has the area under curve of 0.878, indicating that CDRUG is effective to distinguish active and inactive compounds.

  17. Predicting the dengue incidence in Singapore using univariate time series models.

    PubMed

    Dayama, Pankaj; Kameshwaran, S

    2013-01-01

    Dengue is endemic in Singapore with year-around transmission. Prediction of dengue incidence is important for effective use of limited resources for vector-control and contingency measures. In the work, we develop a set of time series models based on the observed weekly dengue incidence since 2000. The dengue incidence data of Singapore from 2000 - 2011 is used to develop and fit the predictive models. For testing and validation, we use the 2012 data at two levels: A) real versus predicted incidence and B) real versus predicted outbreak severity. The statistical measures of validation show that the models predict both the dengue incidence and the outbreak severity level with acceptable level of accuracy.

  18. Parallel effects of memory set activation and search on timing and working memory capacity

    PubMed Central

    Schweickert, Richard; Fortin, Claudette; Xi, Zhuangzhuang; Viau-Quesnel, Charles

    2014-01-01

    Accurately estimating a time interval is required in everyday activities such as driving or cooking. Estimating time is relatively easy, provided a person attends to it. But a brief shift of attention to another task usually interferes with timing. Most processes carried out concurrently with timing interfere with it. Curiously, some do not. Literature on a few processes suggests a general proposition, the Timing and Complex-Span Hypothesis: A process interferes with concurrent timing if and only if process performance is related to complex span. Complex-span is the number of items correctly recalled in order, when each item presented for study is followed by a brief activity. Literature on task switching, visual search, memory search, word generation and mental time travel supports the hypothesis. Previous work found that another process, activation of a memory set in long term memory, is not related to complex-span. If the Timing and Complex-Span Hypothesis is true, activation should not interfere with concurrent timing in dual-task conditions. We tested such activation in single-task memory search task conditions and in dual-task conditions where memory search was executed with concurrent timing. In Experiment 1, activating a memory set increased reaction time, with no significant effect on time production. In Experiment 2, set size and memory set activation were manipulated. Activation and set size had a puzzling interaction for time productions, perhaps due to difficult conditions, leading us to use a related but easier task in Experiment 3. In Experiment 3 increasing set size lengthened time production, but memory activation had no significant effect. Results here and in previous literature on the whole support the Timing and Complex-Span Hypotheses. Results also support a sequential organization of activation and search of memory. This organization predicts activation and set size have additive effects on reaction time and multiplicative effects on percent

  19. Respiratory motion prediction for tumor following radiotherapy by using time-variant seasonal autoregressive techniques.

    PubMed

    Ichiji, Kei; Homma, Noriyasu; Sakai, Masao; Takai, Yoshihiro; Narita, Yuichiro; Abe, Mokoto; Sugita, Norihiro; Yoshizawa, Makoto

    2012-01-01

    We develop a new prediction method of respiratory motion for accurate dynamic radiotherapy, called tumor following radiotherapy. The method is based on a time-variant seasonal autoregressive (TVSAR) model and extended to further capture time-variant and complex nature of various respiratory patterns. The extended TVSAR can represent not only the conventional quasi-periodical nature, but also the residual components, which cannot be expressed by the quasi-periodical model. Then, the residuals are adaptively predicted by using another autoregressive model. The proposed method was tested on 105 clinical data sets of tumor motion. The average errors were 1.28 ± 0.87 mm and 1.75 ± 1.13 mm for 0.5 s and 1.0 s ahead prediction, respectively. The results demonstrate that the proposed method can outperform the state-of-the-art prediction methods. PMID:23367303

  20. Predicting Time Series from Short-Term High-Dimensional Data

    NASA Astrophysics Data System (ADS)

    Ma, Huanfei; Zhou, Tianshou; Aihara, Kazuyuki; Chen, Luonan

    The prediction of future values of time series is a challenging task in many fields. In particular, making prediction based on short-term data is believed to be difficult. Here, we propose a method to predict systems' low-dimensional dynamics from high-dimensional but short-term data. Intuitively, it can be considered as a transformation from the inter-variable information of the observed high-dimensional data into the corresponding low-dimensional but long-term data, thereby equivalent to prediction of time series data. Technically, this method can be viewed as an inverse implementation of delayed embedding reconstruction. Both methods and algorithms are developed. To demonstrate the effectiveness of the theoretical result, benchmark examples and real-world problems from various fields are studied.

  1. Family History Predicts Stress Fracture in Active Female Adolescents

    PubMed Central

    Loud, Keith J.; Micheli, Lyle J.; Bristol, Stephanie; Austin, S. Bryn; Gordon, Catherine M.

    2011-01-01

    OBJECTIVE Increased physical activity and menstrual irregularity have been associated with increased risk for stress fracture among adult women active in athletics. The purposes of this study were to determine whether menstrual irregularity is also a risk factor for stress fracture in active female adolescents and to estimate the quantity of exercise associated with an increased risk for this injury. PATIENTS AND METHODS A case-control study was conducted of 13- to 22-year-old females diagnosed with their first stress fracture, each matched prospectively on age and self-reported ethnicity with 2 controls. Patients with chronic illnesses or use of medications known to affect bone mineral density were excluded, including use of hormonal preparations that could alter menstrual cycles. The primary outcome, stress fracture in any extremity or the spine, was confirmed radiographically. Girls with stress fracture had bone mineral density measured at the lumbar spine by dual-energy x-ray absorptiometry. RESULTS The mean ± SD age of the 168 participants was 15.9 ± 2.1 years; 91.7% were postmenarchal, with a mean age at menarche of 13.1 ± 1.1 years. The prevalence of menstrual irregularity was similar among cases and controls. There was no significant difference in the mean hours per week of total physical activity between girls in this sample with stress fracture (8.2 hours/week) and those without (7.4 hours/week). In multivariate models, case subjects had nearly 3 times the odds of having a family member with osteoporosis or osteopenia. In secondary analyses, participants with stress fracture had a low mean spinal bone mineral density for their age. CONCLUSIONS Among highly active female adolescents, only family history was independently associated with stress fracture. The magnitude of this association suggests that further investigations of inheritable skeletal factors are warranted in this population, along with evaluation of bone mineral density in girls with stress

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

    PubMed

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

    2015-12-01

    It is widely acknowledged that music can communicate and induce a wide range of emotions in the listener. However, music is a highly-complex audio signal composed of a wide range of complex time- and frequency-varying components. Additionally, music-induced emotions are known to differ greatly between listeners. Therefore, it is not immediately clear what emotions will be induced in a given individual by a piece of music. We attempt to predict the music-induced emotional response in a listener by measuring the activity in the listeners electroencephalogram (EEG). We combine these measures with acoustic descriptors of the music, an approach that allows us to consider music as a complex set of time-varying acoustic features, independently of any specific music theory. Regression models are found which allow us to predict the music-induced emotions of our participants with a correlation between the actual and predicted responses of up to r=0.234,p<0.001. This regression fit suggests that over 20% of the variance of the participant's music induced emotions can be predicted by their neural activity and the properties of the music. Given the large amount of noise, non-stationarity, and non-linearity in both EEG and music, this is an encouraging result. Additionally, the combination of measures of brain activity and acoustic features describing the music played to our participants allows us to predict music-induced emotions with significantly higher accuracies than either feature type alone (p<0.01). PMID:26544602

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

    SciTech Connect

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

    2009-03-01

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

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

    PubMed

    Yu, Zirui; Peldszus, Sigrid; Huck, Peter M

    2009-03-01

    The adsorption of two representative pharmaceutically active compounds (PhACs)-naproxen and carbamazepine and one endocrine disrupting compound (EDC)-nonylphenol was studied in pilot-scale granular activated carbon (GAC) adsorbers using post-sedimentation (PS) water from a full-scale drinking water treatment plant. Acidic naproxen broke through fastest while nonylphenol was removed best, which was consistent with the degree to which fouling affected compound removals. Model predictions and experimental data were generally in good agreement for all three compounds, which demonstrated the effectiveness and robustness of the pore and surface diffusion model (PSDM) used in combination with the time-variable parameter approach for predicting removals at environmentally relevant concentrations (i.e., ng/L range). Sensitivity analyses suggested that accurate determination of film diffusion coefficients was critical for predicting breakthrough for naproxen and carbamazepine, in particular when high removals are targeted. Model simulations demonstrated that GAC carbon usage rates (CURs) for naproxen were substantially influenced by the empty bed contact time (EBCT) at the investigated conditions. Model-based comparisons between GAC CURs and minimum CURs for powdered activated carbon (PAC) applications suggested that PAC would be most appropriate for achieving 90% removal of naproxen, whereas GAC would be more suitable for nonylphenol. PMID:19350922

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

    NASA Astrophysics Data System (ADS)

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

    2002-02-01

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

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

    PubMed

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

    2015-02-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

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

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

    PubMed

    Ak, Ronay; Fink, Olga; Zio, Enrico

    2016-08-01

    The increasing liberalization of European electricity markets, the growing proportion of intermittent renewable energy being fed into the energy grids, and also new challenges in the patterns of energy consumption (such as electric mobility) require flexible and intelligent power grids capable of providing efficient, reliable, economical, and sustainable energy production and distribution. From the supplier side, particularly, the integration of renewable energy sources (e.g., wind and solar) into the grid imposes an engineering and economic challenge because of the limited ability to control and dispatch these energy sources due to their intermittent characteristics. Time-series prediction of wind speed for wind power production is a particularly important and challenging task, wherein prediction intervals (PIs) are preferable results of the prediction, rather than point estimates, because they provide information on the confidence in the prediction. In this paper, two different machine learning approaches to assess PIs of time-series predictions are considered and compared: 1) multilayer perceptron neural networks trained with a multiobjective genetic algorithm and 2) extreme learning machines combined with the nearest neighbors approach. The proposed approaches are applied for short-term wind speed prediction from a real data set of hourly wind speed measurements for the region of Regina in Saskatchewan, Canada. Both approaches demonstrate good prediction precision and provide complementary advantages with respect to different evaluation criteria. PMID:25910257

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

    PubMed

    Ak, Ronay; Fink, Olga; Zio, Enrico

    2016-08-01

    The increasing liberalization of European electricity markets, the growing proportion of intermittent renewable energy being fed into the energy grids, and also new challenges in the patterns of energy consumption (such as electric mobility) require flexible and intelligent power grids capable of providing efficient, reliable, economical, and sustainable energy production and distribution. From the supplier side, particularly, the integration of renewable energy sources (e.g., wind and solar) into the grid imposes an engineering and economic challenge because of the limited ability to control and dispatch these energy sources due to their intermittent characteristics. Time-series prediction of wind speed for wind power production is a particularly important and challenging task, wherein prediction intervals (PIs) are preferable results of the prediction, rather than point estimates, because they provide information on the confidence in the prediction. In this paper, two different machine learning approaches to assess PIs of time-series predictions are considered and compared: 1) multilayer perceptron neural networks trained with a multiobjective genetic algorithm and 2) extreme learning machines combined with the nearest neighbors approach. The proposed approaches are applied for short-term wind speed prediction from a real data set of hourly wind speed measurements for the region of Regina in Saskatchewan, Canada. Both approaches demonstrate good prediction precision and provide complementary advantages with respect to different evaluation criteria.

  10. Reward prediction error signals associated with a modified time estimation task.

    PubMed

    Holroyd, Clay B; Krigolson, Olave E

    2007-11-01

    The feedback error-related negativity (fERN) is a component of the human event-related brain potential (ERP) elicited by feedback stimuli. A recent theory holds that the fERN indexes a reward prediction error signal associated with the adaptive modification of behavior. Here we present behavioral and ERP data recorded from participants engaged in a modified time estimation task. As predicted by the theory, our results indicate that fERN amplitude reflects a reward prediction error signal and that the size of this error signal is correlated across participants with changes in task performance.

  11. Extremely Randomized Machine Learning Methods for Compound Activity Prediction.

    PubMed

    Czarnecki, Wojciech M; Podlewska, Sabina; Bojarski, Andrzej J

    2015-11-09

    Speed, a relatively low requirement for computational resources and high effectiveness of the evaluation of the bioactivity of compounds have caused a rapid growth of interest in the application of machine learning methods to virtual screening tasks. However, due to the growth of the amount of data also in cheminformatics and related fields, the aim of research has shifted not only towards the development of algorithms of high predictive power but also towards the simplification of previously existing methods to obtain results more quickly. In the study, we tested two approaches belonging to the group of so-called 'extremely randomized methods'-Extreme Entropy Machine and Extremely Randomized Trees-for their ability to properly identify compounds that have activity towards particular protein targets. These methods were compared with their 'non-extreme' competitors, i.e., Support Vector Machine and Random Forest. The extreme approaches were not only found out to improve the efficiency of the classification of bioactive compounds, but they were also proved to be less computationally complex, requiring fewer steps to perform an optimization procedure.

  12. Predicting Activation of Experiments Inside the Annular Core Research Reactor

    SciTech Connect

    Greenberg, Joseph Isaac

    2015-11-01

    The objective of this thesis is to create a program to quickly estimate the radioactivity and decay of experiments conducted inside of the Annular Core Research Reactor at Sandia National Laboratories and eliminate the need for users to write code. This is achieved by model the neutron fluxes in the reactor’s central cavity where experiments are conducted for 4 different neutron spectra using MCNP. The desired neutron spectrum, experiment material composition, and reactor power level are then input into CINDER2008 burnup code to obtain activation and decay information for every isotope generated. DREAD creates all of the files required for CINDER2008 through user selected inputs in a graphical user interface and executes the program for the user and displays the resulting estimation for dose rate at various distances. The DREAD program was validated by weighing and measuring various experiments in the different spectra and then collecting dose rate information after they were irradiated and comparing it to the dose rates that DREAD predicted. The program provides results with an average of 17% higher estimates than the actual values and takes seconds to execute.

  13. Traction force dynamics predict gap formation in activated endothelium.

    PubMed

    Valent, Erik T; van Nieuw Amerongen, Geerten P; van Hinsbergh, Victor W M; Hordijk, Peter L

    2016-09-10

    In many pathological conditions the endothelium becomes activated and dysfunctional, resulting in hyperpermeability and plasma leakage. No specific therapies are available yet to control endothelial barrier function, which is regulated by inter-endothelial junctions and the generation of acto-myosin-based contractile forces in the context of cell-cell and cell-matrix interactions. However, the spatiotemporal distribution and stimulus-induced reorganization of these integral forces remain largely unknown. Traction force microscopy of human endothelial monolayers was used to visualize contractile forces in resting cells and during thrombin-induced hyperpermeability. Simultaneously, information about endothelial monolayer integrity, adherens junctions and cytoskeletal proteins (F-actin) were captured. This revealed a heterogeneous distribution of traction forces, with nuclear areas showing lower and cell-cell junctions higher traction forces than the whole-monolayer average. Moreover, junctional forces were asymmetrically distributed among neighboring cells. Force vector orientation analysis showed a good correlation with the alignment of F-actin and revealed contractile forces in newly formed filopodia and lamellipodia-like protrusions within the monolayer. Finally, unstable areas, showing high force fluctuations within the monolayer were prone to form inter-endothelial gaps upon stimulation with thrombin. To conclude, contractile traction forces are heterogeneously distributed within endothelial monolayers and force instability, rather than force magnitude, predicts the stimulus-induced formation of intercellular gaps. PMID:27498166

  14. Predicting Water Activity for Complex Wastes with Solvation Cluster Equilibria (SCE) - 12042

    SciTech Connect

    Agnew, S.F.; Reynolds, J.G.; Johnston, C.T.

    2012-07-01

    Predicting an electrolyte mixture's water activity, i.e. the ratio of water vapor pressure over a solution with that of pure water, in principle reveals both boiling point and solubilities for that mixture. Better predictions of these properties helps support the ongoing missions to concentrate complex nuclear waste mixtures in order to conserve tank space and improved predictions of water activity will help. A new approach for predicting water activity, the solvation cluster equilibria (SCE) model, uses pure electrolyte water activities to predict water activity for a complex mixture of those electrolytes. An SCE function based on electrolyte hydration free energy and a standard Debye- Hueckel (DH) charge compression fits each pure electrolyte's water activity with three parameters. Given these pure electrolyte water activities, the SCE predicts any mixture water activity over a large range of concentration with an additional parameter for each mixture vector, the multinarity. In contrast to ionic strength, which scales with concentration, multinarity is related to the relative proportion of electrolytes in a mixture and can either increase or decrease the water activity prediction over a broad range of concentration for that mixture. The SCE model predicts water activity for complex electrolyte mixtures based on the water activities of pure electrolytes. Three parameter SCE functions fit the water activities of pure electrolytes and along with a single multinarity parameter for each mixture vector then predict the mixture water activity. Predictions of water activity can in principle predict solution electrolyte activity and this relationship will be explored in the future. Predicting electrolyte activities for complex mixtures provides a means of determining solubilities for each electrolyte. Although there are a number of reports [9, 10, 11] of water activity models for pure and binary mixtures of electrolytes, none of them compare measured versus calculated

  15. Real-time prediction learning for the simultaneous actuation of multiple prosthetic joints.

    PubMed

    Pilarski, Patrick M; Dick, Travis B; Sutton, Richard S

    2013-06-01

    Integrating learned predictions into a prosthetic control system promises to enhance multi-joint prosthesis use by amputees. In this article, we present a preliminary study of different cases where it may be beneficial to use a set of temporally extended predictions--learned and maintained in real time--within an engineered or learned prosthesis controller. Our study demonstrates the first successful combination of actor-critic reinforcement learning with real-time prediction learning. We evaluate this new approach to control learning during the myoelectric operation of a robot limb. Our results suggest that the integration of real-time prediction and control learning may speed control policy acquisition, allow unsupervised adaptation in myoelectric controllers, and facilitate synergies in highly actuated limbs. These experiments also show that temporally extended prediction learning enables anticipatory actuation, opening the way for coordinated motion in assistive robotic devices. Our work therefore provides initial evidence that realtime prediction learning is a practical way to support intuitive joint control in increasingly complex prosthetic systems. PMID:24187253

  16. Predicting the timing and potential of the spring emergence of overwintered populations of Heliothis spp

    NASA Technical Reports Server (NTRS)

    Hartstack, A. W.; Witz, J. A.; Lopez, J. D. (Principal Investigator)

    1981-01-01

    The current state of knowledge dealing with the prediction of the overwintering population and spring emergence of Heliothis spp., a serious pest of numerous crops is surveyed. Current literature is reviewed in detail. Temperature and day length are the primary factors which program H. spp. larva for possible diapause. Although studies on the interaction of temperature and day length are reported, the complete diapause induction process is not identified sufficiently to allow accurate prediction of diapause timing. Mortality during diapause is reported as highly variable. The factors causing mortality are identified, but only a few are quantified. The spring emergence of overwintering H. spp. adults and mathematical models which predict the timing of emergence are reviewed. Timing predictions compare favorably to observed field data; however, prediction of actual numbers of emerging moths is not possible. The potential for use of spring emergence predictions in pest management applications, as an early warning of potential crop damage, are excellent. Research requirements to develop such an early warning system are discussed.

  17. Multivariable time series prediction for the icing process on overhead power transmission line.

    PubMed

    Li, Peng; Zhao, Na; Zhou, Donghua; Cao, Min; Li, Jingjie; Shi, Xinling

    2014-01-01

    The design of monitoring and predictive alarm systems is necessary for successful overhead power transmission line icing. Given the characteristics of complexity, nonlinearity, and fitfulness in the line icing process, a model based on a multivariable time series is presented here to predict the icing load of a transmission line. In this model, the time effects of micrometeorology parameters for the icing process have been analyzed. The phase-space reconstruction theory and machine learning method were then applied to establish the prediction model, which fully utilized the history of multivariable time series data in local monitoring systems to represent the mapping relationship between icing load and micrometeorology factors. Relevant to the characteristic of fitfulness in line icing, the simulations were carried out during the same icing process or different process to test the model's prediction precision and robustness. According to the simulation results for the Tao-Luo-Xiong Transmission Line, this model demonstrates a good accuracy of prediction in different process, if the prediction length is less than two hours, and would be helpful for power grid departments when deciding to take action in advance to address potential icing disasters. PMID:25136653

  18. Multivariable Time Series Prediction for the Icing Process on Overhead Power Transmission Line

    PubMed Central

    Li, Peng; Zhao, Na; Zhou, Donghua; Cao, Min; Li, Jingjie; Shi, Xinling

    2014-01-01

    The design of monitoring and predictive alarm systems is necessary for successful overhead power transmission line icing. Given the characteristics of complexity, nonlinearity, and fitfulness in the line icing process, a model based on a multivariable time series is presented here to predict the icing load of a transmission line. In this model, the time effects of micrometeorology parameters for the icing process have been analyzed. The phase-space reconstruction theory and machine learning method were then applied to establish the prediction model, which fully utilized the history of multivariable time series data in local monitoring systems to represent the mapping relationship between icing load and micrometeorology factors. Relevant to the characteristic of fitfulness in line icing, the simulations were carried out during the same icing process or different process to test the model's prediction precision and robustness. According to the simulation results for the Tao-Luo-Xiong Transmission Line, this model demonstrates a good accuracy of prediction in different process, if the prediction length is less than two hours, and would be helpful for power grid departments when deciding to take action in advance to address potential icing disasters. PMID:25136653

  19. Investigation of Average Prediction Time for Different Meteorological Variables By Using Chaotic Approach

    NASA Astrophysics Data System (ADS)

    Özgür, Evren; Koçak, Kasım

    2016-04-01

    According to nonlinear dynamical system approach, it is possible that the time evolution of a system can be represented by its trajectories in phase space. This phase space is spanned by the state variables which are necessary to determine the time evolution of the system. Atmospheric processes can not be represented by linear approaches because of their dependency on numerous independent variables. Since a small changes in initial conditions lead to significant differences in prediction, long term prediction of meteorological variables is not possible. This situation can be explained by the term "sensitive dependence on initial conditions". In the study, it was tried to determine the average prediction time for different atmospheric variables by applying nonlinear approach. In order to apply the method, the first step is to reconstruct the phase space. Phase space has two parameters which are time delay and embedding dimension. Mutual Information Function (MIF) can be used to determine optimum time delay. MIF considers both linear and nonlinear inner-dependencies in a given time series. To define phase space, embedding dimension must be identified correctly. Embedding dimesion is the number of necessary state variables which describe the dynamics of a system. The algorithm to define embedding dimension is False Nearest Neighbors (FNN). After constructing the phase space by using time delay and embedding dimension, the maximum Lyapunov exponent was introduced. Lyapunov exponent is related to the exponential divergence or convergence of nearby orbits in the phase space. A dynamical system which has positive Lyapunov exponent is defined as chaotic system. Because meteorological variables can be controlled with large numbers of independent variables, time series of meteorological variables might be produced by a chaotic dynamical system. By using phase space and maximum Lyapunov exponent value, average prediction times of different parameters were calculated

  20. Time to pay attention: attentional performance time-stamped prefrontal cholinergic activation, diurnality and performance

    PubMed Central

    Paolone, Giovanna; Lee, Theresa M.; Sarter, Martin

    2012-01-01

    Although the impairments in cognitive performance that result from shifting or disrupting daily rhythms have been demonstrated, the neuronal mechanisms that optimize fixed time daily performance are poorly understood. We previously demonstrated that daily practice of a sustained attention task (SAT) evokes a diurnal activity pattern in rats. Here we report that SAT practice at a fixed time produced practice time-stamped increases in prefrontal cholinergic neurotransmission that persisted after SAT practice was terminated and in a different environment. SAT time-stamped cholinergic activation occurred irrespective of whether the SAT was practiced during the light or dark phase or in constant light conditions. In contrast, prior daily practice of an operant schedule of reinforcement, albeit generating more rewards and lever presses per session than the SAT, neither activated the cholinergic system nor affected the animals' nocturnal activity pattern. Likewise, food-restricted animals exhibited strong food anticipatory activity (FAA) and attenuated activity during the dark period but FAA was not associated with increases in prefrontal cholinergic activity. Removal of cholinergic neurons impaired SAT performance and facilitated the reemergence of nocturnality. Shifting SAT practice away from a fixed time resulted in significantly lower performance. In conclusion, these experiments demonstrated that fixed time, daily practice of a task assessing attention generates a precisely practice time-stamped activation of the cortical cholinergic input system. Time-stamped cholinergic activation benefits fixed time performance and, if practiced during the light phase, contributes to a diurnal activity pattern. PMID:22933795

  1. Prediction intervals for a noisy nonlinear time series based on a bootstrapping reservoir computing network ensemble.

    PubMed

    Sheng, Chunyang; Zhao, Jun; Wang, Wei; Leung, Henry

    2013-07-01

    Prediction intervals that provide estimated values as well as the corresponding reliability are applied to nonlinear time series forecast. However, constructing reliable prediction intervals for noisy time series is still a challenge. In this paper, a bootstrapping reservoir computing network ensemble (BRCNE) is proposed and a simultaneous training method based on Bayesian linear regression is developed. In addition, the structural parameters of the BRCNE, that is, the number of reservoir computing networks and the reservoir dimension, are determined off-line by the 0.632 bootstrap cross-validation. To verify the effectiveness of the proposed method, two kinds of time series data, including the multisuperimposed oscillator problem with additive noises and a practical gas flow in steel industry are employed here. The experimental results indicate that the proposed approach has a satisfactory performance on prediction intervals for practical applications.

  2. Models to Predict Flowering Time in the Main Saffron Production Regions of Khorasan Province

    NASA Astrophysics Data System (ADS)

    Behdani, M. A.; Koocheki, A.; Nassiri, M.; Rezvani, P.

    The objective of this study was to develop a thermal model that can be used for prediction of saffron flowering time. For this purpose, existing data on saffron flower emergence time were collected in a wide range of temperature regimes over the saffron production regions of Khorasan province, Iran. Linear second-order polynomial and 5-parameter beta models were used and statistically compared for their ability in predicting saffron flowering time as a function of temperature. The results showed a significant delay in flowering date across the temperature gradient. While beta model had a better statistical performance but the simple linear model also showed a good predicting ability and therefore, can be used as a reliable model.

  3. Prediction of activated carbon adsorption capacities for organic vapors using quantitative structure-activity relationship methods

    SciTech Connect

    Nirmalakhandan, N.N. ); Speece, R.E. )

    1993-08-01

    Quantitative structure-activity relationship (QSAR) methods were used to develop models to estimate and predict activated carbon adsorption capacities for organic vapors. Literature isothermal data from two sources for 22 organic contaminants on six different carbons were merged to form a training set of 75 data points. Two different QSAR approaches were evaluated: the molecular connectivity approach and the linear solvation energy relationship approach. The QSAR model developed in this study using the molecular connectivity approach was able to fit the experimental data with r = 0.96 and standard error of 0.09. The utility of the model was demonstrated by using predicted k values to calculate adsorption capacities of 12 chemicals on two different carbons and comparing them with experimentally determined values. 9 refs., 1 fig., 3 tabs.

  4. Objectively Quantified Physical Activity and Sedentary Behavior in Predicting Visceral Adiposity and Liver Fat

    PubMed Central

    Pavey, Toby G.; Caterson, Ian D.; George, Jacob

    2016-01-01

    Objective. Epidemiologic studies suggest an inverse relationship between nonalcoholic fatty liver disease (NAFLD), visceral adipose tissue (VAT), and self-reported physical activity levels. However, subjective measurements can be inaccurate and prone to reporter bias. We investigated whether objectively quantified physical activity levels predicted liver fat and VAT in overweight/obese adults. Methods. Habitual physical activity was measured by triaxial accelerometry for four days (n = 82). Time spent in sedentary behavior (MET < 1.6) and light (MET 1.6 < 3), moderate (MET 3 < 6), and vigorous (MET 6 < 9) physical activity was quantified. Magnetic resonance imaging and spectroscopy were used to quantify visceral and liver fat. Bivariate correlations and hierarchical multiple regression analyses were performed. Results. There were no associations between physical activity or sedentary behavior and liver lipid. Sedentary behavior and moderate and vigorous physical activity accounted for just 3% of variance for VAT (p = 0.14) and 0.003% for liver fat (p = 0.96). Higher levels of VAT were associated with time spent in moderate activity (r = 0.294, p = 0.007), but there was no association with sedentary behavior. Known risk factors for obesity-related NAFLD accounted for 62% and 40% of variance in VAT and liver fat, respectively (p < 0.01). Conclusion. Objectively measured levels of habitual physical activity and sedentary behavior did not influence VAT or liver fat. PMID:27777796

  5. Iterative prediction of chaotic time series using a recurrent neural network

    SciTech Connect

    Essawy, M.A.; Bodruzzaman, M.; Shamsi, A.; Noel, S.

    1996-12-31

    Chaotic systems are known for their unpredictability due to their sensitive dependence on initial conditions. When only time series measurements from such systems are available, neural network based models are preferred due to their simplicity, availability, and robustness. However, the type of neutral network used should be capable of modeling the highly non-linear behavior and the multi-attractor nature of such systems. In this paper the authors use a special type of recurrent neural network called the ``Dynamic System Imitator (DSI)``, that has been proven to be capable of modeling very complex dynamic behaviors. The DSI is a fully recurrent neural network that is specially designed to model a wide variety of dynamic systems. The prediction method presented in this paper is based upon predicting one step ahead in the time series, and using that predicted value to iteratively predict the following steps. This method was applied to chaotic time series generated from the logistic, Henon, and the cubic equations, in addition to experimental pressure drop time series measured from a Fluidized Bed Reactor (FBR), which is known to exhibit chaotic behavior. The time behavior and state space attractor of the actual and network synthetic chaotic time series were analyzed and compared. The correlation dimension and the Kolmogorov entropy for both the original and network synthetic data were computed. They were found to resemble each other, confirming the success of the DSI based chaotic system modeling.

  6. Time-predictable model applicability for earthquake occurrence in northeast India and vicinity

    NASA Astrophysics Data System (ADS)

    Panthi, A.; Shanker, D.; Singh, H. N.; Kumar, A.; Paudyal, H.

    2011-03-01

    Northeast India and its vicinity is one of the seismically most active regions in the world, where a few large and several moderate earthquakes have occurred in the past. In this study the region of northeast India has been considered for an earthquake generation model using earthquake data as reported by earthquake catalogues National Geophysical Data Centre, National Earthquake Information Centre, United States Geological Survey and from book prepared by Gupta et al. (1986) for the period 1906-2008. The events having a surface wave magnitude of Ms≥5.5 were considered for statistical analysis. In this region, nineteen seismogenic sources were identified by the observation of clustering of earthquakes. It is observed that the time interval between the two consecutive mainshocks depends upon the preceding mainshock magnitude (Mp) and not on the following mainshock (Mf). This result corroborates the validity of time-predictable model in northeast India and its adjoining regions. A linear relation between the logarithm of repeat time (T) of two consecutive events and the magnitude of the preceding mainshock is established in the form LogT = cMp+a, where "c" is a positive slope of line and "a" is function of minimum magnitude of the earthquake considered. The values of the parameters "c" and "a" are estimated to be 0.21 and 0.35 in northeast India and its adjoining regions. The less value of c than the average implies that the earthquake occurrence in this region is different from those of plate boundaries. The result derived can be used for long term seismic hazard estimation in the delineated seismogenic regions.

  7. Statistical Observations and Predictions of Time Changes in Electron Flux at Geosynchronous Orbit

    NASA Astrophysics Data System (ADS)

    Olson, D. K.; Larsen, B.; Friedel, R. H.; Geoffrey, R.

    2015-12-01

    A statistical survey of time changes in particle flux values (df/dt) at geosynchronous orbit reveals trends that are instructive to predictive magnetosphere models. A single spacecraft can provide short time scale df/dt measurements, while multiple spacecraft can provide values over periods comparable to the spacecraft separation. Using data from multiple LANL-GEO spacecraft provides a unique view of temporal and spatial variations that allow us to gauge time and length scales for changing particle fluxes at GEO. These scales provide a base ability to predict the plasma environment conditions for spacecraft crossing GEO. Probability distribution functions based on electron df/dt values are used to predict the electron flux at a given magnetic local time at GEO based on prior measurements. The predictions, when compared to new data taken in the same region, provide some measure of how the electron plasma environment at GEO has changed in the interim period. These predictions are compared to data from the Van Allen Probes as their orbits cross GEO to verify the validity of this technique.

  8. A model for prediction of resynchronization after time-zone flights.

    PubMed

    Wegmann, H M; Klein, K E; Conrad, B; Esser, P

    1983-06-01

    Utilizing experimental data from three flight studies, a concept was developed which allows appraising average resynchronization for any day after arrival in a new time-zone. The course of adaptation is nonlinear and can be mathematically represented by an exponential function. The model predicts higher initial resynchronization rates when more time zones are crossed, but total time for complete reentrainment is essentially the same and, thus is independent of the number of time-zones. The equation derived from experimental data is converted into an e-function and the resulting time constants are presented as they evolved for different functions and flight directions.

  9. Real-time prediction of unsteady aerodynamics: Application for aircraft control and manoeuvrability enhancement.

    PubMed

    Faller, W E; Schreck, S J

    1995-01-01

    The capability to control unsteady separated flow fields could dramatically enhance aircraft agility. To enable control, however, real-time prediction of these flow fields over a broad parameter range must be realized. The present work describes real-time predictions of three-dimensional unsteady separated flow fields and aerodynamic coefficients using neural networks. Unsteady surface-pressure readings were obtained from an airfoil pitched at a constant rate through the static stall angle. All data sets were comprised of 15 simultaneously acquired pressure records and one pitch angle record. Five such records and the associated pitch angle histories were used to train the neural network using a time-series algorithm. Post-training, the input to the network was the pitch angle (alpha), the angular velocity (dalpha/dt), and the initial 15 recorded surface pressures at time (t (0)). Subsequently, the time (t+Deltat) network predictions, for each of the surface pressures, were fed back as the input to the network throughout the pitch history. The results indicated that the neural network accurately predicted the unsteady separated flow fields as well as the aerodynamic coefficients to within 5% of the experimental data. Consistent results were obtained both for the training set as well as for generalization to both other constant pitch rates and to sinusoidal pitch motions. The results clearly indicated that the neural-network model could predict the unsteady surface-pressure distributions and aerodynamic coefficients based solely on angle of attack information. The capability for real-time prediction of both unsteady separated flow fields and aerodynamic coefficients across a wide range of parameters in turn provides a critical step towards the development of control systems targeted at exploiting unsteady aerodynamics for aircraft manoeuvrability enhancement.

  10. Time-dependent damage in predictions of fatigue behaviour of normal and healing ligaments

    NASA Astrophysics Data System (ADS)

    Thornton, Gail M.; Bailey, Soraya J.; Schwab, Timothy D.

    2015-08-01

    Ligaments are dense fibrous tissues that connect bones across a joint and are exposed daily to creep and fatigue loading. Ligaments are tensile load-bearing tissues; therefore, fatigue loading will have a component of time-dependent damage from the non-zero mean stress and cycle-dependent damage from the oscillating stress. If time-dependent damage is not sufficient to completely predict the fatigue response, then cycle-dependent damage could be an important contributor. Using data from normal ligaments (current study and Thornton et al., Clin. Biomech. 22:932-940, 2007a) and healing ligaments (Thornton and Bailey, J. Biomech. Eng. 135:091004-1-091004-6, 2013), creep data was used to predict the fatigue response considering time-dependent damage. Relationships between creep lifetime and test stress or initial strain were modelled using exponential or power-law regression. In order to predict fatigue lifetimes, constant rates of damage were assumed and time-varying stresses were introduced into the expressions for time-dependent damage from creep. Then, the predictions of fatigue lifetime were compared with curvefits to the fatigue data where exponential or power-law regressions were used to determine the relationship between fatigue lifetime and test stress or initial strain. The fatigue prediction based on time-dependent damage alone greatly overestimated fatigue lifetime suggesting that time-dependent damage alone cannot account for all of the damage accumulated during fatigue and that cycle-dependent damage has an important role. At lower stress and strain, time-dependent damage was a greater relative contributor for normal ligaments than healing ligaments; however, cycle-dependent damage was a greater relative contributor with incremental increases in stress or strain for normal ligaments than healing ligaments.

  11. Spots and Flares: Stellar Activity in the Time Domain Era

    NASA Astrophysics Data System (ADS)

    Davenport, James

    2015-08-01

    Time domain photometric surveys for large numbers of stars have ushered in a new era of statistical studies of astrophysics. This new parameter space allows us to observe how stars behave and change on a human timescale, and facilitates ensemble studies to understand how stars change over cosmic timescales. With current and planned time domain stellar surveys, we will be able to put the Sun in a Galactic context, and discover how typical or unique our parent star truly is. The goal of this thesis is to develop techniques for detecting and analyzing the most prominent forms of magnetic activity from low-mass stars in modern time domain surveys: starspots and flares. Magnetic field strength is a fundamental property that decays over a star's life. As a result, flux modulations from both flares and starspots become smaller amplitude and more infrequent in light curves. Methods for detecting these forms of magnetic activity will be extensible to future time domain surveys, and helpful in characterizing the properties of stars as they age. Flares can be detected in sparsely sampled wide field surveys by searching for bright single-point outliers in light curves. Using both red optical and near infrared data from ground-based surveys over many years, I have constrained the rate of flares in multiple wavelengths for an ensemble of M dwarfs. Studying flares in these existing ground-based datasets will enable predictions for future survey yields. Space-based photometry enables continuous and precise monitoring of stars for many years, which is crucial for obtaining a complete census of flares from a single star. Using 11 months of 1-minute photometry for the M dwarf GJ 1243, I have amassed over 6100 flare events, the largest sample of white light flares for any low-mass star. I have also created the first high fidelity empirical white light flare template, which shows three distinct phases in typical flare light curves. With this template, I demonstrate that complex multi

  12. Spots and Flares: Stellar Activity in the Time Domain Era

    NASA Astrophysics Data System (ADS)

    Davenport, James R. A.

    Time domain photometric surveys for large numbers of stars have ushered in a new era of statistical studies of astrophysics. This new parameter space allows us to observe how stars behave and change on a human timescale, and facilitates ensemble studies to understand how stars change over cosmic timescales. With current and planned time domain stellar surveys, we will be able to put the Sun in a Galactic context, and discover how typical or unique our parent star truly is. The goal of this thesis is to develop techniques for detecting and analyzing the most prominent forms of magnetic activity from low-mass stars in modern time domain surveys: starspots and flares. Magnetic field strength is a fundamental property that decays over a star's life. As a result, flux modulations from both flares and starspots become smaller amplitude and more infrequent in light curves. Methods for detecting these forms of magnetic activity will be extensible to future time domain surveys, and helpful in characterizing the properties of stars as they age. Flares can be detected in sparsely sampled wide field surveys by searching for bright single-point outliers in light curves. Using both red optical and near infrared data from ground-based surveys over many years, I have constrained the rate of flares in multiple wavelengths for an ensemble of M dwarfs. Studying flares in these existing ground-based datasets will enable predictions for future survey yields. Space-based photometry enables continuous and precise monitoring of stars for many years, which is crucial for obtaining a complete census of flares from a single star. Using 11 months of 1-minute photometry for the M dwarf GJ 1243, I have amassed over 6100 flare events, the largest sample of white light flares for any low-mass star. I have also created the first high fidelity empirical white light flare template, which shows three distinct phases in typical flare light curves. With this template, I demonstrate that complex multi

  13. Prediction of the applicability of active damping elements in high-precision machines

    NASA Astrophysics Data System (ADS)

    Holterman, Jan; de Vries, Theo J. A.

    2004-07-01

    The Smart Disc project at the Drebbel Institute of the University of Twente is aimed at the development of active structural elements for high-precision machines. The active elements consist of a piezoelectric position actuator and a collocated piezoelectric force sensor. As the actuators and sensors are collocated, the elements are especially suited for implementing robust active damping. The decision whether or not to incorporate active damping elements in a high-precision machine should ideally be made in an early design stage, i.e., at a time at which only limited knowledge of the vibration problem is available. Despite the uncertainties that may exist at that stage, one would like to be able to roughly predict the amount of damping that could possibly be obtained. For that reason, the present paper is concerned with the development of an analysis tool that may help in predicting the applicability of active damping elements in a mechanical structure of which only a rough model is available. Based on extensive simulations, several practical rules of thumb are given for the requirements for the mechanical structure and the active elements, in order to enable the realisation of relative damping values as high as 10%.

  14. Personal best times in an Olympic distance triathlon and in a marathon predict Ironman race time in recreational male triathletes

    PubMed Central

    Rüst, Christoph Alexander; Knechtle, Beat; Knechtle, Patrizia; Rosemann, Thomas; Lepers, Romuald

    2011-01-01

    Background The purpose of this study was to define predictor variables for recreational male Ironman triathletes, using age and basic measurements of anthropometry, training, and previous performance to establish an equation for the prediction of an Ironman race time for future recreational male Ironman triathletes. Methods Age and anthropometry, training, and previous experience variables were related to Ironman race time using bivariate and multivariate analysis. Results A total of 184 recreational male triathletes, of mean age 40.9 ± 8.4 years, height 1.80 ± 0.06 m, and weight 76.3 ± 8.4 kg completed the Ironman within 691 ± 83 minutes. They spent 13.9 ± 5.0 hours per week in training, covering 6.3 ± 3.1 km of swimming, 194.4 ± 76.6 km of cycling, and 45.0 ± 15.9 km of running. In total, 149 triathletes had completed at least one marathon, and 150 athletes had finished at least one Olympic distance triathlon. They had a personal best time of 130.4 ± 44.2 minutes in an Olympic distance triathlon and of 193.9 ± 31.9 minutes in marathon running. In total, 126 finishers had completed both an Olympic distance triathlon and a marathon. After multivariate analysis, both a personal best time in a marathon (P < 0.0001) and in an Olympic distance triathlon (P < 0.0001) were the best variables related to Ironman race time. Ironman race time (minutes) might be partially predicted by the following equation: (r2 = 0.65, standard error of estimate = 56.8) = 152.1 + 1.332 × (personal best time in a marathon, minutes) + 1.964 × (personal best time in an Olympic distance triathlon, minutes). Conclusion These results suggest that, in contrast with anthropometric and training characteristics, both the personal best time in an Olympic distance triathlon and in a marathon predict Ironman race time in recreational male Ironman triathletes. PMID:24198578

  15. MO-G-18C-05: Real-Time Prediction in Free-Breathing Perfusion MRI

    SciTech Connect

    Song, H; Liu, W; Ruan, D; Jung, S; Gach, M

    2014-06-15

    Purpose: The aim is to minimize frame-wise difference errors caused by respiratory motion and eliminate the need for breath-holds in magnetic resonance imaging (MRI) sequences with long acquisitions and repeat times (TRs). The technique is being applied to perfusion MRI using arterial spin labeling (ASL). Methods: Respiratory motion prediction (RMP) using navigator echoes was implemented in ASL. A least-square method was used to extract the respiratory motion information from the 1D navigator. A generalized artificial neutral network (ANN) with three layers was developed to simultaneously predict 10 time points forward in time and correct for respiratory motion during MRI acquisition. During the training phase, the parameters of the ANN were optimized to minimize the aggregated prediction error based on acquired navigator data. During realtime prediction, the trained ANN was applied to the most recent estimated displacement trajectory to determine in real-time the amount of spatial Results: The respiratory motion information extracted from the least-square method can accurately represent the navigator profiles, with a normalized chi-square value of 0.037±0.015 across the training phase. During the 60-second training phase, the ANN successfully learned the respiratory motion pattern from the navigator training data. During real-time prediction, the ANN received displacement estimates and predicted the motion in the continuum of a 1.0 s prediction window. The ANN prediction was able to provide corrections for different respiratory states (i.e., inhalation/exhalation) during real-time scanning with a mean absolute error of < 1.8 mm. Conclusion: A new technique enabling free-breathing acquisition during MRI is being developed. A generalized ANN development has demonstrated its efficacy in predicting a continuum of motion profile for volumetric imaging based on navigator inputs. Future work will enhance the robustness of ANN and verify its effectiveness with human

  16. Predicting brain activation patterns associated with individual lexical concepts based on five sensory-motor attributes

    PubMed Central

    Fernandino, Leonardo; Humphries, Colin J.; Seidenberg, Mark S.; Gross, William L.; Conant, Lisa L.; Binder, Jeffrey R.

    2015-01-01

    While major advances have been made in uncovering the neural processes underlying perceptual representations, our grasp of how the brain gives rise to conceptual knowledge remains relatively poor. Recent work has provided strong evidence that concepts rely, at least in part, on the same sensory and motor neural systems through which they were acquired, but it is still unclear whether the neural code for concept representation uses information about sensory-motor features to discriminate between concepts. In the present study, we investigate this question by asking whether an encoding model based on five semantic attributes directly related to sensory-motor experience – sound, color, visual motion, shape, and manipulation – can successfully predict patterns of brain activation elicited by individual lexical concepts. We collected ratings on the relevance of these five attributes to the meaning of 820 words, and used these ratings as predictors in a multiple regression model of the fMRI signal associated with the words in a separate group of participants. The five resulting activation maps were then combined by linear summation to predict the distributed activation pattern elicited by a novel set of 80 test words. The encoding model predicted the activation patterns elicited by the test words significantly better than chance. As expected, prediction was successful for concrete but not for abstract concepts. Comparisons between encoding models based on different combinations of attributes indicate that all five attributes contribute to the representation of concrete concepts. Consistent with embodied theories of semantics, these results show, for the first time, that the distributed activation pattern associated with a concept combines information about different sensory-motor attributes according to their respective relevance. Future research should investigate how additional features of phenomenal experience contribute to the neural representation of conceptual

  17. Predicting brain activation patterns associated with individual lexical concepts based on five sensory-motor attributes.

    PubMed

    Fernandino, Leonardo; Humphries, Colin J; Seidenberg, Mark S; Gross, William L; Conant, Lisa L; Binder, Jeffrey R

    2015-09-01

    While major advances have been made in uncovering the neural processes underlying perceptual representations, our grasp of how the brain gives rise to conceptual knowledge remains relatively poor. Recent work has provided strong evidence that concepts rely, at least in part, on the same sensory and motor neural systems through which they were acquired, but it is still unclear whether the neural code for concept representation uses information about sensory-motor features to discriminate between concepts. In the present study, we investigate this question by asking whether an encoding model based on five semantic attributes directly related to sensory-motor experience - sound, color, visual motion, shape, and manipulation - can successfully predict patterns of brain activation elicited by individual lexical concepts. We collected ratings on the relevance of these five attributes to the meaning of 820 words, and used these ratings as predictors in a multiple regression model of the fMRI signal associated with the words in a separate group of participants. The five resulting activation maps were then combined by linear summation to predict the distributed activation pattern elicited by a novel set of 80 test words. The encoding model predicted the activation patterns elicited by the test words significantly better than chance. As expected, prediction was successful for concrete but not for abstract concepts. Comparisons between encoding models based on different combinations of attributes indicate that all five attributes contribute to the representation of concrete concepts. Consistent with embodied theories of semantics, these results show, for the first time, that the distributed activation pattern associated with a concept combines information about different sensory-motor attributes according to their respective relevance. Future research should investigate how additional features of phenomenal experience contribute to the neural representation of conceptual

  18. Predicting brain activation patterns associated with individual lexical concepts based on five sensory-motor attributes.

    PubMed

    Fernandino, Leonardo; Humphries, Colin J; Seidenberg, Mark S; Gross, William L; Conant, Lisa L; Binder, Jeffrey R

    2015-09-01

    While major advances have been made in uncovering the neural processes underlying perceptual representations, our grasp of how the brain gives rise to conceptual knowledge remains relatively poor. Recent work has provided strong evidence that concepts rely, at least in part, on the same sensory and motor neural systems through which they were acquired, but it is still unclear whether the neural code for concept representation uses information about sensory-motor features to discriminate between concepts. In the present study, we investigate this question by asking whether an encoding model based on five semantic attributes directly related to sensory-motor experience - sound, color, visual motion, shape, and manipulation - can successfully predict patterns of brain activation elicited by individual lexical concepts. We collected ratings on the relevance of these five attributes to the meaning of 820 words, and used these ratings as predictors in a multiple regression model of the fMRI signal associated with the words in a separate group of participants. The five resulting activation maps were then combined by linear summation to predict the distributed activation pattern elicited by a novel set of 80 test words. The encoding model predicted the activation patterns elicited by the test words significantly better than chance. As expected, prediction was successful for concrete but not for abstract concepts. Comparisons between encoding models based on different combinations of attributes indicate that all five attributes contribute to the representation of concrete concepts. Consistent with embodied theories of semantics, these results show, for the first time, that the distributed activation pattern associated with a concept combines information about different sensory-motor attributes according to their respective relevance. Future research should investigate how additional features of phenomenal experience contribute to the neural representation of conceptual

  19. STRUCTURE-ACTIVITY RELATIONSHIP STUIDES AND THEIR ROLE IN PREDICTING AND INVESTIGATING CHEMICAL TOXICITY

    EPA Science Inventory

    Structure-Activity Relationship Studies and their Role in Predicting and Investigating Chemical Toxicity

    Structure-activity relationships (SAR) represent attempts to generalize chemical information relative to biological activity for the twin purposes of generating insigh...

  20. Detecting determinism with improved sensitivity in time series: Rank-based nonlinear predictability score

    NASA Astrophysics Data System (ADS)

    Naro, Daniel; Rummel, Christian; Schindler, Kaspar; Andrzejak, Ralph G.

    2014-09-01

    The rank-based nonlinear predictability score was recently introduced as a test for determinism in point processes. We here adapt this measure to time series sampled from time-continuous flows. We use noisy Lorenz signals to compare this approach against a classical amplitude-based nonlinear prediction error. Both measures show an almost identical robustness against Gaussian white noise. In contrast, when the amplitude distribution of the noise has a narrower central peak and heavier tails than the normal distribution, the rank-based nonlinear predictability score outperforms the amplitude-based nonlinear prediction error. For this type of noise, the nonlinear predictability score has a higher sensitivity for deterministic structure in noisy signals. It also yields a higher statistical power in a surrogate test of the null hypothesis of linear stochastic correlated signals. We show the high relevance of this improved performance in an application to electroencephalographic (EEG) recordings from epilepsy patients. Here the nonlinear predictability score again appears of higher sensitivity to nonrandomness. Importantly, it yields an improved contrast between signals recorded from brain areas where the first ictal EEG signal changes were detected (focal EEG signals) versus signals recorded from brain areas that were not involved at seizure onset (nonfocal EEG signals).

  1. Validation of Quantitative Structure-Activity Relationship (QSAR) Model for Photosensitizer Activity Prediction

    PubMed Central

    Frimayanti, Neni; Yam, Mun Li; Lee, Hong Boon; Othman, Rozana; Zain, Sharifuddin M.; Rahman, Noorsaadah Abd.

    2011-01-01

    Photodynamic therapy is a relatively new treatment method for cancer which utilizes a combination of oxygen, a photosensitizer and light to generate reactive singlet oxygen that eradicates tumors via direct cell-killing, vasculature damage and engagement of the immune system. Most of photosensitizers that are in clinical and pre-clinical assessments, or those that are already approved for clinical use, are mainly based on cyclic tetrapyrroles. In an attempt to discover new effective photosensitizers, we report the use of the quantitative structure-activity relationship (QSAR) method to develop a model that could correlate the structural features of cyclic tetrapyrrole-based compounds with their photodynamic therapy (PDT) activity. In this study, a set of 36 porphyrin derivatives was used in the model development where 24 of these compounds were in the training set and the remaining 12 compounds were in the test set. The development of the QSAR model involved the use of the multiple linear regression analysis (MLRA) method. Based on the method, r2 value, r2 (CV) value and r2 prediction value of 0.87, 0.71 and 0.70 were obtained. The QSAR model was also employed to predict the experimental compounds in an external test set. This external test set comprises 20 porphyrin-based compounds with experimental IC50 values ranging from 0.39 μM to 7.04 μM. Thus the model showed good correlative and predictive ability, with a predictive correlation coefficient (r2 prediction for external test set) of 0.52. The developed QSAR model was used to discover some compounds as new lead photosensitizers from this external test set. PMID:22272096

  2. Reliable activation to novel stimuli predicts higher fluid intelligence.

    PubMed

    Euler, Matthew J; Weisend, Michael P; Jung, Rex E; Thoma, Robert J; Yeo, Ronald A

    2015-07-01

    The ability to reliably respond to stimuli could be an important biological determinant of differences in fluid intelligence (Gf). However, most electrophysiological studies of Gf employ event-related potential (ERP) measures that average brain activity over trials, and hence have limited power to quantify neural variability. Time-frequency analyses can capture cross-trial variation in the phase of neural activity, and thus can help address the importance of neural reliability to differences in Gf. This study recruited a community sample of healthy adults and measured inter-trial phase clustering (ITPC), total spectral power, and ERP amplitudes elicited by Repeated and Novel non-target stimuli during two visual oddball tasks. Condition effects, relations among the EEG measures, and relations with Gf were assessed. Early visual responses to Repeated stimuli elicited higher ITPC, yet only ITPC elicited by Novel stimuli was associated with Gf. Analyses of spectral power further highlighted the contribution of phase consistency to the findings. The link between Gf and reliable responding to changing inputs suggests an important role for flexible resource allocation in fluid intellectual skills.

  3. A method of predictions geomagnetic activity based on a coronal model of relations between solar and geomagnetic activities

    NASA Technical Reports Server (NTRS)

    Halenka, J.

    1979-01-01

    A method developed to predict both disturbed and quiet geomagnetic periods is described. The method uses solar situations along the CM with the key role of filaments, giving indirect evidence of types of directly unobservable coronal structures above them. The time lag, not to be interpreted in terms of propagation speed, between the CM activity and the commencement of the geomagnetic response is about one to two days. Solar phenomena serve as indicators within approximately 10 deg of the CM and up to the zone of high latitude filaments.

  4. Young Urban African American Adolescents' Experience of Discretionary Time Activities

    ERIC Educational Resources Information Center

    Bohnert, Amy M.; Richards, Maryse H.; Kolmodin, Karen E.; Lakin, Brittany L.

    2008-01-01

    This cross-sectional study examined the daily discretionary time experiences of 246 (107 boys, 139 girls) fifth through eighth grade urban African American adolescents using the Experience Sampling Method. Relations between the types of activities (i.e., active structured, active unstructured, passive unstructured) engaged in during discretionary…

  5. On determining the prediction limits of mathematical models for time series

    NASA Astrophysics Data System (ADS)

    Peluso, E.; Murari, A.; Gelfusa, M.; Lungaroni, M.; Talebzadeh, S.; Gaudio, P.; contributors, JET

    2016-07-01

    Prediction is one of the main objectives of scientific analysis and it refers to both modelling and forecasting. The determination of the limits of predictability is an important issue of both theoretical and practical relevance. In the case of modelling time series, reached a certain level in performance in either modelling or prediction, it is often important to assess whether all the information available in the data has been exploited or whether there are still margins for improvement of the tools being developed. In this paper, an information theoretic approach is proposed to address this issue and quantify the quality of the models and/or predictions. The excellent properties of the proposed indicator have been proved with the help of a systematic series of numerical tests and a concrete example of extreme relevance for nuclear fusion.

  6. Strategic influence on the time course of predictive inferences in reading.

    PubMed

    Calvo, Manuel G; Castillo, M Dolores; Schmalhofer, Franz

    2006-01-01

    In the present study, we investigated how reading strategies affect the time course of online predictive inferences. Participants read sentences under instructions either to anticipate the outcomes of described events or to understand the sentences. These were followed by a target word to be named, with stimulus onset asynchronies (SOAs) of 500 or 1,000 msec (50- or 550-msec interstimulus interval, respectively). Sentences either were predictive of events or were lexically matched control sentences. There was facilitation in naming latencies for predictable target words in the strategic-anticipation condition at both SOAs, but not in the read-to-understand condition, with a significant improvement in the former condition in comparison with the latter. This suggests that predictive inferences, which are typically considered to be resource demanding, can be speeded up by specific goals in reading. Moreover, this can occur at no cost to comprehension of explicit information, as was revealed by a comprehension test.

  7. The short-term prediction of universal time and length of day using atmospheric angular momentum

    NASA Technical Reports Server (NTRS)

    Freedman, A. P.; Steppe, J. A.; Dickey, J. O.; Eubanks, T. M.; Sung, L.-Y.

    1994-01-01

    The ability to predict short-term variations in the Earth's rotation has gained importance in recent years owing to more precise spacecraft tracking requirements. Universal time (UT1), that component of the Earth's orientation corresponding to the rotation angle, can be measured by number of high-precision space geodetic techniques. A Kalman filter developed at the Jet Propulsion Laboratory (JPL) optimally combines these different data sets and generates a smoothed times series and a set of predictions for UT1, as well as for additional Earth orientation components. These UT1 predictions utilize an empirically derived random walk stochastic model for the length of the day (LOD) and require frequent and up-to-date measurements of either UT1 or LOD to keep errors from quickly accumulating. Recent studies have shown that LOD variations are correlated with changes in the Earth's axial atmospheric angular momentum (AAM) over timescales of several years down to as little as 8 days. AAM estimates and forecasts out to 10 days are routinely available from meteorological analysis centers; these data can supplement geodetic measurements to improve the short-term prediction of LOD and have therefore been incorporated as independent data types in the JPL Kalman filter. We find that AAM and, to a lesser extent, AAM forecast data are extremely helpful in generating accurate near-real-time estimates of UT1 and LOD and in improving short-term predictions of these quantities out to about 10 days.

  8. Assembling genes from predicted exons in linear time with dynamic programming.

    PubMed

    Guigó, R

    1998-01-01

    In a number of programs for gene structure prediction in higher eukaryotic genomic sequences, exon prediction is decoupled from gene assembly: a large pool of candidate exons is predicted and scored from features located in the query DNA sequence, and candidate genes are assembled from such a pool as sequences of nonoverlapping frame-compatible exons. Genes are scored as a function of the scores of the assembled exons, and the highest scoring candidate gene is assumed to be the most likely gene encoded by the query DNA sequence. Considering additive gene scoring functions, currently available algorithms to determine such a highest scoring candidate gene run in time proportional to the square of the number of predicted exons. Here, we present an algorithm whose running time grows only linearly with the size of the set of predicted exons. Polynomial algorithms rely on the fact that, while scanning the set of predicted exons, the highest scoring gene ending in a given exon can be obtained by appending the exon to the highest scoring among the highest scoring genes ending at each compatible preceding exon. The algorithm here relies on the simple fact that such highest scoring gene can be stored and updated. This requires scanning the set of predicted exons simultaneously by increasing acceptor and donor position. On the other hand, the algorithm described here does not assume an underlying gene structure model. Indeed, the definition of valid gene structures is externally defined in the so-called Gene Model. The Gene Model specifies simply which gene features are allowed immediately upstream which other gene features in valid gene structures. This allows for great flexibility in formulating the gene identification problem. In particular it allows for multiple-gene two-strand predictions and for considering gene features other than coding exons (such as promoter elements) in valid gene structures.

  9. Ecological prediction with nonlinear multivariate time-frequency functional data models

    USGS Publications Warehouse

    Yang, Wen-Hsi; Wikle, Christopher K.; Holan, Scott H.; Wildhaber, Mark L.

    2013-01-01

    Time-frequency analysis has become a fundamental component of many scientific inquiries. Due to improvements in technology, the amount of high-frequency signals that are collected for ecological and other scientific processes is increasing at a dramatic rate. In order to facilitate the use of these data in ecological prediction, we introduce a class of nonlinear multivariate time-frequency functional models that can identify important features of each signal as well as the interaction of signals corresponding to the response variable of interest. Our methodology is of independent interest and utilizes stochastic search variable selection to improve model selection and performs model averaging to enhance prediction. We illustrate the effectiveness of our approach through simulation and by application to predicting spawning success of shovelnose sturgeon in the Lower Missouri River.

  10. Warning Times and Impact Predictions of Asteroids and Comets on a Collision Course with Earth

    NASA Astrophysics Data System (ADS)

    Heidt, Ahren D.

    1997-12-01

    This study investigates the amount of data and time necessary to accurately predict Earth impacts of Earth Crossing Objects (ECOs). Trajectories are simulated by numerically integrating in an N-Body system. Given final impact parameters, the trajectory is propagated backwards to an earlier time, creating initial conditions and simulated observation data at requested intervals to which Gaussian random noise is introduced. Utilizing a Bayes Filter to estimate position and velocity from the simulated observation data, the estimate is then propagated forward in time to determine whether or not an impact can be accurately predicted. State vectors and covariance matrices are then propagated to the impact time and the one sigma error ellipsoid is analyzed.

  11. Multiscale time activity data exploration via temporal clustering visualization spreadsheet.

    PubMed

    Woodring, Jonathan; Shen, Han-Wei

    2009-01-01

    Time-varying data is usually explored by animation or arrays of static images. Neither is particularly effective for classifying data by different temporal activities. Important temporal trends can be missed due to the lack of ability to find them with current visualization methods. In this paper, we propose a method to explore data at different temporal resolutions to discover and highlight data based upon time-varying trends. Using the wavelet transform along the time axis, we transform data points into multi-scale time series curve sets. The time curves are clustered so that data of similar activity are grouped together, at different temporal resolutions. The data are displayed to the user in a global time view spreadsheet where she is able to select temporal clusters of data points, and filter and brush data across temporal scales. With our method, a user can interact with data based on time activities and create expressive visualizations. PMID:19008560

  12. Time Perspective and Physical Activity among Central Appalachian Adolescents

    ERIC Educational Resources Information Center

    Gulley, Tauna

    2013-01-01

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

  13. Quantitative structure-activity relationship (QSAR) for insecticides: development of predictive in vivo insecticide activity models.

    PubMed

    Naik, P K; Singh, T; Singh, H

    2009-07-01

    Quantitative structure-activity relationship (QSAR) analyses were performed independently on data sets belonging to two groups of insecticides, namely the organophosphates and carbamates. Several types of descriptors including topological, spatial, thermodynamic, information content, lead likeness and E-state indices were used to derive quantitative relationships between insecticide activities and structural properties of chemicals. A systematic search approach based on missing value, zero value, simple correlation and multi-collinearity tests as well as the use of a genetic algorithm allowed the optimal selection of the descriptors used to generate the models. The QSAR models developed for both organophosphate and carbamate groups revealed good predictability with r(2) values of 0.949 and 0.838 as well as [image omitted] values of 0.890 and 0.765, respectively. In addition, a linear correlation was observed between the predicted and experimental LD(50) values for the test set data with r(2) of 0.871 and 0.788 for both the organophosphate and carbamate groups, indicating that the prediction accuracy of the QSAR models was acceptable. The models were also tested successfully from external validation criteria. QSAR models developed in this study should help further design of novel potent insecticides.

  14. Physics-based enzyme design: predicting binding affinity and catalytic activity.

    PubMed

    Sirin, Sarah; Pearlman, David A; Sherman, Woody

    2014-12-01

    Computational enzyme design is an emerging field that has yielded promising success stories, but where numerous challenges remain. Accurate methods to rapidly evaluate possible enzyme design variants could provide significant value when combined with experimental efforts by reducing the number of variants needed to be synthesized and speeding the time to reach the desired endpoint of the design. To that end, extending our computational methods to model the fundamental physical-chemical principles that regulate activity in a protocol that is automated and accessible to a broad population of enzyme design researchers is essential. Here, we apply a physics-based implicit solvent MM-GBSA scoring approach to enzyme design and benchmark the computational predictions against experimentally determined activities. Specifically, we evaluate the ability of MM-GBSA to predict changes in affinity for a steroid binder protein, catalytic turnover for a Kemp eliminase, and catalytic activity for α-Gliadin peptidase variants. Using the enzyme design framework developed here, we accurately rank the most experimentally active enzyme variants, suggesting that this approach could provide enrichment of active variants in real-world enzyme design applications.

  15. Period, epoch, and prediction errors of ephemerides from continuous sets of timing measurements

    NASA Astrophysics Data System (ADS)

    Deeg, H. J.

    2015-06-01

    Space missions such as Kepler and CoRoT have led to large numbers of eclipse or transit measurements in nearly continuous time series. This paper shows how to obtain the period error in such measurements from a basic linear least-squares fit, and how to correctly derive the timing error in the prediction of future transit or eclipse events. Assuming strict periodicity, a formula for the period error of these time series is derived, σP = σT (12 / (N3-N))1 / 2, where σP is the period error, σT the timing error of a single measurement, and N the number of measurements. Compared to the iterative method for period error estimation by Mighell & Plavchan (2013), this much simpler formula leads to smaller period errors, whose correctness has been verified through simulations. For the prediction of times of future periodic events, usual linear ephemeris were epoch errors are quoted for the first time measurement, are prone to an overestimation of the error of that prediction. This may be avoided by a correction for the duration of the time series. An alternative is the derivation of ephemerides whose reference epoch and epoch error are given for the centre of the time series. For long continuous or near-continuous time series whose acquisition is completed, such central epochs should be the preferred way for the quotation of linear ephemerides. While this work was motivated from the analysis of eclipse timing measures in space-based light curves, it should be applicable to any other problem with an uninterrupted sequence of discrete timings for which the determination of a zero point, of a constant period and of the associated errors is needed.

  16. Prediction problem for target events based on the inter-event waiting time

    NASA Astrophysics Data System (ADS)

    Shapoval, A.

    2010-11-01

    In this paper we address the problem of forecasting the target events of a time series given the distribution ξ of time gaps between target events. Strong earthquakes and stock market crashes are the two types of such events that we are focusing on. In the series of earthquakes, as McCann et al. show [W.R. Mc Cann, S.P. Nishenko, L.R. Sykes, J. Krause, Seismic gaps and plate tectonics: seismic potential for major boundaries, Pure and Applied Geophysics 117 (1979) 1082-1147], there are well-defined gaps (called seismic gaps) between strong earthquakes. On the other hand, usually there are no regular gaps in the series of stock market crashes [M. Raberto, E. Scalas, F. Mainardi, Waiting-times and returns in high-frequency financial data: an empirical study, Physica A 314 (2002) 749-755]. For the case of seismic gaps, we analytically derive an upper bound of prediction efficiency given the coefficient of variation of the distribution ξ. For the case of stock market crashes, we develop an algorithm that predicts the next crash within a certain time interval after the previous one. We show that this algorithm outperforms random prediction. The efficiency of our algorithm sets up a lower bound of efficiency for effective prediction of stock market crashes.

  17. Development of VIS/NIR spectroscopic system for real-time prediction of fresh pork quality

    NASA Astrophysics Data System (ADS)

    Zhang, Haiyun; Peng, Yankun; Zhao, Songwei; Sasao, Akira

    2013-05-01

    Quality attributes of fresh meat will influence nutritional value and consumers' purchasing power. The aim of the research was to develop a prototype for real-time detection of quality in meat. It consisted of hardware system and software system. A VIS/NIR spectrograph in the range of 350 to 1100 nm was used to collect the spectral data. In order to acquire more potential information of the sample, optical fiber multiplexer was used. A conveyable and cylindrical device was designed and fabricated to hold optical fibers from multiplexer. High power halogen tungsten lamp was collected as the light source. The spectral data were obtained with the exposure time of 2.17ms from the surface of the sample by press down the trigger switch on the self-developed system. The system could automatically acquire, process, display and save the data. Moreover the quality could be predicted on-line. A total of 55 fresh pork samples were used to develop prediction model for real time detection. The spectral data were pretreated with standard normalized variant (SNV) and partial least squares regression (PLSR) was used to develop prediction model. The correlation coefficient and root mean square error of the validation set for water content and pH were 0.810, 0.653, and 0.803, 0.098 respectively. The research shows that the real-time non-destructive detection system based on VIS/NIR spectroscopy can be efficient to predict the quality of fresh meat.

  18. Probabilistic prediction of real-world time series: A local regression approach

    NASA Astrophysics Data System (ADS)

    Laio, Francesco; Ridolfi, Luca; Tamea, Stefania

    2007-02-01

    We propose a probabilistic prediction method, based on local polynomial regressions, which complements the point forecasts with robust estimates of the corresponding forecast uncertainty. The reliability, practicability and generality of the method is demonstrated by applying it to astronomical, physiological, economic, and geophysical time series.

  19. Compensation for the distortion in satellite laser range predictions due to varying pulse travel times

    NASA Technical Reports Server (NTRS)

    Paunonen, Matti

    1993-01-01

    A method for compensating for the effect of the varying travel time of a transmitted laser pulse to a satellite is described. The 'observed minus predicted' range differences then appear to be linear, which makes data screening or use in range gating more effective.

  20. Predicting High Risk Adolescents' Substance Use over Time: The Role of Parental Monitoring

    ERIC Educational Resources Information Center

    Clark, Heddy Kovach; Shamblen, Stephen R.; Ringwalt, Chris L.; Hanley, Sean

    2012-01-01

    We examined whether parental monitoring at baseline predicted subsequent substance use in a high-risk youth population. Students in 14 alternative high schools in Washington State completed self-report surveys at three time points over the course of 2 years. Primary analyses included 1,423 students aged 14-20 who lived with at least one parent or…

  1. Temporal Characteristics of the Predictive Synchronous Firing Modeled by Spike-Timing-Dependent Plasticity

    ERIC Educational Resources Information Center

    Kitano, Katsunori; Fukai, Tomoki

    2004-01-01

    When a sensory cue was repeatedly followed by a behavioral event with fixed delays, pairs of premotor and primary motor neurons showed significant increases of coincident spikes at times a monkey was expecting the event. These results provided evidence that neuronal firing synchrony has predictive power. To elucidate the underlying mechanism, here…

  2. Predicting the Risk of Attrition for Undergraduate Students with Time Based Modelling

    ERIC Educational Resources Information Center

    Chai, Kevin E. K.; Gibson, David

    2015-01-01

    Improving student retention is an important and challenging problem for universities. This paper reports on the development of a student attrition model for predicting which first year students are most at-risk of leaving at various points in time during their first semester of study. The objective of developing such a model is to assist…

  3. Prediction of energy requirements and drying times for surface drying fresh produce

    SciTech Connect

    Miller, W.M.

    1985-01-01

    For numerous fresh fruits and vegetables, drying of surface adhering water is required to facilitate materials handling and wax treatments. Using humidity ratio difference and air flow rates as manipulated variables, a computer program and a graphical approach were developed to predict required drying time. Modeling results were extended to investigate air recycling and the relationship of recycling on energy requirements.

  4. American Time Use Survey: Sleep Time and Its Relationship to Waking Activities

    PubMed Central

    Basner, Mathias; Fomberstein, Kenneth M.; Razavi, Farid M.; Banks, Siobhan; William, Jeffrey H.; Rosa, Roger R.; Dinges, David F.

    2007-01-01

    Study Objectives: To gain some insight into how various behavioral (lifestyle) factors influence sleep duration, by investigation of the relationship of sleep time to waking activities using the American Time Use Survey (ATUS). Design: Cross-sectional data from ATUS, an annual telephone survey of a population sample of US citizens who are interviewed regarding how they spent their time during a 24-hour period between 04:00 on the previous day and 04:00 on the interview day. Participants: Data were pooled from the 2003, 2004, and 2005 ATUS databases involving N=47,731 respondents older than 14 years of age. Interventions: N/A Results: Adjusted multiple linear regression models showed that the largest reciprocal relationship to sleep was found for work time, followed by travel time, which included commute time. Only shorter than average sleepers (<7.5 h) spent more time socializing, relaxing, and engaging in leisure activities, while both short (<5.5 h) and long sleepers (≥8.5 h) watched more TV than the average sleeper. The extent to which sleep time was exchanged for waking activities was also shown to depend on age and gender. Sleep time was minimal while work time was maximal in the age group 45–54 yr, and sleep time increased both with lower and higher age. Conclusions: Work time, travel time, and time for socializing, relaxing, and leisure are the primary activities reciprocally related to sleep time among Americans. These activities may be confounding the frequently observed association between short and long sleep on one hand and morbidity and mortality on the other hand and should be controlled for in future studies. Citation: Basner M; Fomberstein KM; Razavi FM; Banks S; William JH; Rosa RR; Dinges DF. American time use survey: sleep time and its relationship to waking activities. SLEEP 2007;30(9):1085-1095. PMID:17910380

  5. Time-Perception Network and Default Mode Network Are Associated with Temporal Prediction in a Periodic Motion Task.

    PubMed

    Carvalho, Fabiana M; Chaim, Khallil T; Sanchez, Tiago A; de Araujo, Draulio B

    2016-01-01

    The updating of prospective internal models is necessary to accurately predict future observations. Uncertainty-driven internal model updating has been studied using a variety of perceptual paradigms, and have revealed engagement of frontal and parietal areas. In a distinct literature, studies on temporal expectations have also characterized a time-perception network, which relies on temporal orienting of attention. However, the updating of prospective internal models is highly dependent on temporal attention, since temporal attention must be reoriented according to the current environmental demands. In this study, we used functional magnetic resonance imaging (fMRI) to evaluate to what extend the continuous manipulation of temporal prediction would recruit update-related areas and the time-perception network areas. We developed an exogenous temporal task that combines rhythm cueing and time-to-contact principles to generate implicit temporal expectation. Two patterns of motion were created: periodic (simple harmonic oscillation) and non-periodic (harmonic oscillation with variable acceleration). We found that non-periodic motion engaged the exogenous temporal orienting network, which includes the ventral premotor and inferior parietal cortices, and the cerebellum, as well as the presupplementary motor area, which has previously been implicated in internal model updating, and the motion-sensitive area MT+. Interestingly, we found a right-hemisphere preponderance suggesting the engagement of explicit timing mechanisms. We also show that the periodic motion condition, when compared to the non-periodic motion, activated a particular subset of the default-mode network (DMN) midline areas, including the left dorsomedial prefrontal cortex (DMPFC), anterior cingulate cortex (ACC), and bilateral posterior cingulate cortex/precuneus (PCC/PC). It suggests that the DMN plays a role in processing contextually expected information and supports recent evidence that the DMN may

  6. Time-Perception Network and Default Mode Network Are Associated with Temporal Prediction in a Periodic Motion Task.

    PubMed

    Carvalho, Fabiana M; Chaim, Khallil T; Sanchez, Tiago A; de Araujo, Draulio B

    2016-01-01

    The updating of prospective internal models is necessary to accurately predict future observations. Uncertainty-driven internal model updating has been studied using a variety of perceptual paradigms, and have revealed engagement of frontal and parietal areas. In a distinct literature, studies on temporal expectations have also characterized a time-perception network, which relies on temporal orienting of attention. However, the updating of prospective internal models is highly dependent on temporal attention, since temporal attention must be reoriented according to the current environmental demands. In this study, we used functional magnetic resonance imaging (fMRI) to evaluate to what extend the continuous manipulation of temporal prediction would recruit update-related areas and the time-perception network areas. We developed an exogenous temporal task that combines rhythm cueing and time-to-contact principles to generate implicit temporal expectation. Two patterns of motion were created: periodic (simple harmonic oscillation) and non-periodic (harmonic oscillation with variable acceleration). We found that non-periodic motion engaged the exogenous temporal orienting network, which includes the ventral premotor and inferior parietal cortices, and the cerebellum, as well as the presupplementary motor area, which has previously been implicated in internal model updating, and the motion-sensitive area MT+. Interestingly, we found a right-hemisphere preponderance suggesting the engagement of explicit timing mechanisms. We also show that the periodic motion condition, when compared to the non-periodic motion, activated a particular subset of the default-mode network (DMN) midline areas, including the left dorsomedial prefrontal cortex (DMPFC), anterior cingulate cortex (ACC), and bilateral posterior cingulate cortex/precuneus (PCC/PC). It suggests that the DMN plays a role in processing contextually expected information and supports recent evidence that the DMN may

  7. Time-Perception Network and Default Mode Network Are Associated with Temporal Prediction in a Periodic Motion Task

    PubMed Central

    Carvalho, Fabiana M.; Chaim, Khallil T.; Sanchez, Tiago A.; de Araujo, Draulio B.

    2016-01-01

    The updating of prospective internal models is necessary to accurately predict future observations. Uncertainty-driven internal model updating has been studied using a variety of perceptual paradigms, and have revealed engagement of frontal and parietal areas. In a distinct literature, studies on temporal expectations have also characterized a time-perception network, which relies on temporal orienting of attention. However, the updating of prospective internal models is highly dependent on temporal attention, since temporal attention must be reoriented according to the current environmental demands. In this study, we used functional magnetic resonance imaging (fMRI) to evaluate to what extend the continuous manipulation of temporal prediction would recruit update-related areas and the time-perception network areas. We developed an exogenous temporal task that combines rhythm cueing and time-to-contact principles to generate implicit temporal expectation. Two patterns of motion were created: periodic (simple harmonic oscillation) and non-periodic (harmonic oscillation with variable acceleration). We found that non-periodic motion engaged the exogenous temporal orienting network, which includes the ventral premotor and inferior parietal cortices, and the cerebellum, as well as the presupplementary motor area, which has previously been implicated in internal model updating, and the motion-sensitive area MT+. Interestingly, we found a right-hemisphere preponderance suggesting the engagement of explicit timing mechanisms. We also show that the periodic motion condition, when compared to the non-periodic motion, activated a particular subset of the default-mode network (DMN) midline areas, including the left dorsomedial prefrontal cortex (DMPFC), anterior cingulate cortex (ACC), and bilateral posterior cingulate cortex/precuneus (PCC/PC). It suggests that the DMN plays a role in processing contextually expected information and supports recent evidence that the DMN may

  8. Predicting anti-androgenic activity of bisphenols using molecular docking and quantitative structure-activity relationships.

    PubMed

    Yang, Xianhai; Liu, Huihui; Yang, Qian; Liu, Jining; Chen, Jingwen; Shi, Lili

    2016-11-01

    Both in vivo and in vitro assay indicated that bisphenols can inhibit the androgen receptor. However, the underlying antagonistic mechanism is unclear. In this study, molecular docking was employed to probe the interaction mechanism between bisphenols and human androgen receptor (hAR). The binding pattern of ligands in hAR crystal structures was also analyzed. Results show that hydrogen bonding and hydrophobic interactions are the dominant interactions between the ligands and hAR. The critical amino acid residues involved in forming hydrogen bonding between bisphenols and hAR is Asn 705 and Gln 711. Furthermore, appropriate molecular structural descriptors were selected to characterize the non-bonded interactions. Stepwise multiple linear regressions (MLR) analysis was employed to develop quantitative structure-activity relationship (QSAR) models for predicting the anti-androgenic activity of bisphenols. Based on the QSAR development and validation guideline issued by OECD, the goodness-of-fit, robustness and predictive ability of constructed QSAR model were assessed. The model application domain was characterized by the Euclidean distance and Williams plot. The mechanisms of the constructed model were also interpreted based on the selected molecular descriptors i.e. the number of hydroxyl groups (nROH), the most positive values of the molecular surface potential (Vs,max) and the lowest unoccupied molecular orbital energy (ELUMO). Finally, based on the model developed, the data gap for other twenty-six bisphenols on their anti-androgenic activity was filled. The predicted results indicated that the anti-androgenic activity of seven bisphenols was higher than that of bisphenol A. PMID:27561732

  9. Predicting Marathon Time Using Exhaustive Graded Exercise Test in Marathon Runners.

    PubMed

    Till, Eloise S; Armstrong, Stuart A; Harris, Greg; Maloney, Stephen

    2016-02-01

    The study aimed to investigate the correlation between time on a treadmill test and exhaustion 2 weeks before a road marathon and the subsequent road marathon performance time (MPT). The study recruited 59 runners entered in the Melbourne 2012 marathon, Canberra 2013 marathon, and Gold Coast 2013 marathon. Forty runners completed both the graded exercise treadmill test to exhaustion and the 42.2 km marathon. Nineteen participants dropped out of the study due to illness, injury, or did not begin the treadmill test. A statistically significant correlation was found between treadmill time and MPT (adjusted R(2) = 0.447). Sex, weekly running duration (t = -1.58, p = 0.12), years of running (t = 1.10, p = 0.28), and age (t = 0.94, p = 0.36) did not statistically correlate with MPT. The relationship between the graded exercise test and MPT can be used to predict MPT using y = -3.85x + 351.57, where y is MPT and x is treadmill time. This is a simple, accessible, and cost-effective method to aid athletes in predicting their race time over 42.2 km. Prediction of marathon time in a simple and accessible manner was believed to be useful to the growing population of marathon runners around the world.

  10. Accuracy of the Timed Up and Go test for predicting sarcopenia in elderly hospitalized patients

    PubMed Central

    Martinez, Bruno Prata; Gomes, Isabela Barboza; de Oliveira, Carolina Santana; Ramos, Isis Resende; Rocha, Mônica Diniz Marques; Júnior, Luiz Alberto Forgiarini; Camelier, Fernanda Warken Rosa; Camelier, Aquiles Assunção

    2015-01-01

    OBJECTIVES: The ability of the Timed Up and Go test to predict sarcopenia has not been evaluated previously. The objective of this study was to evaluate the accuracy of the Timed Up and Go test for predicting sarcopenia in elderly hospitalized patients. METHODS: This cross-sectional study analyzed 68 elderly patients (≥60 years of age) in a private hospital in the city of Salvador-BA, Brazil, between the 1st and 5th day of hospitalization. The predictive variable was the Timed Up and Go test score, and the outcome of interest was the presence of sarcopenia (reduced muscle mass associated with a reduction in handgrip strength and/or weak physical performance in a 6-m gait-speed test). After the descriptive data analyses, the sensitivity, specificity and accuracy of a test using the predictive variable to predict the presence of sarcopenia were calculated. RESULTS: In total, 68 elderly individuals, with a mean age 70.4±7.7 years, were evaluated. The subjects had a Charlson Comorbidity Index score of 5.35±1.97. Most (64.7%) of the subjects had a clinical admission profile; the main reasons for hospitalization were cardiovascular disorders (22.1%), pneumonia (19.1%) and abdominal disorders (10.2%). The frequency of sarcopenia in the sample was 22.1%, and the mean length of time spent performing the Timed Up and Go test was 10.02±5.38 s. A time longer than or equal to a cutoff of 10.85 s on the Timed Up and Go test predicted sarcopenia with a sensitivity of 67% and a specificity of 88.7%. The accuracy of this cutoff for the Timed Up and Go test was good (0.80; IC=0.66-0.94; p=0.002). CONCLUSION: The Timed Up and Go test was shown to be a predictor of sarcopenia in elderly hospitalized patients. PMID:26039955

  11. A genetic-based neuro-fuzzy approach for prediction of solar activity

    NASA Astrophysics Data System (ADS)

    Attia, Abdel-Fattah A.; Abdel-Hamid, Rabab H.; Quassim, Maha

    2004-09-01

    This paper presents an application of the neuro-fuzzy modeling to analyze the time series of solar activity, as measured through the relative Wolf number. The neuro-fuzzy structure will be optimized based on the linear adapted genetic algorithm with controlling population size (LAGA-POP). First, the dimension of the time series characteristic attractor is obtained based on the smallest Regularity Criterion (RC) and the neuro-fuzzy modeling. Second, after describing the neuro-fuzzy structure and optimizing its parameters based on LAGA-POP, the performance of the present approach in forecasting yearly sunspot numbers is favorably compared to that of other published methods. Finally, the comparison predictions for the remaining part of the 22nd and the whole 23rd cycle of solar activity are presented.

  12. Cognitive domains that predict time to diagnosis in prodromal Huntington disease

    PubMed Central

    Harrington, Deborah L.; Smith, Megan M.; Zhang, Ying; Carlozzi, Noelle E.; Paulsen, Jane S.

    2013-01-01

    Background Prodromal Huntington disease (prHD) is associated with a myriad of cognitive changes, but the domains that best predict time to clinical diagnosis have not been studied. This is a notable gap because some domains may be more sensitive to cognitive decline, which would inform clinical trials. Objectives The present study sought to characterize cognitive domains underlying a large test battery and for the first time, evaluate their ability to predict to time to diagnosis. Methods Participants included gene-negative and gene-positive prHD participants who were enrolled in the PREDICT-HD study. The CAG/Age Product (CAP) score was the measure of an individual’s genetic signature. A factor analysis of 18 tests was performed to identify sets of measures or latent factors that elucidated core constructs of tests. Factor scores were then fit to a survival model to evaluate their ability to predict time to diagnosis. Results Six factors were identified: 1) speed/inhibition, 2) verbal working memory, 3) motor planning/speed, 4) attention-information integration, 5) sensory-perceptual processing, and 6) verbal learning/memory. Factor scores were sensitive to a worsening of cognitive functioning in prHD, typically more so than performances on individual tests comprising the factors. Only the motor planning/speed and sensory-perceptual processing factors predicted time to diagnosis, after controlling for CAP scores and motor symptoms. Conclusions The results suggest that motor planning/speed and sensory-perceptual processing are important markers of disease prognosis. The findings also have implications for using composite indices of cognition in preventive HD trials where they may be more sensitive than individual tests. PMID:22451099

  13. Real-Time Safety Monitoring and Prediction for the National Airspace System

    NASA Technical Reports Server (NTRS)

    Roychoudhury, Indranil

    2016-01-01

    As new operational paradigms and additional aircraft are being introduced into the National Airspace System (NAS), maintaining safety in such a rapidly growing environment becomes more challenging. It is therefore desirable to have both an overview of the current safety of the airspace at different levels of granularity, as well an understanding of how the state of the safety will evolve into the future given the anticipated flight plans, weather forecasts, predicted health of assets in the airspace, and so on. To this end, we have developed a Real-Time Safety Monitoring (RTSM) that first, estimates the state of the NAS using the dynamic models. Then, given the state estimate and a probability distribution of future inputs to the NAS, the framework predicts the evolution of the NAS, i.e., the future state, and analyzes these future states to predict the occurrence of unsafe events. The entire probability distribution of airspace safety metrics is computed, not just point estimates, without significant assumptions regarding the distribution type and or parameters. We demonstrate our overall approach by predicting the occurrence of some unsafe events and show how these predictions evolve in time as flight operations progress.

  14. Data-driven prediction of thresholded time series of rainfall and self-organized criticality models

    NASA Astrophysics Data System (ADS)

    Deluca, Anna; Moloney, Nicholas R.; Corral, Álvaro

    2015-05-01

    We study the occurrence of events, subject to threshold, in a representative self-organized criticality (SOC) sandpile model and in high-resolution rainfall data. The predictability in both systems is analyzed by means of a decision variable sensitive to event clustering, and the quality of the predictions is evaluated by the receiver operating characteristic (ROC) method. In the case of the SOC sandpile model, the scaling of quiet-time distributions with increasing threshold leads to increased predictability of extreme events. A scaling theory allows us to understand all the details of the prediction procedure and to extrapolate the shape of the ROC curves for the most extreme events. For rainfall data, the quiet-time distributions do not scale for high thresholds, which means that the corresponding ROC curves cannot be straightforwardly related to those for lower thresholds. In this way, ROC curves are useful for highlighting differences in predictability of extreme events between toy models and real-world phenomena.

  15. Reliability prediction of ontology-based service compositions using Petri net and time series models.

    PubMed

    Li, Jia; Xia, Yunni; Luo, Xin

    2014-01-01

    OWL-S, one of the most important Semantic Web service ontologies proposed to date, provides a core ontological framework and guidelines for describing the properties and capabilities of their web services in an unambiguous, computer interpretable form. Predicting the reliability of composite service processes specified in OWL-S allows service users to decide whether the process meets the quantitative quality requirement. In this study, we consider the runtime quality of services to be fluctuating and introduce a dynamic framework to predict the runtime reliability of services specified in OWL-S, employing the Non-Markovian stochastic Petri net (NMSPN) and the time series model. The framework includes the following steps: obtaining the historical response times series of individual service components; fitting these series with a autoregressive-moving-average-model (ARMA for short) and predicting the future firing rates of service components; mapping the OWL-S process into a NMSPN model; employing the predicted firing rates as the model input of NMSPN and calculating the normal completion probability as the reliability estimate. In the case study, a comparison between the static model and our approach based on experimental data is presented and it is shown that our approach achieves higher prediction accuracy. PMID:24688429

  16. Reliability prediction of ontology-based service compositions using Petri net and time series models.

    PubMed

    Li, Jia; Xia, Yunni; Luo, Xin

    2014-01-01

    OWL-S, one of the most important Semantic Web service ontologies proposed to date, provides a core ontological framework and guidelines for describing the properties and capabilities of their web services in an unambiguous, computer interpretable form. Predicting the reliability of composite service processes specified in OWL-S allows service users to decide whether the process meets the quantitative quality requirement. In this study, we consider the runtime quality of services to be fluctuating and introduce a dynamic framework to predict the runtime reliability of services specified in OWL-S, employing the Non-Markovian stochastic Petri net (NMSPN) and the time series model. The framework includes the following steps: obtaining the historical response times series of individual service components; fitting these series with a autoregressive-moving-average-model (ARMA for short) and predicting the future firing rates of service components; mapping the OWL-S process into a NMSPN model; employing the predicted firing rates as the model input of NMSPN and calculating the normal completion probability as the reliability estimate. In the case study, a comparison between the static model and our approach based on experimental data is presented and it is shown that our approach achieves higher prediction accuracy.

  17. Comparison of Prothrombin Time and Aspartate Aminotransferase in Predicting Hepatotoxicity After Acetaminophen Overdose.

    PubMed

    Levine, Michael; O'Connor, Ayrn D; Padilla-Jones, Angela; Gerkin, Richard D

    2016-03-01

    Despite decades of experience with acetaminophen (APAP) overdoses, it remains unclear whether elevated hepatic transaminases or coagulopathy develop first. Furthermore, comparison of the predictive value of these two variables in determining hepatic toxicity following APAP overdoses has been poorly elucidated. The primary objective of this study is to determine the test characteristics of the aspartate aminotransferase (AST) and the prothrombin time (PT) in patients with APAP toxicity. A retrospective chart review of APAP overdoses treated with IV N-acetylcysteine at a tertiary care referral center was performed. Of the 304 subjects included in the study, 246 with an initial AST less than 1000 were analyzed to determine predictors of hepatic injury, defined as an AST exceeding 1000 IU/L. The initial AST >50 was 79.5 % sensitive and 82.6 % specific for predicting hepatic injury. The corresponding negative and positive predictive values were 95.5 and 46.3 %, respectively. In contrast, an initial abnormal PT had a sensitivity of 82.1 % and a specificity of 63.6 %. The negative and positive predictive values for initial PT were 94.9 and 30.2 %, respectively. Although the two tests performed similarly for predicting a composite endpoint of death or liver transplant, neither was a useful predictor. Initial AST performed better than the initial PT for predicting hepatic injury in this series of patients with APAP overdose. PMID:26341088

  18. Application of Grey Model GM(1, 1) to Ultra Short-Term Predictions of Universal Time

    NASA Astrophysics Data System (ADS)

    Lei, Yu; Guo, Min; Zhao, Danning; Cai, Hongbing; Hu, Dandan

    2016-03-01

    A mathematical model known as one-order one-variable grey differential equation model GM(1, 1) has been herein employed successfully for the ultra short-term (<10days) predictions of universal time (UT1-UTC). The results of predictions are analyzed and compared with those obtained by other methods. It is shown that the accuracy of the predictions is comparable with that obtained by other prediction methods. The proposed method is able to yield an exact prediction even though only a few observations are provided. Hence it is very valuable in the case of a small size dataset since traditional methods, e.g., least-squares (LS) extrapolation, require longer data span to make a good forecast. In addition, these results can be obtained without making any assumption about an original dataset, and thus is of high reliability. Another advantage is that the developed method is easy to use. All these reveal a great potential of the GM(1, 1) model for UT1-UTC predictions.

  19. Nonlinear techniques for forecasting solar activity directly from its time series

    NASA Technical Reports Server (NTRS)

    Ashrafi, S.; Roszman, L.; Cooley, J.

    1993-01-01

    This paper presents numerical techniques for constructing nonlinear predictive models to forecast solar flux directly from its time series. This approach makes it possible to extract dynamical in variants of our system without reference to any underlying solar physics. We consider the dynamical evolution of solar activity in a reconstructed phase space that captures the attractor (strange), give a procedure for constructing a predictor of future solar activity, and discuss extraction of dynamical invariants such as Lyapunov exponents and attractor dimension.

  20. Nonlinear techniques for forecasting solar activity directly from its time series

    NASA Technical Reports Server (NTRS)

    Ashrafi, S.; Roszman, L.; Cooley, J.

    1992-01-01

    Numerical techniques for constructing nonlinear predictive models to forecast solar flux directly from its time series are presented. This approach makes it possible to extract dynamical invariants of our system without reference to any underlying solar physics. We consider the dynamical evolution of solar activity in a reconstructed phase space that captures the attractor (strange), given a procedure for constructing a predictor of future solar activity, and discuss extraction of dynamical invariants such as Lyapunov exponents and attractor dimension.

  1. In vitro anti-Mycobacterium avium activities of quinolones: predicted active structures and mechanistic considerations.

    PubMed

    Klopman, G; Li, J Y; Wang, S; Pearson, A J; Chang, K; Jacobs, M R; Bajaksouzian, S; Ellner, J J

    1994-08-01

    The relationship between the structures of quinolones and their anti-Mycobacterium avium activities has been previously derived by using the Multiple Computer-Automated Structure Evaluation program. A number of substructural constraints required to overcome the resistance of most of the strains have been identified. Nineteen new quinolones which qualify under these substructural requirements were identified by the program and subsequently tested. The results show that the substructural attributes identified by the program produced a successful a priori prediction of the anti-M. avium activities of the new quinolones. All 19 quinolones were found to be active, and 4 of them are as active or better than ciprofloxacin. With these new quinolones, the updated multiple computer-automated structure evaluation program structure-activity relationship analysis has helped to uncover additional information about the nature of the substituents at the C5 and C7 positions needed for optimal inhibitory activity. A possible explanation of drug resistance based on the observation of suicide inactivation of bacterial cytochrome P-450 by the cyclopropylamine moiety has also been proposed and is discussed in this report. Furthermore, we confirm the view that the amount of the uncharged form present in a neutral pH solution plays a crucial role in the drug's penetration ability.

  2. Eruption forerunners from multiparameter monitoring and application for eruptions time predictability (Piton de la Fournaise)

    NASA Astrophysics Data System (ADS)

    Schmid, A.; Grasso, J. R.; Clarke, D.; Ferrazzini, V.; BachèLery, P.; Staudacher, T.

    2012-11-01

    Volcanic eruptions impact on societal risk, and volcanic hazard assessment is a necessary ingredient for decision-makers. However, the prediction of volcanic eruptions remains challenging due to the complexity and the non-linearity of volcanic processes. Identified forerunners such as increasing seismicity or deformation of the volcanic edifice prior to eruption are not deterministic. In this study, we use statistical methods to identify and discriminate precursory patterns to eruptions, on three sets of observables of Piton de la Fournaise volcano. We analyzed the short-term (i.e. the inter-eruptive period) time series of the seismicity rate, the deformation and the seismic velocity changes (deduced from seismic noise cross-correlations) over the period 1999-2006, with two main goals. First, we characterize the average pre-eruptive time patterns before 22 eruptions using superposed epoch analysis for the three observables. Using daily rate values, we resolve (1) a velocity change within 100-50 days from the eruptions onsets, then a plateau value up to eruption onset; (2) a power law increase in seismicity rate from noise level 15-10 days before eruption time; (3) an increase of displacement rate on the eruption day. These results support a three step mechanism leading to magma transfers toward the surface. Second we use pattern recognition techniques and the formalization of error diagrams to quantify the predictive power of each forerunner either as used independently or as combined to each other. We show that when seismicity rate alone performs the best prediction in the failure to predict versus alarm duration space, the combination of the displacement and seismicity data reduces the false alarm rate. We further propose a tool which explores the prediction results in order to optimize prediction strategy for decision-makers, as a function of the risk value.

  3. Structure-activity relationships: quantitative techniques for predicting the behavior of chemicals in the ecosystem

    SciTech Connect

    Nirmalakhandan, N.; Speece, R.E.

    1988-06-01

    Quantitative Structure-Activity Relationships (QSARs) are used increasingly to screen and predict the toxicity and the fate of chemicals released into the environment. The impetus to use QSAR methods in this area has been the large number of synthetic chemicals introduced into the ecosystem via intensive agriculture and industrialization. Because of the costly and time-consuming nature of environmental fate testing, QSARs have been effectively used to screen large classes of chemical compounds and flag those that appear to warrant more thorough testing.

  4. Perfusion Imaging in the 3 Hour Time Window Predicts a tPA-Associated Hemorrhage in Acute Ischemic Stroke

    PubMed Central

    Rangaraju, Srikant; Edwards, Adam; Dehkharghani, Seena; Nahab, Fadi

    2015-01-01

    Perfusion imaging is being evaluated in acute ischemic stroke patients to identify those who may benefit from reperfusion therapies beyond standard thrombolytic time windows but limited data is available on its utility in patients presenting within standard thrombolytic time windows. We report a case of a patient presenting within the 3 hour time window where computerized tomographic perfusion imaging before intravenous thrombolysis identified a large volume of severely ischemic tissue and where intravenous tissue plasminogen activator (tPA) administration subsequently resulted in a fatal intracerebral hemorrhage. Whether perfusion imaging can predict an increased risk of tPA-associated symptomatic hemorrhage in patients presenting within standard thrombolytic time windows requires further study. PMID:25692511

  5. Space can substitute for time in predicting climate-change effects on biodiversity.

    PubMed

    Blois, Jessica L; Williams, John W; Fitzpatrick, Matthew C; Jackson, Stephen T; Ferrier, Simon

    2013-06-01

    "Space-for-time" substitution is widely used in biodiversity modeling to infer past or future trajectories of ecological systems from contemporary spatial patterns. However, the foundational assumption--that drivers of spatial gradients of species composition also drive temporal changes in diversity--rarely is tested. Here, we empirically test the space-for-time assumption by constructing orthogonal datasets of compositional turnover of plant taxa and climatic dissimilarity through time and across space from Late Quaternary pollen records in eastern North America, then modeling climate-driven compositional turnover. Predictions relying on space-for-time substitution were ∼72% as accurate as "time-for-time" predictions. However, space-for-time substitution performed poorly during the Holocene when temporal variation in climate was small relative to spatial variation and required subsampling to match the extent of spatial and temporal climatic gradients. Despite this caution, our results generally support the judicious use of space-for-time substitution in modeling community responses to climate change.

  6. Space can substitute for time in predicting climate-change effects on biodiversity

    NASA Astrophysics Data System (ADS)

    Blois, Jessica L.; Williams, John W.; Fitzpatrick, Matthew C.; Jackson, Stephen T.; Ferrier, Simon

    2013-06-01

    "Space-for-time" substitution is widely used in biodiversity modeling to infer past or future trajectories of ecological systems from contemporary spatial patterns. However, the foundational assumption-that drivers of spatial gradients of species composition also drive temporal changes in diversity-rarely is tested. Here, we empirically test the space-for-time assumption by constructing orthogonal datasets of compositional turnover of plant taxa and climatic dissimilarity through time and across space from Late Quaternary pollen records in eastern North America, then modeling climate-driven compositional turnover. Predictions relying on space-for-time substitution were ∼72% as accurate as "time-for-time" predictions. However, space-for-time substitution performed poorly during the Holocene when temporal variation in climate was small relative to spatial variation and required subsampling to match the extent of spatial and temporal climatic gradients. Despite this caution, our results generally support the judicious use of space-for-time substitution in modeling community responses to climate change.

  7. Space can substitute for time in predicting climate-change effects on biodiversity.

    PubMed

    Blois, Jessica L; Williams, John W; Fitzpatrick, Matthew C; Jackson, Stephen T; Ferrier, Simon

    2013-06-01

    "Space-for-time" substitution is widely used in biodiversity modeling to infer past or future trajectories of ecological systems from contemporary spatial patterns. However, the foundational assumption--that drivers of spatial gradients of species composition also drive temporal changes in diversity--rarely is tested. Here, we empirically test the space-for-time assumption by constructing orthogonal datasets of compositional turnover of plant taxa and climatic dissimilarity through time and across space from Late Quaternary pollen records in eastern North America, then modeling climate-driven compositional turnover. Predictions relying on space-for-time substitution were ∼72% as accurate as "time-for-time" predictions. However, space-for-time substitution performed poorly during the Holocene when temporal variation in climate was small relative to spatial variation and required subsampling to match the extent of spatial and temporal climatic gradients. Despite this caution, our results generally support the judicious use of space-for-time substitution in modeling community responses to climate change. PMID:23690569

  8. Obtaining Reliable Predictions of Terrestrial Energy Coupling From Real-Time Solar Wind Measurements

    NASA Technical Reports Server (NTRS)

    Weimer, Daniel R.

    2002-01-01

    Measurements of the interplanetary magnetic field (IMF) from the ACE (Advanced Composition Explorer), Wind, IMP-8 (Interplanetary Monitoring Platform), and Geotail spacecraft have revealed that the IMF variations are contained in phase planes that are tilted with respect to the propagation direction, resulting in continuously variable changes in propagation times between spacecraft, and therefore, to the Earth. Techniques for using 'minimum variance analysis' have been developed in order to be able to measure the phase front tilt angles, and better predict the actual propagation times from the L1 orbit to the Earth, using only the real-time IMF measurements from one spacecraft. The use of empirical models with the IMF measurements at L1 from ACE (or future satellites) for predicting 'space weather' effects has also been demonstrated.

  9. A Comparison of Center/TRACON Automation System and Airline Time of Arrival Predictions

    NASA Technical Reports Server (NTRS)

    Heere, Karen R.; Zelenka, Richard E.

    2000-01-01

    Benefits from information sharing between an air traffic service provider and a major air carrier are evaluated. Aircraft arrival time schedules generated by the NASA/FAA Center/TRACON Automation System (CTAS) were provided to the American Airlines System Operations Control Center in Fort Worth, Texas, during a field trial of a specialized CTAS display. A statistical analysis indicates that the CTAS schedules, based on aircraft trajectories predicted from real-time radar and weather data, are substantially more accurate than the traditional airline arrival time estimates, constructed from flight plans and en route crew updates. The improvement offered by CTAS is especially advantageous during periods of heavy traffic and substantial terminal area delay, allowing the airline to avoid large predictive errors with serious impact on the efficiency and profitability of flight operations.

  10. A quantitative parameter-free prediction of simulated crystal nucleation times

    SciTech Connect

    Aga, Rachel S; Morris, James R; Hoyt, Jeffrey John; Mendelev, Mikhail I.

    2006-01-01

    We present direct comparisons between simulated crystal-nucleation times and theoretical predictions using a model of aluminum, and demonstrate that a quantitative prediction can be made. All relevant thermodynamic properties of the system are known, making the agreement of our simulation data with nucleation theories free of any adjustable parameters. The role of transient nucleation is included in the classical nucleation theory approach, and shown to be necessary to understand the observed nucleation times. The calculations provide an explanation on why nucleation is difficult to observe in simulations at moderate undercoolings. Even when the simulations are significantly larger than the critical nucleus, and when simulation times are sufficiently long, at moderate undercoolings the small concentration of critical nuclei makes the probability of the nucleation low in molecular dynamics simulations.

  11. Fast time-series prediction using high-dimensional data: Evaluating confidence interval credibility

    NASA Astrophysics Data System (ADS)

    Hirata, Yoshito

    2014-05-01

    I propose an index for evaluating the credibility of confidence intervals for future observables predicted from high-dimensional time-series data. The index evaluates the distance from the current state to the data manifold. I demonstrate the index with artificial datasets generated from the Lorenz'96 II model [Lorenz, in Proceedings of the Seminar on Predictability, Vol. 1 (ECMWF, Reading, UK, 1996), p. 1], the Lorenz'96 I model [Hansen and Smith, J. Atmos. Sci. 57, 2859 (2000), 10.1175/1520-0469(2000)057<2859:TROOCI>2.0.CO;2], and the coupled map lattice, and a real dataset for the solar irradiation around Japan.

  12. A 2-D Model to Predict Time Development of Scour below Pipelines with Spoiler

    NASA Astrophysics Data System (ADS)

    Alam, M. S.; Cheng, Liang

    2010-05-01

    A lattice Boltzmann 2-D scour model is developed in order to predict time development of scour around offshore pipelines with spoiler. The fluid flow is captured employing Lattice Boltzmann method and the scour model is designed with the combination of multi-particle Cellular Automata technique and threshold of sediment entrainment technique available in literature. It is revealed that the proposed hybrid model is robust enough to predict evolution of bed profiles for flow and scour underneath offshore pipelines considering various orientation and length of spoiler attached.

  13. Predictive Optimal Control of Active and Passive Building Thermal Storage Inventory

    SciTech Connect

    Gregor P. Henze; Moncef Krarti

    2003-12-17

    Cooling of commercial buildings contributes significantly to the peak demand placed on an electrical utility grid. Time-of-use electricity rates encourage shifting of electrical loads to off-peak periods at night and weekends. Buildings can respond to these pricing signals by shifting cooling-related thermal loads either by precooling the building's massive structure or the use of active thermal energy storage systems such as ice storage. While these two thermal batteries have been engaged separately in the past, this project investigates the merits of harnessing both storage media concurrently in the context of predictive optimal control. This topical report describes the demonstration of the model-based predictive optimal control for active and passive building thermal storage inventory in a test facility in real-time using time-of-use differentiated electricity prices without demand charges. The laboratory testing findings presented in this topical report cover the second of three project phases. The novel supervisory controller successfully executed a three-step procedure consisting of (1) short-term weather prediction, (2) optimization of control strategy over the next planning horizon using a calibrated building model, and (3) post-processing of the optimal strategy to yield a control command for the current time step that can be executed in the test facility. The primary and secondary building mechanical systems were effectively orchestrated by the model-based predictive optimal controller in real-time while observing comfort and operational constraints. The findings reveal that when the optimal controller is given imperfect weather fore-casts and when the building model used for planning control strategies does not match the actual building perfectly, measured utility costs savings relative to conventional building operation can be substantial. This requires that the facility under control lends itself to passive storage utilization and the building model

  14. The Elasticity of Time: Associations between Physical Activity and Use of Time in Adolescents

    ERIC Educational Resources Information Center

    Olds, Tim; Ferrar, Katia E.; Gomersall, Sjaan R.; Maher, Carol; Walters, J. L.

    2012-01-01

    The way an individual uses one's time can greatly affect his or her health. The purpose of this article was to examine the cross-sectional cross-elasticity relationships for use of time domains in a sample of Australian adolescents. This study analyzed 24-hour recall time use data collected using the Multimedia Activity Recall for Children and…

  15. Short term prediction of dynamic hydra precipitation activity using a microwave radiometer over Eastern Himalaya, India

    NASA Astrophysics Data System (ADS)

    Singh, S.

    2015-12-01

    First ever study of the feasibility of ground based radiometric study to predict a very short term based rain precipitation study has been conducted in eastern Himalaya, Darjeeling (27.01°N, 88.15°E, 2200 masl). Short term prediction or nowcasting relates to forecasting convective precipitation for time periods less than a few hours to avoid its effect on agriculture, aviation and lifestyle. Theoretical models involving radiometric predictions are not well understood and lack in temporal and spatial resolution. In this study specific utilization of a microwave Radiometer (Radiometrics Corporation, USA) for online monitoring of precipitable rainfall activity has been observed repeatability of data has been established. Previous few studies have shown the increase of water vapour and corresponding Brightness Temperature, but in mountain climatic conditions over Darjeeling, due to presence of fog 90 % of the year, water vapour monitoring related predictions can lead to false alarms. The measurement of blackbody emission noise in the bands of 23.8 GHz and 31.4 GHz, using a quadratic regression retrieval algorithm is converted to atmospheric parameters like integrated water vapour and liquid water content. It has been found in our study that the liquid water shows significant activity prior to precipitation events even for mild and stratiform rainfall. The alarm can be generated well 20 mins before the commencement of actual rain events even in the upper atmosphere of 6 Kms, measured by a rain radar also operating in 24 Ghz microwave band. Although few rain events were found and reported which do not respond in the microwave liquid water channel. Efforts to identify such rain events and their possible explanation is going on and shall be reported in near future. Such studies are important to predict flash flooding in the Himalayas. Darjeeling owing to its geographical conditions experiences mild to very heavy rain. Such studies help improve aspects of Himalayas as

  16. Predicting survival of Salmonella in low-water activity foods: an analysis of literature data.

    PubMed

    Santillana Farakos, Sofia M; Schaffner, Donald W; Frank, Joseph F

    2014-09-01

    Factors such as temperature, water activity (aw), substrate, culture media, serotype, and strain influence the survival of Salmonella in low-aw foods. Predictive models for Salmonella survival in low-aw foods at temperatures ranging from 21 to 80(u) C and water activities below 0.6 were previously developed. Literature data on survival of Salmonella in low-aw foods were analyzed in the present study to validate these predictive models and to determine global influencing factors. The results showed the Weibull model provided suitable fits to the data in 75% of the curves as compared with the log-linear model. The secondary models predicting the time required for log-decimal reduction (log δ) and shape factor (log β) values were useful in predicting the survival of Salmonella in low-aw foods. Statistical analysis indicated overall fail-safe secondary models, with 88% of the residuals in the acceptable and safe zones (<0.5 log CFU) and a 59% correlation coefficient (R(2) = 0.35). A high variability in log δ-values and log β-values was observed, emphasizing the importance of experimental design. Factors of significant influence on the times required for first log-decimal reduction included temperature, aw, product, and serotype. Log β-values were significantly influenced by serotype, the type of inoculum (wet or dry), and whether the recovery media was selective or not. The results of this analysis provide a general overview of survival kinetics of Salmonella in low-aw foods and its influencing factors. PMID:25198835

  17. Predicting survival of Salmonella in low-water activity foods: an analysis of literature data.

    PubMed

    Santillana Farakos, Sofia M; Schaffner, Donald W; Frank, Joseph F

    2014-09-01

    Factors such as temperature, water activity (aw), substrate, culture media, serotype, and strain influence the survival of Salmonella in low-aw foods. Predictive models for Salmonella survival in low-aw foods at temperatures ranging from 21 to 80(u) C and water activities below 0.6 were previously developed. Literature data on survival of Salmonella in low-aw foods were analyzed in the present study to validate these predictive models and to determine global influencing factors. The results showed the Weibull model provided suitable fits to the data in 75% of the curves as compared with the log-linear model. The secondary models predicting the time required for log-decimal reduction (log δ) and shape factor (log β) values were useful in predicting the survival of Salmonella in low-aw foods. Statistical analysis indicated overall fail-safe secondary models, with 88% of the residuals in the acceptable and safe zones (<0.5 log CFU) and a 59% correlation coefficient (R(2) = 0.35). A high variability in log δ-values and log β-values was observed, emphasizing the importance of experimental design. Factors of significant influence on the times required for first log-decimal reduction included temperature, aw, product, and serotype. Log β-values were significantly influenced by serotype, the type of inoculum (wet or dry), and whether the recovery media was selective or not. The results of this analysis provide a general overview of survival kinetics of Salmonella in low-aw foods and its influencing factors.

  18. Significance of time awake for predicting pilots' fatigue on short-haul flights: implications for flight duty time regulations.

    PubMed

    Vejvoda, Martin; Elmenhorst, Eva-Maria; Pennig, Sibylle; Plath, Gernot; Maass, Hartmut; Tritschler, Kristjof; Basner, Mathias; Aeschbach, Daniel

    2014-10-01

    European regulations restrict the duration of the maximum daily flight duty period for pilots as a function of the duty start time and the number of scheduled flights. However, late duty end times that may include long times awake are not specifically regulated. In this study, fatigue levels in pilots finishing their duty late at night (00:00-01:59 hour) were analysed and compared with pilots starting their duty early (05:00-06:59 hour). Fatigue levels of 40 commercial short-haul pilots were studied during a total of 188 flight duty periods, of which 87 started early and 22 finished late. Pilots used a small handheld computer to maintain a duty and sleep log, and to indicate fatigue levels immediately after each flight. Sleep logs were checked with actigraphy. Pilots on late-finishing flight duty periods were more fatigued at the end of their duty than pilots on early-starting flight duty periods, despite the fact that preceding sleep duration was longer by 1.1 h. Linear mixed-model regression identified time awake as a preeminent factor predicting fatigue. Workload had a minor effect. Pilots on late-finishing flight duty periods were awake longer by an average of 5.5 h (6.6 versus 1.1 h) before commencing their duty than pilots who started early in the morning. Late-finishing flights were associated with long times awake at a time when the circadian system stops promoting alertness, and an increased, previously underestimated fatigue risk. Based on these findings, flight duty limitations should consider not only duty start time, but also the time of the final landing. PMID:25040665

  19. Significance of time awake for predicting pilots' fatigue on short-haul flights: implications for flight duty time regulations.

    PubMed

    Vejvoda, Martin; Elmenhorst, Eva-Maria; Pennig, Sibylle; Plath, Gernot; Maass, Hartmut; Tritschler, Kristjof; Basner, Mathias; Aeschbach, Daniel

    2014-10-01

    European regulations restrict the duration of the maximum daily flight duty period for pilots as a function of the duty start time and the number of scheduled flights. However, late duty end times that may include long times awake are not specifically regulated. In this study, fatigue levels in pilots finishing their duty late at night (00:00-01:59 hour) were analysed and compared with pilots starting their duty early (05:00-06:59 hour). Fatigue levels of 40 commercial short-haul pilots were studied during a total of 188 flight duty periods, of which 87 started early and 22 finished late. Pilots used a small handheld computer to maintain a duty and sleep log, and to indicate fatigue levels immediately after each flight. Sleep logs were checked with actigraphy. Pilots on late-finishing flight duty periods were more fatigued at the end of their duty than pilots on early-starting flight duty periods, despite the fact that preceding sleep duration was longer by 1.1 h. Linear mixed-model regression identified time awake as a preeminent factor predicting fatigue. Workload had a minor effect. Pilots on late-finishing flight duty periods were awake longer by an average of 5.5 h (6.6 versus 1.1 h) before commencing their duty than pilots who started early in the morning. Late-finishing flights were associated with long times awake at a time when the circadian system stops promoting alertness, and an increased, previously underestimated fatigue risk. Based on these findings, flight duty limitations should consider not only duty start time, but also the time of the final landing.

  20. Real-time prediction of ground motion using real-time correction of site amplification factors for EEW

    NASA Astrophysics Data System (ADS)

    Aoki, S.; Hoshiba, M.

    2013-12-01

    1. Introduction Hoshiba(2013a, JGR) proposed a method for prediction of ground motion based on real-time monitoring, in which hypocenter and M are not required. In this method, site amplification must be corrected in real-time manner. Hoshiba (2013b, BSSA) developed a causal recursive digital filter in the time domain for the real-time correction of frequency-dependent site factors. In this presentation we will apply this correction by using the site factors estimated by the spectral ratio method. 2. Method of site correction When the epicentral distances r to a station-pair (site 1 and 2) are much larger than the distance d between those sites, the averaged spectral ratio of S waves from many events can be regarded as the relative site factor between site 1 and 2 in the spectral ratio method. In this study, the dataset, which consists of the station-pairs and events that occurred in the area around Japan from 1996 to 2010, are selected to satisfy the conditions of 100km≦r≦350km and d≦30km, and we design the causal digital filter having similar amplitude property to relative site factor for the station-pair. The filter parameters are estimated by minimizing the residuals between the frequency-dependent site factor and modeled amplitude spectrum in the frequency range between 0.1 and 20Hz. In order to examine the effect of this method, we compare the results of following two methods for the prediction of the JMA seismic intensity. We want to focus on only site effect in this examination, so that the above-mentioned dataset is analyzed. Method A (MA): Seismic intensities at site 2 are predicted from waveforms observed at site 1 by using the correction for frequency-dependent site factors. Here, for each event the 3-componet waveforms at site 2 are first simulated from those at site 1 applying the causal filter, and then the predicted seismic intensity is calculated from the simulated waveforms by the method of Kunugi et al. (2008). Method B (MB

  1. Predicting muscle activation patterns from motion and anatomy: modelling the skull of Sphenodon (Diapsida: Rhynchocephalia)

    PubMed Central

    Curtis, Neil; Jones, Marc E. H.; Evans, Susan E.; Shi, JunFen; O'Higgins, Paul; Fagan, Michael J.

    2010-01-01

    The relationship between skull shape and the forces generated during feeding is currently under widespread scrutiny and increasingly involves the use of computer simulations such as finite element analysis. The computer models used to represent skulls are often based on computed tomography data and thus are structurally accurate; however, correctly representing muscular loading during food reduction remains a major problem. Here, we present a novel approach for predicting the forces and activation patterns of muscles and muscle groups based on their known anatomical orientation (line of action). The work was carried out for the lizard-like reptile Sphenodon (Rhynchocephalia) using a sophisticated computer-based model and multi-body dynamics analysis. The model suggests that specific muscle groups control specific motions, and that during certain times in the bite cycle some muscles are highly active whereas others are inactive. The predictions of muscle activity closely correspond to data previously recorded from live Sphenodon using electromyography. Apparent exceptions can be explained by variations in food resistance, food size, food position and lower jaw motions. This approach shows considerable promise in advancing detailed functional models of food acquisition and reduction, and for use in other musculoskeletal systems where no experimental determination of muscle activity is possible, such as in rare, endangered or extinct species. PMID:19474084

  2. Predicting Ambulance Time of Arrival to the Emergency Department Using Global Positioning System and Google Maps

    PubMed Central

    Fleischman, Ross J.; Lundquist, Mark; Jui, Jonathan; Newgard, Craig D.; Warden, Craig

    2014-01-01

    Objective To derive and validate a model that accurately predicts ambulance arrival time that could be implemented as a Google Maps web application. Methods This was a retrospective study of all scene transports in Multnomah County, Oregon, from January 1 through December 31, 2008. Scene and destination hospital addresses were converted to coordinates. ArcGIS Network Analyst was used to estimate transport times based on street network speed limits. We then created a linear regression model to improve the accuracy of these street network estimates using weather, patient characteristics, use of lights and sirens, daylight, and rush-hour intervals. The model was derived from a 50% sample and validated on the remainder. Significance of the covariates was determined by p < 0.05 for a t-test of the model coefficients. Accuracy was quantified by the proportion of estimates that were within 5 minutes of the actual transport times recorded by computer-aided dispatch. We then built a Google Maps-based web application to demonstrate application in real-world EMS operations. Results There were 48,308 included transports. Street network estimates of transport time were accurate within 5 minutes of actual transport time less than 16% of the time. Actual transport times were longer during daylight and rush-hour intervals and shorter with use of lights and sirens. Age under 18 years, gender, wet weather, and trauma system entry were not significant predictors of transport time. Our model predicted arrival time within 5 minutes 73% of the time. For lights and sirens transports, accuracy was within 5 minutes 77% of the time. Accuracy was identical in the validation dataset. Lights and sirens saved an average of 3.1 minutes for transports under 8.8 minutes, and 5.3 minutes for longer transports. Conclusions An estimate of transport time based only on a street network significantly underestimated transport times. A simple model incorporating few variables can predict ambulance time of

  3. Implicit theories about willpower predict the activation of a rest goal following self-control exertion.

    PubMed

    Job, Veronika; Bernecker, Katharina; Miketta, Stefanie; Friese, Malte

    2015-10-01

    Past research indicates that peoples' implicit theories about the nature of willpower moderate the ego-depletion effect. Only people who believe or were led to believe that willpower is a limited resource (limited-resource theory) showed lower self-control performance after an initial demanding task. As of yet, the underlying processes explaining this moderating effect by theories about willpower remain unknown. Here, we propose that the exertion of self-control activates the goal to preserve and replenish mental resources (rest goal) in people with a limited-resource theory. Five studies tested this hypothesis. In Study 1, individual differences in implicit theories about willpower predicted increased accessibility of a rest goal after self-control exertion. Furthermore, measured (Study 2) and manipulated (Study 3) willpower theories predicted an increased preference for rest-conducive objects. Finally, Studies 4 and 5 provide evidence that theories about willpower predict actual resting behavior: In Study 4, participants who held a limited-resource theory took a longer break following self-control exertion than participants with a nonlimited-resource theory. Longer resting time predicted decreased rest goal accessibility afterward. In Study 5, participants with an induced limited-resource theory sat longer on chairs in an ostensible product-testing task when they had engaged in a task requiring self-control beforehand. This research provides consistent support for a motivational shift toward rest after self-control exertion in people holding a limited-resource theory about willpower. PMID:26075793

  4. Implicit theories about willpower predict the activation of a rest goal following self-control exertion.

    PubMed

    Job, Veronika; Bernecker, Katharina; Miketta, Stefanie; Friese, Malte

    2015-10-01

    Past research indicates that peoples' implicit theories about the nature of willpower moderate the ego-depletion effect. Only people who believe or were led to believe that willpower is a limited resource (limited-resource theory) showed lower self-control performance after an initial demanding task. As of yet, the underlying processes explaining this moderating effect by theories about willpower remain unknown. Here, we propose that the exertion of self-control activates the goal to preserve and replenish mental resources (rest goal) in people with a limited-resource theory. Five studies tested this hypothesis. In Study 1, individual differences in implicit theories about willpower predicted increased accessibility of a rest goal after self-control exertion. Furthermore, measured (Study 2) and manipulated (Study 3) willpower theories predicted an increased preference for rest-conducive objects. Finally, Studies 4 and 5 provide evidence that theories about willpower predict actual resting behavior: In Study 4, participants who held a limited-resource theory took a longer break following self-control exertion than participants with a nonlimited-resource theory. Longer resting time predicted decreased rest goal accessibility afterward. In Study 5, participants with an induced limited-resource theory sat longer on chairs in an ostensible product-testing task when they had engaged in a task requiring self-control beforehand. This research provides consistent support for a motivational shift toward rest after self-control exertion in people holding a limited-resource theory about willpower.

  5. Predicting urban design effects on physical activity and public health: A case study.

    PubMed

    MacDonald Gibson, Jacqueline; Rodriguez, Daniel; Dennerlein, Taylor; Mead, Jill; Hasch, Trisha; Meacci, Grant; Levin, Stuart

    2015-09-01

    With increasing global concerns about obesity and related health effects, tools to predict how urban form affects population physical activity and health are needed. However, such tools have not been well established. This article develops a computer simulation model for forecasting the health effects of urban features that promote walking. The article demonstrates the model using a proposed small-area plan for a neighborhood of 10,400 residents in Raleigh, North Carolina, one of the fastest-growing and most sprawling U.S. cities. The simulation model predicts that the plan would increase average daily time spent walking for transportation by 17 min. As a result, annual deaths from all causes are predicted to decrease by 5.5%. Annual new cases of diabetes, coronary heart disease, stroke, and hypertension are predicted to decline by 1.9%, 2.3%, 1.3%, and 1.6%, respectively. The present value of these health benefits is $21,000 per resident.

  6. Real time prediction of marine vessel motions using Kalman filtering techniques

    NASA Technical Reports Server (NTRS)

    Triantafyllou, M. S.; Bodson, M.

    1982-01-01

    The present investigation is concerned with the prediction of the future behavior of a vessel within some confidence bounds at a specific instant of time, taking into account an interval of a few seconds. The ability to predict accurately the motions of a vessel can reduce significantly the probability of failure of operations in rough seas. The investigation was started as part of an effort to ensure safe landing of aircraft on relatively small vessels. However, the basic principles involved in the study are the same for any offshore operation, such as carbo transfer in the open sea, structure installation, and floating crane operation. The Kalman filter is a powerful tool for achieving the goals of the prediction procedure. Attention is given to a linear optimal predictor, the equations of motion of the vessel, the wave spectrum, rational approximation, the use of Kalman filter and predictor in an application for a ship, and the motions of a semisubmersible.

  7. Evaluation of a Satellite-based Near Real-time Global Flood Prediction System

    NASA Astrophysics Data System (ADS)

    Yilmaz, K. K.; Adler, R.; Pierce, H.

    2009-04-01

    Satellite-based rainfall and geospatial datasets are potentially useful for cost effective detection and early warning of natural hazards, such as floods, specifically for regions of the world where local data are sparse or non-existent. Recently, our group has implemented an initial satellite-based near real-time global flood prediction system that is operationally available. In this system, a relatively simple hydrologic model, based on the runoff curve number (CN) and antecedent precipitation index (API) methods, transforms rainfall into runoff. Runoff is then routed grid-to-grid to estimate flow. The key input to the current system is the near real-time rainfall estimates from the NASA-based Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA; 3 hourly, 0.25x0.25 degree). In this study we will present an in-depth testing/evaluation of this current flood prediction system, discuss its strengths and limitations and point toward potential improvements necessary for increasing its near real-time global flood prediction reliability and accuracy. This evaluation study focuses on the severe flooding events and will include comparison of the current product with observed runoff and inundation data at global and watershed scale as well as with other available remotely sensed products, such as those from Dartmouth Flood Observatory. Initial evaluation suggests that current global near-real time flood predictions provide valuable information related to spatial extent and onset time of extreme flooding events. However the accuracy diminishes in tracking the later stages of the flood event. This behavior suggests that one way to improve the current system is a new (possibly finer scale) routing component. Of course, flood predictions are intimately tied to the accuracy of the satellite-based rainfall estimates. Our presentation will also compare the performance of the flood prediction system when the current version of the NASA TMPA real

  8. Dynamic variation in pleasure in children predicts nonlinear change in lateral frontal brain electrical activity.

    PubMed

    Light, Sharee N; Coan, James A; Frye, Corrina; Goldsmith, H Hill; Davidson, Richard J

    2009-03-01

    Individual variation in the experience and expression of pleasure may relate to differential patterns of lateral frontal activity. Brain electrical measures have been used to study the asymmetric involvement of lateral frontal cortex in positive emotion, but the excellent time resolution of these measures has not been used to capture second-by-second changes in ongoing emotion until now. The relationship between pleasure and second-by-second lateral frontal activity was examined with the use of hierarchical linear modeling in a sample of 128 children ages 6-10 years. Electroencephalographic activity was recorded during "pop-out toy," a standardized task that elicits pleasure. The task consisted of 3 epochs: an anticipation period sandwiched between 2 play periods. The amount of pleasure expressed during the task predicted the pattern of nonlinear change in lateral frontal activity. Children who expressed increasing amounts of pleasure during the task exhibited increasing left lateral frontal activity during the task, whereas children who expressed contentment exhibited increasing right/decreasing left activity. These findings indicate that task-dependent changes in pleasure relate to dynamic, nonlinear changes in lateral frontal activity as the task unfolds. PMID:19271836

  9. Visual cortex activity predicts subjective experience after reading books with colored letters.

    PubMed

    Colizoli, Olympia; Murre, Jaap M J; Scholte, H Steven; van Es, Daniel M; Knapen, Tomas; Rouw, Romke

    2016-07-29

    One of the most astonishing properties of synesthesia is that the evoked concurrent experiences are perceptual. Is it possible to acquire similar effects after learning cross-modal associations that resemble synesthetic mappings? In this study, we examine whether brain activation in early visual areas can be directly related to letter-color associations acquired by training. Non-synesthetes read specially prepared books with colored letters for several weeks and were scanned using functional magnetic resonance imaging. If the acquired letter-color associations were visual in nature, then brain activation in visual cortex while viewing the trained black letters (compared to untrained black letters) should predict the strength of the associations, the quality of the color experience, or the vividness of visual mental imagery. Results showed that training-related activation of area V4 was correlated with differences in reported subjective color experience. Trainees who were classified as having stronger 'associator' types of color experiences also had more negative activation for trained compared to untrained achromatic letters in area V4. In contrast, the strength of the acquired associations (measured as the Stroop effect) was not reliably reflected in visual cortex activity. The reported vividness of visual mental imagery was related to veridical color activation in early visual cortex, but not to the acquired color associations. We show for the first time that subjective experience related to a synesthesia-training paradigm was reflected in visual brain activation.

  10. Visual cortex activity predicts subjective experience after reading books with colored letters.

    PubMed

    Colizoli, Olympia; Murre, Jaap M J; Scholte, H Steven; van Es, Daniel M; Knapen, Tomas; Rouw, Romke

    2016-07-29

    One of the most astonishing properties of synesthesia is that the evoked concurrent experiences are perceptual. Is it possible to acquire similar effects after learning cross-modal associations that resemble synesthetic mappings? In this study, we examine whether brain activation in early visual areas can be directly related to letter-color associations acquired by training. Non-synesthetes read specially prepared books with colored letters for several weeks and were scanned using functional magnetic resonance imaging. If the acquired letter-color associations were visual in nature, then brain activation in visual cortex while viewing the trained black letters (compared to untrained black letters) should predict the strength of the associations, the quality of the color experience, or the vividness of visual mental imagery. Results showed that training-related activation of area V4 was correlated with differences in reported subjective color experience. Trainees who were classified as having stronger 'associator' types of color experiences also had more negative activation for trained compared to untrained achromatic letters in area V4. In contrast, the strength of the acquired associations (measured as the Stroop effect) was not reliably reflected in visual cortex activity. The reported vividness of visual mental imagery was related to veridical color activation in early visual cortex, but not to the acquired color associations. We show for the first time that subjective experience related to a synesthesia-training paradigm was reflected in visual brain activation. PMID:26162617

  11. Predicting the start week of respiratory syncytial virus outbreaks using real time weather variables

    PubMed Central

    2010-01-01

    Background Respiratory Syncytial Virus (RSV), a major cause of bronchiolitis, has a large impact on the census of pediatric hospitals during outbreak seasons. Reliable prediction of the week these outbreaks will start, based on readily available data, could help pediatric hospitals better prepare for large outbreaks. Methods Naïve Bayes (NB) classifier models were constructed using weather data from 1985-2008 considering only variables that are available in real time and that could be used to forecast the week in which an RSV outbreak will occur in Salt Lake County, Utah. Outbreak start dates were determined by a panel of experts using 32,509 records with ICD-9 coded RSV and bronchiolitis diagnoses from Intermountain Healthcare hospitals and clinics for the RSV seasons from 1985 to 2008. Results NB models predicted RSV outbreaks up to 3 weeks in advance with an estimated sensitivity of up to 67% and estimated specificities as high as 94% to 100%. Temperature and wind speed were the best overall predictors, but other weather variables also showed relevance depending on how far in advance the predictions were made. The weather conditions predictive of an RSV outbreak in our study were similar to those that lead to temperature inversions in the Salt Lake Valley. Conclusions We demonstrate that Naïve Bayes (NB) classifier models based on weather data available in real time have the potential to be used as effective predictive models. These models may be able to predict the week that an RSV outbreak will occur with clinical relevance. Their clinical usefulness will be field tested during the next five years. PMID:21044325

  12. Incorporating system latency associated with real-time target tracking radiotherapy in the dose prediction step

    NASA Astrophysics Data System (ADS)

    Roland, Teboh; Mavroidis, Panayiotis; Shi, Chengyu; Papanikolaou, Nikos

    2010-05-01

    System latency introduces geometric errors in the course of real-time target tracking radiotherapy. This effect can be minimized, for example by the use of predictive filters, but cannot be completely avoided. In this work, we present a convolution technique that can incorporate the effect as part of the treatment planning process. The method can be applied independently or in conjunction with the predictive filters to compensate for residual latency effects. The implementation was performed on TrackBeam (Initia Ltd, Israel), a prototype real-time target tracking system assembled and evaluated at our Cancer Institute. For the experimental system settings examined, a Gaussian distribution attributable to the TrackBeam latency was derived with σ = 3.7 mm. The TrackBeam latency, expressed as an average response time, was deduced to be 172 ms. Phantom investigations were further performed to verify the convolution technique. In addition, patient studies involving 4DCT volumes of previously treated lung cancer patients were performed to incorporate the latency effect in the dose prediction step. This also enabled us to effectively quantify the dosimetric and radiobiological impact of the TrackBeam and other higher latency effects on the clinical outcome of a real-time target tracking delivery.

  13. Neural-network predictive control for nonlinear dynamic systems with time-delay.

    PubMed

    Huang, Jin-Quan; Lewis, F L

    2003-01-01

    A new recurrent neural-network predictive feedback control structure for a class of uncertain nonlinear dynamic time-delay systems in canonical form is developed and analyzed. The dynamic system has constant input and feedback time delays due to a communications channel. The proposed control structure consists of a linearized subsystem local to the controlled plant and a remote predictive controller located at the master command station. In the local linearized subsystem, a recurrent neural network with on-line weight tuning algorithm is employed to approximate the dynamics of the time-delay-free nonlinear plant. No linearity in the unknown parameters is required. No preliminary off-line weight learning is needed. The remote controller is a modified Smith predictor that provides prediction and maintains the desired tracking performance; an extra robustifying term is needed to guarantee stability. Rigorous stability proofs are given using Lyapunov analysis. The result is an adaptive neural net compensation scheme for unknown nonlinear systems with time delays. A simulation example is provided to demonstrate the effectiveness of the proposed control strategy.

  14. Circadian Activity Rhythms, Time Urgency, and Achievement Concerns.

    ERIC Educational Resources Information Center

    Watts, Barbara L.

    Many physiological and psychological processes fluctuate throughout the day in fairly stable, rhythmic patterns. The relationship between individual differences in circadian activity rhythms and a sense of time urgency were explored as well as a number of achievement-related variables. Undergraduates (N=308), whose circadian activity rhythms were…

  15. Predicting mountain lion activity using radiocollars equipped with mercury tip-sensors

    USGS Publications Warehouse

    Janis, Michael W.; Clark, Joseph D.; Johnson, Craig

    1999-01-01

    Radiotelemetry collars with tip-sensors have long been used to monitor wildlife activity. However, comparatively few researchers have tested the reliability of the technique on the species being studied. To evaluate the efficacy of using tip-sensors to assess mountain lion (Puma concolor) activity, we radiocollared 2 hand-reared mountain lions and simultaneously recorded their behavior and the associated telemetry signal characteristics. We noted both the number of pulse-rate changes and the percentage of time the transmitter emitted a fast pulse rate (i.e., head up) within sampling intervals ranging from 1-5 minutes. Based on 27 hours of observations, we were able to correctly distinguish between active and inactive behaviors >93% of the time using a logistic regression model. We present several models to predict activity of mountain lions; the selection of which to us would depend on study objectives and logistics. Our results indicate that field protocols that use only pulse-rate changes to indicate activity can lead to significant classification errors.

  16. A Long-Term Prediction Model of Beijing Haze Episodes Using Time Series Analysis

    PubMed Central

    Zhang, Zhongqiu; Sun, Liren; Xu, Cui

    2016-01-01

    The rapid industrial development has led to the intermittent outbreak of pm2.5 or haze in developing countries, which has brought about great environmental issues, especially in big cities such as Beijing and New Delhi. We investigated the factors and mechanisms of haze change and present a long-term prediction model of Beijing haze episodes using time series analysis. We construct a dynamic structural measurement model of daily haze increment and reduce the model to a vector autoregressive model. Typical case studies on 886 continuous days indicate that our model performs very well on next day's Air Quality Index (AQI) prediction, and in severely polluted cases (AQI ≥ 300) the accuracy rate of AQI prediction even reaches up to 87.8%. The experiment of one-week prediction shows that our model has excellent sensitivity when a sudden haze burst or dissipation happens, which results in good long-term stability on the accuracy of the next 3–7 days' AQI prediction.

  17. Predictive decision making driven by multiple time-linked reward representations in the anterior cingulate cortex.

    PubMed

    Wittmann, Marco K; Kolling, Nils; Akaishi, Rei; Chau, Bolton K H; Brown, Joshua W; Nelissen, Natalie; Rushworth, Matthew F S

    2016-01-01

    In many natural environments the value of a choice gradually gets better or worse as circumstances change. Discerning such trends makes predicting future choice values possible. We show that humans track such trends by comparing estimates of recent and past reward rates, which they are able to hold simultaneously in the dorsal anterior cingulate cortex (dACC). Comparison of recent and past reward rates with positive and negative decision weights is reflected by opposing dACC signals indexing these quantities. The relative strengths of time-linked reward representations in dACC predict whether subjects persist in their current behaviour or switch to an alternative. Computationally, trend-guided choice can be modelled by using a reinforcement-learning mechanism that computes a longer-term estimate (or expectation) of prediction errors. Using such a model, we find a relative predominance of expected prediction errors in dACC, instantaneous prediction errors in the ventral striatum and choice signals in the ventromedial prefrontal cortex. PMID:27477632

  18. A Long-Term Prediction Model of Beijing Haze Episodes Using Time Series Analysis.

    PubMed

    Yang, Xiaoping; Zhang, Zhongxia; Zhang, Zhongqiu; Sun, Liren; Xu, Cui; Yu, Li

    2016-01-01

    The rapid industrial development has led to the intermittent outbreak of pm2.5 or haze in developing countries, which has brought about great environmental issues, especially in big cities such as Beijing and New Delhi. We investigated the factors and mechanisms of haze change and present a long-term prediction model of Beijing haze episodes using time series analysis. We construct a dynamic structural measurement model of daily haze increment and reduce the model to a vector autoregressive model. Typical case studies on 886 continuous days indicate that our model performs very well on next day's Air Quality Index (AQI) prediction, and in severely polluted cases (AQI ≥ 300) the accuracy rate of AQI prediction even reaches up to 87.8%. The experiment of one-week prediction shows that our model has excellent sensitivity when a sudden haze burst or dissipation happens, which results in good long-term stability on the accuracy of the next 3-7 days' AQI prediction.

  19. A Long-Term Prediction Model of Beijing Haze Episodes Using Time Series Analysis

    PubMed Central

    Zhang, Zhongqiu; Sun, Liren; Xu, Cui

    2016-01-01

    The rapid industrial development has led to the intermittent outbreak of pm2.5 or haze in developing countries, which has brought about great environmental issues, especially in big cities such as Beijing and New Delhi. We investigated the factors and mechanisms of haze change and present a long-term prediction model of Beijing haze episodes using time series analysis. We construct a dynamic structural measurement model of daily haze increment and reduce the model to a vector autoregressive model. Typical case studies on 886 continuous days indicate that our model performs very well on next day's Air Quality Index (AQI) prediction, and in severely polluted cases (AQI ≥ 300) the accuracy rate of AQI prediction even reaches up to 87.8%. The experiment of one-week prediction shows that our model has excellent sensitivity when a sudden haze burst or dissipation happens, which results in good long-term stability on the accuracy of the next 3–7 days' AQI prediction. PMID:27597861

  20. Predictive decision making driven by multiple time-linked reward representations in the anterior cingulate cortex

    PubMed Central

    Wittmann, Marco K.; Kolling, Nils; Akaishi, Rei; Chau, Bolton K. H.; Brown, Joshua W.; Nelissen, Natalie; Rushworth, Matthew F. S.

    2016-01-01

    In many natural environments the value of a choice gradually gets better or worse as circumstances change. Discerning such trends makes predicting future choice values possible. We show that humans track such trends by comparing estimates of recent and past reward rates, which they are able to hold simultaneously in the dorsal anterior cingulate cortex (dACC). Comparison of recent and past reward rates with positive and negative decision weights is reflected by opposing dACC signals indexing these quantities. The relative strengths of time-linked reward representations in dACC predict whether subjects persist in their current behaviour or switch to an alternative. Computationally, trend-guided choice can be modelled by using a reinforcement-learning mechanism that computes a longer-term estimate (or expectation) of prediction errors. Using such a model, we find a relative predominance of expected prediction errors in dACC, instantaneous prediction errors in the ventral striatum and choice signals in the ventromedial prefrontal cortex. PMID:27477632

  1. A Long-Term Prediction Model of Beijing Haze Episodes Using Time Series Analysis.

    PubMed

    Yang, Xiaoping; Zhang, Zhongxia; Zhang, Zhongqiu; Sun, Liren; Xu, Cui; Yu, Li

    2016-01-01

    The rapid industrial development has led to the intermittent outbreak of pm2.5 or haze in developing countries, which has brought about great environmental issues, especially in big cities such as Beijing and New Delhi. We investigated the factors and mechanisms of haze change and present a long-term prediction model of Beijing haze episodes using time series analysis. We construct a dynamic structural measurement model of daily haze increment and reduce the model to a vector autoregressive model. Typical case studies on 886 continuous days indicate that our model performs very well on next day's Air Quality Index (AQI) prediction, and in severely polluted cases (AQI ≥ 300) the accuracy rate of AQI prediction even reaches up to 87.8%. The experiment of one-week prediction shows that our model has excellent sensitivity when a sudden haze burst or dissipation happens, which results in good long-term stability on the accuracy of the next 3-7 days' AQI prediction. PMID:27597861

  2. Focused attention vs. crossmodal signals paradigm: deriving predictions from the time-window-of-integration model.

    PubMed

    Colonius, Hans; Diederich, Adele

    2012-01-01

    In the crossmodal signals paradigm (CSP) participants are instructed to respond to a set of stimuli from different modalities, presented more or less simultaneously, as soon as a stimulus from any modality has been detected. In the focused attention paradigm (FAP), on the other hand, responses should only be made to a stimulus from a pre-defined target modality and stimuli from non-target modalities should be ignored. Whichever paradigm is being applied, a typical result is that responses tend to be faster to crossmodal stimuli than to unimodal stimuli, a phenomenon often referred to as "crossmodal interaction." Here, we investigate predictions of the time-window-of-integration (TWIN) modeling framework previously proposed by the authors. It is shown that TWIN makes specific qualitative and quantitative predictions on how the two paradigms differ with respect to the probability of multisensory integration and the amount of response enhancement, including the effect of stimulus intensity ("inverse effectiveness"). Introducing a decision-theoretic framework for TWIN further allows comparing the two paradigms with respect to the predicted optimal time window size and its dependence on the prior probability that the crossmodal stimulus information refers to the same event. In order to test these predictions, experimental studies that systematically compare crossmodal effects under stimulus conditions that are identical except for the CSP-FAP instruction should be performed in the future. PMID:22952460

  3. Predicting the Incidence of Smear Positive Tuberculosis Cases in Iran Using Time Series Analysis

    PubMed Central

    MOOSAZADEH, Mahmood; KHANJANI, Narges; NASEHI, Mahshid; BAHRAMPOUR, Abbas

    2015-01-01

    Background: Determining the temporal variation and forecasting the incidence of smear positive tuberculosis (TB) can play an important role in promoting the TB control program. Its results may be used as a decision-supportive tool for planning and allocating resources. The present study forecasts the incidence of smear positive TB in Iran. Methods: This a longitudinal study using monthly tuberculosis incidence data recorded in the Iranian National Tuberculosis Control Program. The sum of registered cases in each month created 84 time points. Time series methods were used for analysis. Based on the residual chart of ACF, PACF, Ljung-Box tests and the lowest levels of AIC and BIC, the most suitable model was selected. Results: From April 2005 until March 2012, 34012 smear positive TB cases were recorded. The mean of TB monthly incidence was 404.9 (SD=54.7). The highest number of cases was registered in May and the difference in monthly incidence of smear positive TB was significant (P<0.001). SARIMA (0,1,1)(0,1,1)12 was selected as the most adequate model for prediction. It was predicted that the incidence of smear positive TB for 2015 will be about 9.8 per 100,000 people. Conclusion: Based on the seasonal pattern of smear positive TB recorded cases, seasonal ARIMA model was suitable for predicting its incidence. Meanwhile, prediction results show an increasing trend of smear positive TB cases in Iran. PMID:26744711

  4. Pulse-echo ultrasound transit time spectroscopy: A comparison of experimental measurement and simulation prediction.

    PubMed

    Wille, Marie-Luise; Almualimi, Majdi A; Langton, Christian M

    2016-01-01

    Considering ultrasound propagation through complex composite media as an array of parallel sonic rays, a comparison of computer-simulated prediction with experimental data has previously been reported for transmission mode (where one transducer serves as transmitter, the other as receiver) in a series of 10 acrylic step-wedge samples, immersed in water, exhibiting varying degrees of transit time inhomogeneity. In this study, the same samples were used but in pulse-echo mode, where the same ultrasound transducer served as both transmitter and receiver, detecting both 'primary' (internal sample interface) and 'secondary' (external sample interface) echoes. A transit time spectrum was derived, describing the proportion of sonic rays with a particular transit time. A computer simulation was performed to predict the transit time and amplitude of various echoes created, and compared with experimental data. Applying an amplitude-tolerance analysis, 91.7% ± 3.7% of the simulated data were within ±1 standard deviation of the experimentally measured amplitude-time data. Correlation of predicted and experimental transit time spectra provided coefficients of determination (R(2)%) ranging from 100.0% to 96.8% for the various samples tested. The results acquired from this study provide good evidence for the concept of parallel sonic rays. Furthermore, deconvolution of experimental input and output signals has been shown to provide an effective method to identify echoes otherwise lost due to phase cancellation. Potential applications of pulse-echo ultrasound transit time spectroscopy include improvement of ultrasound image fidelity by improving spatial resolution and reducing phase interference artefacts.

  5. Subgenual PFC Activity Predicts Individual Differences in HPA Activity Across Different Contexts

    PubMed Central

    Jahn, Allison L.; Fox, Andrew S.; Abercrombie, Heather C.; Shelton, Steven E.; Oakes, Terrence R.; Davidson, Richard J.; Kalin, Ned H.

    2009-01-01

    Background Hypothalamic-pituitary-adrenal (HPA) system activation is adaptive in response to stress, and HPA dysregulation occurs in stress-related psychopathology. It is important to understand the mechanisms that modulate HPA output; yet, few studies have addressed the neural circuitry associated with HPA regulation in primates and humans. Using high-resolution [F-18]-fluoro-deoxyglucose (FDG) positron emission tomography (PET) in rhesus monkeys, we assessed the relation between individual differences in brain activity and HPA function across multiple contexts that varied in stressfulness. Methods Using a logical AND conjunctions analysis, we assessed cortisol and brain metabolic activity with FDG-PET in 35 adolescent rhesus monkeys exposed to two threat and two home-cage conditions. To test the robustness of our findings, we used similar methods in an archival data set. In this data set, brain metabolic activity and cortisol were assessed in 17 adolescent male rhesus monkeys that were exposed to three different stress-related contexts. Results Results from the two studies, revealed that subgenual PFC metabolism (Area 25/24) consistently predicted individual differences in plasma cortisol concentrations regardless of the context in which brain activity and cortisol were assessed. Conclusions These findings suggest that activation in subgenual PFC may be related to HPA output across a variety of contexts (including familiar settings and novel or threatening situations). Individuals prone to elevated subgenual PFC activity across multiple contexts may be individuals who consistently show heightened cortisol, and may be at risk for stress-related HPA dysregulation. PMID:19846063

  6. Prediction of Pathway Activation by Xenobiotic-Responsive Transcription Factors in the Mouse Liver

    EPA Science Inventory

    Many drugs and environmentally-relevant chemicals activate xenobioticresponsive transcription factors (TF). Identification of target genes of these factors would be useful in predicting pathway activation in in vitro chemical screening. Starting with a large compendium of Affymet...

  7. Space can substitute for time in predicting climate-change effects on biodiversity

    USGS Publications Warehouse

    Blois, Jessica L.; Williams, John W.; Fitzpatrick, Matthew C.; Jackson, Stephen T.; Ferrier, Simon

    2013-01-01

    “Space-for-time” substitution is widely used in biodiversity modeling to infer past or future trajectories of ecological systems from contemporary spatial patterns. However, the foundational assumption—that drivers of spatial gradients of species composition also drive temporal changes in diversity—rarely is tested. Here, we empirically test the space-for-time assumption by constructing orthogonal datasets of compositional turnover of plant taxa and climatic dissimilarity through time and across space from Late Quaternary pollen records in eastern North America, then modeling climate-driven compositional turnover. Predictions relying on space-for-time substitution were ∼72% as accurate as “time-for-time” predictions. However, space-for-time substitution performed poorly during the Holocene when temporal variation in climate was small relative to spatial variation and required subsampling to match the extent of spatial and temporal climatic gradients. Despite this caution, our results generally support the judicious use of space-for-time substitution in modeling community responses to climate change.

  8. PREDICTING OUTCOMES IN PATIENTS WITH CHRONIC MYELOID LEUKEMIA AT ANY TIME DURING TYROSINE KINASE INHIBITOR THERAPY

    PubMed Central

    Quintás-Cardama, Alfonso; Choi, Sangbum; Kantarjian, Hagop; Jabbour, Elias; Huang, Xuelin; Cortes, Jorge

    2014-01-01

    Current recommendations for monitoring patients with chronic myeloid leukemia (CML) provide recommendations for response assessment and treatment only at 3, 6, 12, and 18 months. These recommendations are based on clinical trial outcomes computed from treatment start. Conditional survival estimates take into account the changing hazard rates as time from treatment elapses as a continuum. We performed conditional survival analyses among patients with CML to improve prognostication at any time point during the course of therapy. We used two cohorts of patients with CML in chronic phase: one treated in the frontline DASISION phase III study (n=519) and another treated after imatinib failure in the dasatinib dose-optimization phase III CA180-034 study (n=670). Conditional survival estimates were calculated. A modified Cox proportional hazards model was used to build a prognostic nomogram. As the time alive or free from events from commencement of treatment increased, conditional survival estimates changed. No differences were observed regarding future outcomes between patients treated with imatinib or dasatinib in the frontline setting for patients with the same BCR-ABL1 transcript levels evaluated at the same time-point. Age over 60 years greatly impacted future outcomes particularly in the short-term. Conditional survival-based nomograms allowed the prediction of future outcomes at any time-point. In summary, we designed a calculator to predict future outcomes of patients with CML at any time-point during the course of therapy. PMID:24594142

  9. Space can substitute for time in predicting climate-change effects on biodiversity

    PubMed Central

    Blois, Jessica L.; Williams, John W.; Fitzpatrick, Matthew C.; Jackson, Stephen T.; Ferrier, Simon

    2013-01-01

    “Space-for-time” substitution is widely used in biodiversity modeling to infer past or future trajectories of ecological systems from contemporary spatial patterns. However, the foundational assumption—that drivers of spatial gradients of species composition also drive temporal changes in diversity—rarely is tested. Here, we empirically test the space-for-time assumption by constructing orthogonal datasets of compositional turnover of plant taxa and climatic dissimilarity through time and across space from Late Quaternary pollen records in eastern North America, then modeling climate-driven compositional turnover. Predictions relying on space-for-time substitution were ∼72% as accurate as “time-for-time” predictions. However, space-for-time substitution performed poorly during the Holocene when temporal variation in climate was small relative to spatial variation and required subsampling to match the extent of spatial and temporal climatic gradients. Despite this caution, our results generally support the judicious use of space-for-time substitution in modeling community responses to climate change. PMID:23690569

  10. Predicting time to prostate cancer recurrence based on joint models for non-linear longitudinal biomarkers and event time outcomes.

    PubMed

    Pauler, Donna K; Finkelstein, Dianne M

    2002-12-30

    Biological markers that are both sensitive and specific for tumour regrowth or metastasis are increasingly becoming available and routinely monitored during the regular follow-up of patients treated for cancer. Obtained by a simple blood test, these markers provide an inexpensive non-invasive means for the early detection of recurrence (or progression). Currently, the longitudinal behaviour of the marker is viewed as an indicator of early disease progression, and is applied by a physician in making clinical decisions. One marker that has been studied for use in both population screening for early disease and for detection of recurrence in prostate cancer patients is PSA. The elevation of PSA levels is known to precede clinically detectable recurrence by 2 to 5 years, and current clinical practice often relies partially on multiple recent rises in PSA to trigger a change in treatment. However, the longitudinal trajectory for individual markers is often non-linear; in many cases there is a decline immediately following radiation therapy or surgery, a plateau during remission, followed by an exponential rise following the recurrence of the cancer. The aim of this article is to determine the multiple aspects of the longitudinal PSA biomarker trajectory that can be most sensitive for predicting time to clinical recurrence. Joint Bayesian models for the longitudinal measures and event times are utilized based on non-linear hierarchical models, implied by unknown change-points, for the longitudinal trajectories, and a Cox proportional hazard model for progression times, with functionals of the longitudinal parameters as covariates in the Cox model. Using Markov chain Monte Carlo sampling schemes, the joint model is fit to longitudinal PSA measures from 676 patients treated at Massachusetts General Hospital between the years 1988 and 1995 with follow-up to 1999. Based on these data, predictive schemes for detecting cancer recurrence in new patients based on their

  11. Real-time prediction of clinical trial enrollment and event counts: A review.

    PubMed

    Heitjan, Daniel F; Ge, Zhiyun; Ying, Gui-Shuang

    2015-11-01

    Clinical trial planning involves the specification of a projected duration of enrollment and follow-up needed to achieve the targeted study power. If pre-trial estimates of enrollment and event rates are inaccurate, projections can be faulty, leading potentially to inadequate power or other mis-allocation of resources. Recent years have witnessed the development of methods that use the accumulating data from the trial itself to create improved predictions in real time. We review these methods, taking as a case study REMATCH, a trial that compared a left-ventricular assist device to optimal medical management in the treatment of end-stage heart failure. REMATCH provided the motivation and test bed for the first real-time clinical trial prediction model. Our review summarizes developments to date and points to unresolved issues and open research opportunities. PMID:26188165

  12. Constructing and predicting solitary pattern solutions for nonlinear time-fractional dispersive partial differential equations

    NASA Astrophysics Data System (ADS)

    Arqub, Omar Abu; El-Ajou, Ahmad; Momani, Shaher

    2015-07-01

    Building fractional mathematical models for specific phenomena and developing numerical or analytical solutions for these fractional mathematical models are crucial issues in mathematics, physics, and engineering. In this work, a new analytical technique for constructing and predicting solitary pattern solutions of time-fractional dispersive partial differential equations is proposed based on the generalized Taylor series formula and residual error function. The new approach provides solutions in the form of a rapidly convergent series with easily computable components using symbolic computation software. For method evaluation and validation, the proposed technique was applied to three different models and compared with some of the well-known methods. The resultant simulations clearly demonstrate the superiority and potentiality of the proposed technique in terms of the quality performance and accuracy of substructure preservation in the construct, as well as the prediction of solitary pattern solutions for time-fractional dispersive partial differential equations.

  13. Predicting the time-temperature dependent axial failure of B/A1 composites

    NASA Technical Reports Server (NTRS)

    Dicarlo, J. A.

    1980-01-01

    Experimental and theoretical studies were conducted in order to understand and predict the effects of time, temperature, and stress on the axial failure modes of boron fibers and B/A1 composites. Due to the anelastic nature of boron fiber deformation, it was possible to determine simple creep functions which can be employed to accurately describe creep and fracture stress of as-produced fibers. Analysis of damping and strength data for B/6061 A1 composites indicates that fiber creep effects of creep on fiber fracture are measurably reduced by the composite fabrication process. The creep function appropriate for fibers with B/Al composites was also determined. A fracture theory is presented for predicting the time-temperature dependence of the axial tensile strength for metal matrix composites in general and B/A1 composites in particular.

  14. Comparisons of Crosswind Velocity Profile Estimates Used in Fast-Time Wake Vortex Prediction Models

    NASA Technical Reports Server (NTRS)

    Pruis, Mathew J.; Delisi, Donald P.; Ahmad, Nashat N.

    2011-01-01

    Five methods for estimating crosswind profiles used in fast-time wake vortex prediction models are compared in this study. Previous investigations have shown that temporal and spatial variations in the crosswind vertical profile have a large impact on the transport and time evolution of the trailing vortex pair. The most important crosswind parameters are the magnitude of the crosswind and the gradient in the crosswind shear. It is known that pulsed and continuous wave lidar measurements can provide good estimates of the wind profile in the vicinity of airports. In this study comparisons are made between estimates of the crosswind profiles from a priori information on the trajectory of the vortex pair as well as crosswind profiles derived from different sensors and a regional numerical weather prediction model.

  15. A Two-Time Scale Decentralized Model Predictive Controller Based on Input and Output Model

    PubMed Central

    Niu, Jian; Zhao, Jun; Xu, Zuhua; Qian, Jixin

    2009-01-01

    A decentralized model predictive controller applicable for some systems which exhibit different dynamic characteristics in different channels was presented in this paper. These systems can be regarded as combinations of a fast model and a slow model, the response speeds of which are in two-time scale. Because most practical models used for control are obtained in the form of transfer function matrix by plant tests, a singular perturbation method was firstly used to separate the original transfer function matrix into two models in two-time scale. Then a decentralized model predictive controller was designed based on the two models derived from the original system. And the stability of the control method was proved. Simulations showed that the method was effective. PMID:19834542

  16. Reduction in predicted survival times in cold water due to wind and waves.

    PubMed

    Power, Jonathan; Simões Ré, António; Barwood, Martin; Tikuisis, Peter; Tipton, Michael

    2015-07-01

    Recent marine accidents have called into question the level of protection provided by immersion suits in real (harsh) life situations. Two immersion suit studies, one dry and the other with 500 mL of water underneath the suit, were conducted in cold water with 10-12 males in each to test body heat loss under three environmental conditions: calm, as mandated for immersion suit certification, and two combinations of wind plus waves to simulate conditions typically found offshore. In both studies mean skin heat loss was higher in wind and waves vs. calm; deep body temperature and oxygen consumption were not different. Mean survival time predictions exceeded 36 h for all conditions in the first study but were markedly less in the second in both calm and wind and waves. Immersion suit protection and consequential predicted survival times under realistic environmental conditions and with leakage are reduced relative to calm conditions.

  17. Reduction in predicted survival times in cold water due to wind and waves.

    PubMed

    Power, Jonathan; Simões Ré, António; Barwood, Martin; Tikuisis, Peter; Tipton, Michael

    2015-07-01

    Recent marine accidents have called into question the level of protection provided by immersion suits in real (harsh) life situations. Two immersion suit studies, one dry and the other with 500 mL of water underneath the suit, were conducted in cold water with 10-12 males in each to test body heat loss under three environmental conditions: calm, as mandated for immersion suit certification, and two combinations of wind plus waves to simulate conditions typically found offshore. In both studies mean skin heat loss was higher in wind and waves vs. calm; deep body temperature and oxygen consumption were not different. Mean survival time predictions exceeded 36 h for all conditions in the first study but were markedly less in the second in both calm and wind and waves. Immersion suit protection and consequential predicted survival times under realistic environmental conditions and with leakage are reduced relative to calm conditions. PMID:25766418

  18. Plant folklore: a tool for predicting sources of antitumor activity?

    PubMed

    Spjut, R W; Perdue, R E

    1976-08-01

    The National Cancer Institute's record of "active plants" (extracts which showed a significant inhibitory effect in experimental tumor systems) was compared with plants reported in folklore to have medicinal or poisonous properties. The occurrence of active plants was found to be higher in plants reported in folk literature than in plants collected at random, suggesting a correlation between plants used in folklore and those with anticancer activity.

  19. Activity levels in the left hemisphere caudate–fusiform circuit predict how well a second language will be learned

    PubMed Central

    Tan, Li Hai; Chen, Lin; Yip, Virginia; Chan, Alice H. D.; Yang, Jing; Gao, Jia-Hong; Siok, Wai Ting

    2011-01-01

    How second language (L2) learning is achieved in the human brain remains one of the fundamental questions of neuroscience and linguistics. Previous neuroimaging studies with bilinguals have consistently shown overlapping cortical organization of the native language (L1) and L2, leading to a prediction that a common neurobiological marker may be responsible for the development of the two languages. Here, by using functional MRI, we show that later skills to read in L2 are predicted by the activity level of the fusiform–caudate circuit in the left hemisphere, which nonetheless is not predictive of the ability to read in the native language. We scanned 10-y-old children while they performed a lexical decision task on L2 (and L1) stimuli. The subjects’ written language (reading) skills were behaviorally assessed twice, the first time just before we performed the fMRI scan (time 1 reading) and the second time 1 y later (time 2 reading). A whole-brain based analysis revealed that activity levels in left caudate and left fusiform gyrus correlated with L2 literacy skills at time 1. After controlling for the effects of time 1 reading and nonverbal IQ, or the effect of in-scanner lexical performance, the development in L2 literacy skills (time 2 reading) was also predicted by activity in left caudate and fusiform regions that are thought to mediate language control functions and resolve competition arising from L1 during L2 learning. Our findings suggest that the activity level of left caudate and fusiform regions serves as an important neurobiological marker for predicting accomplishment in reading skills in a new language. PMID:21262807

  20. Activity levels in the left hemisphere caudate-fusiform circuit predict how well a second language will be learned.

    PubMed

    Tan, Li Hai; Chen, Lin; Yip, Virginia; Chan, Alice H D; Yang, Jing; Gao, Jia-Hong; Siok, Wai Ting

    2011-02-01

    How second language (L2) learning is achieved in the human brain remains one of the fundamental questions of neuroscience and linguistics. Previous neuroimaging studies with bilinguals have consistently shown overlapping cortical organization of the native language (L1) and L2, leading to a prediction that a common neurobiological marker may be responsible for the development of the two languages. Here, by using functional MRI, we show that later skills to read in L2 are predicted by the activity level of the fusiform-caudate circuit in the left hemisphere, which nonetheless is not predictive of the ability to read in the native language. We scanned 10-y-old children while they performed a lexical decision task on L2 (and L1) stimuli. The subjects' written language (reading) skills were behaviorally assessed twice, the first time just before we performed the fMRI scan (time 1 reading) and the second time 1 y later (time 2 reading). A whole-brain based analysis revealed that activity levels in left caudate and left fusiform gyrus correlated with L2 literacy skills at time 1. After controlling for the effects of time 1 reading and nonverbal IQ, or the effect of in-scanner lexical performance, the development in L2 literacy skills (time 2 reading) was also predicted by activity in left caudate and fusiform regions that are thought to mediate language control functions and resolve competition arising from L1 during L2 learning. Our findings suggest that the activity level of left caudate and fusiform regions serves as an important neurobiological marker for predicting accomplishment in reading skills in a new language.

  1. A Simple and Efficient Computational Approach to Chafed Cable Time-Domain Reflectometry Signature Prediction

    NASA Technical Reports Server (NTRS)

    Kowalski, Marc Edward

    2009-01-01

    A method for the prediction of time-domain signatures of chafed coaxial cables is presented. The