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

PubMed Central

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

2015-01-01

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

Activation timing of postural muscles of lower legs and prediction of postural disturbance during bilateral arm flexion in older adults.

PubMed

Yaguchi, Chie; Fujiwara, Katsuo; Kiyota, Naoe

2017-12-22

Activation timings of postural muscles of lower legs and prediction of postural disturbance were investigated in young and older adults during bilateral arm flexion in a self-timing task and an oddball task with different probabilities of target presentation. Arm flexion was started from a standing posture with hands suspended 10 cm below the horizontal level in front of the body, in which postural control focused on the ankles is important. Fourteen young and 14 older adults raised the arms in response to the target sound signal. Three task conditions were used: 15 and 45% probabilities of the target in the oddball task and self-timing. Analysis items were activation timing of postural muscles (erector spinae, biceps femoris, and gastrocnemius) with respect to the anterior deltoid (AD), and latency and amplitude of the P300 component of event-related brain potential. For young adults, all postural muscles were activated significantly earlier than AD under each condition, and time of preceding gastrocnemius activation was significantly longer in the order of the self-timing, 45 and 15% conditions. P300 latency was significantly shorter, and P300 amplitude was significantly smaller under the 45% condition than under the 15% condition. For older adults, although all postural muscles, including gastrocnemius, were activated significantly earlier than AD in the self-timing condition, only activation timing of gastrocnemius was not significantly earlier than that of AD in oddball tasks, regardless of target probability. No significant differences were found between 15 and 45% conditions in onset times of all postural muscles, and latency and amplitude of P300. These results suggest that during arm movement, young adults can achieve sufficient postural preparation in proportion to the probability of target presentation in the oddball task. Older adults can achieve postural control using ankle joints in the self-timing task. However, in the oddball task, older adults

Prediction of calving time in dairy cattle.

PubMed

Mahmoud, Fadul; Christopher, Bogdahn; Maher, Alsaaod; Jürg, Hüsler; Alexander, Starke; Adrian, Steiner; Gaby, Hirsbrunner

2017-12-01

This prospective study was carried out to predict the calving time in primiparous (n=11) and multiparous (n=22) Holstein-Friesian cows using the combination of data obtained from the RumiWatch noseband-sensor and 3D-accelerometer. The animals included in the study were fitted with the RumiWatch noseband-sensor and 3D-accelerometer at least 10days before the expected calving day. The calving event was defined as the time of the first appearance of the calves' feet outside the vulva, and this moment was determined by farm staff and/or confirmed by video monitor. As primiparous and multiparous cows behaved differently, two models including data of noseband-sensors and 3D-accelerometers were used to predict the calving time in each group. Lying bouts (LB) increased and rumination chews (RC) decreased similarly in both groups; besides that, boluses (B) decreased and other activities (OA) increased significantly in multiparous and primiparous cows, respectively. The sensitivity (Se) and specificity (Sp) for prediction of the onset of calving within the next 3h were determined with the logistic regression and ROC analysis (Se=88.9%, 85% and Sp=93.3%, 74% for multiparous and primiparous cows, respectively). This pilot study revealed that the RumiWatch system is a useful tool to predict calving time under farm conditions. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

Ongoing activity in temporally coherent networks predicts intra-subject fluctuation of response time to sporadic executive control demands.

PubMed

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.

A novel time series link prediction method: Learning automata approach

NASA Astrophysics Data System (ADS)

Moradabadi, Behnaz; Meybodi, Mohammad Reza

2017-09-01

Link prediction is a main social network challenge that uses the network structure to predict future links. The common link prediction approaches to predict hidden links use a static graph representation where a snapshot of the network is analyzed to find hidden or future links. For example, similarity metric based link predictions are a common traditional approach that calculates the similarity metric for each non-connected link and sort the links based on their similarity metrics and label the links with higher similarity scores as the future links. Because people activities in social networks are dynamic and uncertainty, and the structure of the networks changes over time, using deterministic graphs for modeling and analysis of the social network may not be appropriate. In the time-series link prediction problem, the time series link occurrences are used to predict the future links In this paper, we propose a new time series link prediction based on learning automata. In the proposed algorithm for each link that must be predicted there is one learning automaton and each learning automaton tries to predict the existence or non-existence of the corresponding link. To predict the link occurrence in time T, there is a chain consists of stages 1 through T - 1 and the learning automaton passes from these stages to learn the existence or non-existence of the corresponding link. Our preliminary link prediction experiments with co-authorship and email networks have provided satisfactory results when time series link occurrences are considered.

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

PubMed Central

Henry, Molly J.; McAuley, J. Devin

2013-01-01

A number of accounts of human auditory perception assume that listeners use prior stimulus context to generate predictions about future stimulation. Here, we tested an auditory pitch-motion hypothesis that was developed from this perspective. Listeners judged either the time change (i.e., duration) or pitch change of a comparison frequency glide relative to a standard (referent) glide. Under a constant-velocity assumption, listeners were hypothesized to use the pitch velocity (Δf/Δt) of the standard glide to generate predictions about the pitch velocity of the comparison glide, leading to perceptual distortions along the to-be-judged dimension when the velocities of the two glides differed. These predictions were borne out in the pattern of relative points of subjective equality by a significant three-way interaction between the velocities of the two glides and task. In general, listeners’ judgments along the task-relevant dimension (pitch or time) were affected by expectations generated by the constant-velocity standard, but in an opposite manner for the two stimulus dimensions. When the comparison glide velocity was faster than the standard, listeners overestimated time change, but underestimated pitch change, whereas when the comparison glide velocity was slower than the standard, listeners underestimated time change, but overestimated pitch change. Perceptual distortions were least evident when the velocities of the standard and comparison glides were matched. Fits of an imputed velocity model further revealed increasingly larger distortions at faster velocities. The present findings provide support for the auditory pitch-motion hypothesis and add to a larger body of work revealing a role for active prediction in human auditory perception. PMID:23936462

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

PubMed

Henry, Molly J; McAuley, J Devin

2013-01-01

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

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

PubMed

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

2016-12-01

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

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

PubMed

Limongi, Roberto; Silva, Angélica M

2016-11-01

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

Adults' future time perspective predicts engagement in physical activity.

PubMed

Stahl, Sarah T; Patrick, Julie Hicks

2012-07-01

Our aim was to examine how the relations among known predictors of physical activity, such as age, sex, and body mass index, interact with future time perspective (FTP) and perceived functional limitation to explain adults' engagement in physical activity. Self-report data from 226 adults (range 20-88 years) were collected to examine the hypothesis that a more expansive FTP is associated with engagement in physical activity. Results indicated a good fit of the data to the model χ(2) (4, N = 226) = 7.457, p = .14 and accounted for a moderate amount of variance in adults' physical activity (R(2) = 15.7). Specifically, results indicated that perceived functional limitation (β = -.140) and FTP (β = .162) were directly associated with physical activity. Age was indirectly associated with physical activity through its association with perceived functional limitation (β = -.264) and FTP (β = .541). Results indicate that FTP may play an important role in explaining engagement in health promoting behaviors across the life span. Researchers should consider additional constructs and perhaps adopt socioemotional selectivity theory when explaining adults' engagement in physical activity.

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

PubMed Central

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

2016-01-01

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

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.

Predicting forest insect flight activity: A Bayesian network approach

PubMed Central

Pawson, Stephen M.; Marcot, Bruce G.; Woodberry, Owen G.

2017-01-01

Daily flight activity patterns of forest insects are influenced by temporal and meteorological conditions. Temperature and time of day are frequently cited as key drivers of activity; however, complex interactions between multiple contributing factors have also been proposed. Here, we report individual Bayesian network models to assess the probability of flight activity of three exotic insects, Hylurgus ligniperda, Hylastes ater, and Arhopalus ferus in a managed plantation forest context. Models were built from 7,144 individual hours of insect sampling, temperature, wind speed, relative humidity, photon flux density, and temporal data. Discretized meteorological and temporal variables were used to build naïve Bayes tree augmented networks. Calibration results suggested that the H. ater and A. ferus Bayesian network models had the best fit for low Type I and overall errors, and H. ligniperda had the best fit for low Type II errors. Maximum hourly temperature and time since sunrise had the largest influence on H. ligniperda flight activity predictions, whereas time of day and year had the greatest influence on H. ater and A. ferus activity. Type II model errors for the prediction of no flight activity is improved by increasing the model’s predictive threshold. Improvements in model performance can be made by further sampling, increasing the sensitivity of the flight intercept traps, and replicating sampling in other regions. Predicting insect flight informs an assessment of the potential phytosanitary risks of wood exports. Quantifying this risk allows mitigation treatments to be targeted to prevent the spread of invasive species via international trade pathways. PMID:28953904

Online communication predicts Belgian adolescents' initiation of romantic and sexual activity.

PubMed

Vandenbosch, Laura; Beyens, Ine; Vangeel, Laurens; Eggermont, Steven

2016-04-01

Online communication is associated with offline romantic and sexual activity among college students. Yet, it is unknown whether online communication is associated with the initiation of romantic and sexual activity among adolescents. This two-wave panel study investigated whether chatting, visiting dating websites, and visiting erotic contact websites predicted adolescents' initiation of romantic and sexual activity. We analyzed two-wave panel data from 1163 Belgian adolescents who participated in the MORES Study. We investigated the longitudinal impact of online communication on the initiation of romantic relationships and sexual intercourse using logistic regression analyses. The odds ratios of initiating a romantic relationship among romantically inexperienced adolescents who frequently used chat rooms, dating websites, or erotic contact websites were two to three times larger than those of non-users. Among sexually inexperienced adolescents who frequently used chat rooms, dating websites, or erotic contact websites, the odds ratios of initiating sexual intercourse were two to five times larger than that among non-users, even after a number of other relevant factors were introduced. This is the first study to demonstrate that online communication predicts the initiation of offline sexual and romantic activity as early as adolescence. Practitioners and parents need to consider the role of online communication in adolescents' developing sexuality. • Adolescents increasingly communicate online with peers. • Online communication predicts romantic and sexual activity among college students. What is New: • Online communication predicts adolescents' offline romantic activity over time. • Online communication predicts adolescents' offline sexual activity over time.

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

A continuous time-resolved measure decoded from EEG oscillatory activity predicts working memory task performance.

PubMed

Astrand, Elaine

2018-06-01

Working memory (WM), crucial for successful behavioral performance in most of our everyday activities, holds a central role in goal-directed behavior. As task demands increase, inducing higher WM load, maintaining successful behavioral performance requires the brain to work at the higher end of its capacity. Because it is depending on both external and internal factors, individual WM load likely varies in a continuous fashion. The feasibility to extract such a continuous measure in time that correlates to behavioral performance during a working memory task remains unsolved. Multivariate pattern decoding was used to test whether a decoder constructed from two discrete levels of WM load can generalize to produce a continuous measure that predicts task performance. Specifically, a linear regression with L2-regularization was chosen with input features from EEG oscillatory activity recorded from healthy participants while performing the n-back task, [Formula: see text]. The feasibility to extract a continuous time-resolved measure that correlates positively to trial-by-trial working memory task performance is demonstrated (r  =  0.47, p  <  0.05). It is furthermore shown that this measure allows to predict task performance before action (r  =  0.49, p  <  0.05). We show that the extracted continuous measure enables to study the temporal dynamics of the complex activation pattern of WM encoding during the n-back task. Specifically, temporally precise contributions of different spectral features are observed which extends previous findings of traditional univariate approaches. These results constitute an important contribution towards a wide range of applications in the field of cognitive brain-machine interfaces. Monitoring mental processes related to attention and WM load to reduce the risk of committing errors in high-risk environments could potentially prevent many devastating consequences or using the continuous measure as

A continuous time-resolved measure decoded from EEG oscillatory activity predicts working memory task performance

NASA Astrophysics Data System (ADS)

Astrand, Elaine

2018-06-01

Objective. Working memory (WM), crucial for successful behavioral performance in most of our everyday activities, holds a central role in goal-directed behavior. As task demands increase, inducing higher WM load, maintaining successful behavioral performance requires the brain to work at the higher end of its capacity. Because it is depending on both external and internal factors, individual WM load likely varies in a continuous fashion. The feasibility to extract such a continuous measure in time that correlates to behavioral performance during a working memory task remains unsolved. Approach. Multivariate pattern decoding was used to test whether a decoder constructed from two discrete levels of WM load can generalize to produce a continuous measure that predicts task performance. Specifically, a linear regression with L2-regularization was chosen with input features from EEG oscillatory activity recorded from healthy participants while performing the n-back task, n\\in [1,2] . Main results. The feasibility to extract a continuous time-resolved measure that correlates positively to trial-by-trial working memory task performance is demonstrated (r  =  0.47, p  <  0.05). It is furthermore shown that this measure allows to predict task performance before action (r  =  0.49, p  <  0.05). We show that the extracted continuous measure enables to study the temporal dynamics of the complex activation pattern of WM encoding during the n-back task. Specifically, temporally precise contributions of different spectral features are observed which extends previous findings of traditional univariate approaches. Significance. These results constitute an important contribution towards a wide range of applications in the field of cognitive brain–machine interfaces. Monitoring mental processes related to attention and WM load to reduce the risk of committing errors in high-risk environments could potentially prevent many devastating consequences or

Body-Related Shame and Guilt Predict Physical Activity in Breast Cancer Survivors Over Time.

PubMed

Castonguay, Andrée L; Wrosch, Carsten; Pila, Eva; Sabiston, Catherine M

2017-07-01

To test body-related shame and guilt as predictors of breast cancer survivors' (BCS') moderate to vigorous intensity physical activity (MVPA) during six months and to examine motivational regulations as mediators of this association.
. Prospective study.
. Survivors were recruited through advertisements and oncologist referrals from medical clinics and hospitals in Montreal, Quebec, Canada.
. 149 female BCS.
. Self-reports of body-related shame and guilt, motivational regulations, and MVPA were measured among BCS at baseline. MVPA was assessed a second time six months later. Residual change scores were used.
. Body-related shame and guilt; external, introjected, and autonomous (identified and intrinsic) motivational regulations; MVPA.
. In the multiple mediation models, body-related shame was associated with low levels of MVPA, as well as external, introjected, and autonomous motivational regulations. Guilt was related to high levels of MVPA and introjected and autonomous motivational regulations. Indirect effects linked shame, guilt, and MVPA via autonomous motivation. Only body-related shame was a significant predictor of six-month changes in MVPA.
. Based on these results, the specific emotions of shame and guilt contextualized to the body differentially predict BCS' health motivations and behavior over time.
. Survivorship programs may benefit from integrating intervention strategies aimed at reducing body-related shame and helping women manage feelings of guilt to improve physical activity.

Can We Predict Patient Wait Time?

PubMed

Pianykh, Oleg S; Rosenthal, Daniel I

2015-10-01

The importance of patient wait-time management and predictability can hardly be overestimated: For most hospitals, it is the patient queues that drive and define every bit of clinical workflow. The objective of this work was to study the predictability of patient wait time and identify its most influential predictors. To solve this problem, we developed a comprehensive list of 25 wait-related parameters, suggested in earlier work and observed in our own experiments. All parameters were chosen as derivable from a typical Hospital Information System dataset. The parameters were fed into several time-predicting models, and the best parameter subsets, discovered through exhaustive model search, were applied to a large sample of actual patient wait data. We were able to discover the most efficient wait-time prediction factors and models, such as the line-size models introduced in this work. Moreover, these models proved to be equally accurate and computationally efficient. Finally, the selected models were implemented in our patient waiting areas, displaying predicted wait times on the monitors located at the front desks. The limitations of these models are also discussed. Optimal regression models based on wait-line sizes can provide accurate and efficient predictions for patient wait time. Copyright © 2015 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Which domains of childhood physical activity predict physical activity in adulthood? A 20-year prospective tracking study.

PubMed

Cleland, Verity; Dwyer, Terence; Venn, Alison

2012-06-01

It is important to examine how childhood physical activity is related to adult physical activity in order to best tailor physical activity-promotion strategies. The time- and resource-intensive nature of studies spanning childhood into adulthood means the understanding of physical activity trajectories over this time span is limited. This study aimed to determine whether childhood domain-specific physical activities predict domain-specific physical activity 20 years later in adulthood, and whether age and sex play a role in these trajectories. In 1985, 6412 children of age 9-15 years self-reported frequency and duration of discretionary sport and exercise (leisure activity), transport activity, school sport and physical education (PE) in the past week and number of sports played in the past year. In 2004-2006, 2201 of these participants (aged 26-36 years) completed the long International Physical Activity Questionnaire and/or wore a Yamax pedometer. Analyses included partial correlation coefficients and log-binomial regression. Childhood and adult activity were weakly correlated (r=-0.08-0.14). Total weekly physical activity in childhood did not predict adult activity. School PE predicted adult total weekly physical activity and daily steps (older females), while school sport demonstrated inconsistent associations. Leisure and transport activity in childhood predicted adult leisure activity among younger males and older females, respectively. Childhood past year sport participation positively predicted adult physical activity (younger males and older females). Despite modest associations between childhood and adult physical activity that varied by domain, age and sex, promoting a range of physical activities to children of all ages is warranted.

Posture and activity recognition and energy expenditure prediction in a wearable platform.

PubMed

Sazonova, Nadezhda; Browning, Raymond; Melanson, Edward; Sazonov, Edward

2014-01-01

The use of wearable sensors coupled with the processing power of mobile phones may be an attractive way to provide real-time feedback about physical activity and energy expenditure (EE). Here we describe use of a shoe-based wearable sensor system (SmartShoe) with a mobile phone for real-time prediction and display of time spent in various postures/physical activities and the resulting EE. To deal with processing power and memory limitations of the phone, we introduce new algorithms that require substantially less computational power. The algorithms were validated using data from 15 subjects who performed up to 15 different activities of daily living during a four-hour stay in a room calorimeter. Use of Multinomial Logistic Discrimination (MLD) for posture and activity classification resulted in an accuracy comparable to that of Support Vector Machines (SVM) (90% vs. 95%-98%) while reducing the running time by a factor of 190 and reducing the memory requirement by a factor of 104. Per minute EE estimation using activity-specific models resulted in an accurate EE prediction (RMSE of 0.53 METs vs. RMSE of 0.69 METs using previously reported SVM-branched models). These results demonstrate successful implementation of real-time physical activity monitoring and EE prediction system on a wearable platform.

Predicting human activities in sequences of actions in RGB-D videos

NASA Astrophysics Data System (ADS)

Jardim, David; Nunes, Luís.; Dias, Miguel

2017-03-01

In our daily activities we perform prediction or anticipation when interacting with other humans or with objects. Prediction of human activity made by computers has several potential applications: surveillance systems, human computer interfaces, sports video analysis, human-robot-collaboration, games and health-care. We propose a system capable of recognizing and predicting human actions using supervised classifiers trained with automatically labeled data evaluated in our human activity RGB-D dataset (recorded with a Kinect sensor) and using only the position of the main skeleton joints to extract features. Using conditional random fields (CRFs) to model the sequential nature of actions in a sequence has been used before, but where other approaches try to predict an outcome or anticipate ahead in time (seconds), we try to predict what will be the next action of a subject. Our results show an activity prediction accuracy of 89.9% using an automatically labeled dataset.

The timing of associative memory formation: frontal lobe and anterior medial temporal lobe activity at associative binding predicts memory

PubMed Central

Hales, J. B.

2011-01-01

The process of associating items encountered over time and across variable time delays is fundamental for creating memories in daily life, such as for stories and episodes. Forming associative memory for temporally discontiguous items involves medial temporal lobe structures and additional neocortical processing regions, including prefrontal cortex, parietal lobe, and lateral occipital regions. However, most prior memory studies, using concurrently presented stimuli, have failed to examine the temporal aspect of successful associative memory formation to identify when activity in these brain regions is predictive of associative memory formation. In the current study, functional MRI data were acquired while subjects were shown pairs of sequentially presented visual images with a fixed interitem delay within pairs. This design allowed the entire time course of the trial to be analyzed, starting from onset of the first item, across the 5.5-s delay period, and through offset of the second item. Subjects then completed a postscan recognition test for the items and associations they encoded during the scan and their confidence for each. After controlling for item-memory strength, we isolated brain regions selectively involved in associative encoding. Consistent with prior findings, increased regional activity predicting subsequent associative memory success was found in anterior medial temporal lobe regions of left perirhinal and entorhinal cortices and in left prefrontal cortex and lateral occipital regions. The temporal separation within each pair, however, allowed extension of these findings by isolating the timing of regional involvement, showing that increased response in these regions occurs during binding but not during maintenance. PMID:21248058

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

PubMed

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

2014-01-01

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

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

Seasonal Extratropical Storm Activity Potential Predictability and its Origins during the Cold Seasons

NASA Astrophysics Data System (ADS)

Pingree-Shippee, K. A.; Zwiers, F. W.; Atkinson, D. E.

2016-12-01

Extratropical cyclones (ETCs) often produce extreme hazardous weather conditions, such as high winds, blizzard conditions, heavy precipitation, and flooding, all of which can have detrimental socio-economic impacts. The North American east and west coastal regions are both strongly influenced by ETCs and, subsequently, land-based, coastal, and maritime economic sectors in Canada and the USA all experience strong adverse impacts from extratropical storm activity from time to time. Society would benefit if risks associated with ETCs and storm activity variability could be reliably predicted for the upcoming season. Skillful prediction would enable affected sectors to better anticipate, prepare for, manage, and respond to storm activity variability and the associated risks and impacts. In this study, the potential predictability of seasonal variations in extratropical storm activity is investigated using analysis of variance to provide quantitative and geographical observational evidence indicative of whether it may be possible to predict storm activity on the seasonal timescale. This investigation will also identify origins of the potential predictability using composite analysis and large-scale teleconnections (Southern Oscillation, Pacific Decadal Oscillation, and North Atlantic Oscillation), providing the basis upon which seasonal predictions can be developed. Seasonal potential predictability and its origins are investigated for the cold seasons (OND, NDJ, DJF, JFM) during the 1979-2015 time period using daily mean sea level pressure, absolute pressure tendency, and 10-m wind speed from the ECMWF ERA-Interim reanalysis as proxies for extratropical storm activity. Results indicate potential predictability of seasonal variations in storm activity in areas strongly influenced by ETCs and with origins in the investigated teleconnections. For instance, the North Pacific storm track has considerable potential predictability and with notable origins in the SO and PDO.

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. Copyright © 2013 Elsevier B.V. All rights reserved.

Premotor neural correlates of predictive motor timing for speech production and hand movement: evidence for a temporal predictive code in the motor system.

PubMed

Johari, Karim; Behroozmand, Roozbeh

2017-05-01

The predictive coding model suggests that neural processing of sensory information is facilitated for temporally-predictable stimuli. This study investigated how temporal processing of visually-presented sensory cues modulates movement reaction time and neural activities in speech and hand motor systems. Event-related potentials (ERPs) were recorded in 13 subjects while they were visually-cued to prepare to produce a steady vocalization of a vowel sound or press a button in a randomized order, and to initiate the cued movement following the onset of a go signal on the screen. Experiment was conducted in two counterbalanced blocks in which the time interval between visual cue and go signal was temporally-predictable (fixed delay at 1000 ms) or unpredictable (variable between 1000 and 2000 ms). Results of the behavioral response analysis indicated that movement reaction time was significantly decreased for temporally-predictable stimuli in both speech and hand modalities. We identified premotor ERP activities with a left-lateralized parietal distribution for hand and a frontocentral distribution for speech that were significantly suppressed in response to temporally-predictable compared with unpredictable stimuli. The premotor ERPs were elicited approximately -100 ms before movement and were significantly correlated with speech and hand motor reaction times only in response to temporally-predictable stimuli. These findings suggest that the motor system establishes a predictive code to facilitate movement in response to temporally-predictable sensory stimuli. Our data suggest that the premotor ERP activities are robust neurophysiological biomarkers of such predictive coding mechanisms. These findings provide novel insights into the temporal processing mechanisms of speech and hand motor systems.

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

PubMed Central

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

2014-01-01

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

Physical activity behavior predicts endogenous pain modulation in older adults.

PubMed

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

2017-03-01

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

Asymmetric bagging and feature selection for activities prediction of drug molecules.

PubMed

Li, Guo-Zheng; Meng, Hao-Hua; Lu, Wen-Cong; Yang, Jack Y; Yang, Mary Qu

2008-05-28

Activities of drug molecules can be predicted by QSAR (quantitative structure activity relationship) models, which overcomes the disadvantages of high cost and long cycle by employing the traditional experimental method. With the fact that the number of drug molecules with positive activity is rather fewer than that of negatives, it is important to predict molecular activities considering such an unbalanced situation. Here, asymmetric bagging and feature selection are introduced into the problem and asymmetric bagging of support vector machines (asBagging) is proposed on predicting drug activities to treat the unbalanced problem. At the same time, the features extracted from the structures of drug molecules affect prediction accuracy of QSAR models. Therefore, a novel algorithm named PRIFEAB is proposed, which applies an embedded feature selection method to remove redundant and irrelevant features for asBagging. Numerical experimental results on a data set of molecular activities show that asBagging improve the AUC and sensitivity values of molecular activities and PRIFEAB with feature selection further helps to improve the prediction ability. Asymmetric bagging can help to improve prediction accuracy of activities of drug molecules, which can be furthermore improved by performing feature selection to select relevant features from the drug molecules data sets.

Prediction of time-integrated activity coefficients in PRRT using simulated dynamic PET and a pharmacokinetic model.

PubMed

Hardiansyah, Deni; Attarwala, Ali Asgar; Kletting, Peter; Mottaghy, Felix M; Glatting, Gerhard

2017-10-01

To investigate the accuracy of predicted time-integrated activity coefficients (TIACs) in peptide-receptor radionuclide therapy (PRRT) using simulated dynamic PET data and a physiologically based pharmacokinetic (PBPK) model. PBPK parameters were estimated using biokinetic data of 15 patients after injection of (152±15)MBq of 111 In-DTPAOC (total peptide amount (5.78±0.25)nmol). True mathematical phantoms of patients (MPPs) were the PBPK model with the estimated parameters. Dynamic PET measurements were simulated as being done after bolus injection of 150MBq 68 Ga-DOTATATE using the true MPPs. Dynamic PET scans around 35min p.i. (P 1 ), 4h p.i. (P 2 ) and the combination of P 1 and P 2 (P 3 ) were simulated. Each measurement was simulated with four frames of 5min each and 2 bed positions. PBPK parameters were fitted to the PET data to derive the PET-predicted MPPs. Therapy was simulated assuming an infusion of 5.1GBq of 90 Y-DOTATATE over 30min in both true and PET-predicted MPPs. TIACs of simulated therapy were calculated, true MPPs (true TIACs) and predicted MPPs (predicted TIACs) followed by the calculation of variabilities v. For P 1 and P 2 the population variabilities of kidneys, liver and spleen were acceptable (v<10%). For the tumours and the remainders, the values were large (up to 25%). For P 3 , population variabilities for all organs including the remainder further improved, except that of the tumour (v>10%). Treatment planning of PRRT based on dynamic PET data seems possible for the kidneys, liver and spleen using a PBPK model and patient specific information. Copyright © 2017 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Gender differences in leisure-time versus non-leisure-time physical activity among Saudi adolescents.

PubMed

Al-Sobayel, Hana; Al-Hazzaa, Hazzaa M; Abahussain, Nanda A; Qahwaji, Dina M; Musaiger, Abdulrahman O

2015-01-01

The aim of the study was to examine the gender differences and predictors of leisure versus non-leisure time physical activities among Saudi adolescents aged 14-19 years. The multistage stratified cluster random sampling technique was used. A sample of 1,388 males and 1,500 females enrolled in secondary schools in three major cities in Saudi Arabia was included. Anthropometric measurements were performed and Body Mass Index was calculated. Physical activity, sedentary behaviours and dietary habits were measured using a self-reported validated questionnaire. The total time spent in leisure and non-leisure physical activity per week was 90 and 77 minutes, respectively. The males spent more time per week in leisure-time physical activities than females. Females in private schools spent more time during the week in leisure-time physical activities, compared to females in Stateschools. There was a significant difference between genders by obesity status interaction in leisure-time physical activity. Gender, and other factors, predicted total duration spent in leisure-time and non-leisure-time physical activity. The study showed that female adolescents are much less active than males, especially in leisure-time physical activities. Programmes to promote physical activity among adolescents are urgently needed, with consideration of gender differences.

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

PubMed

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

2014-11-26

Normalization is a widespread neural computation, mediating divisive gain control in sensory processing and implementing a context-dependent value code in decision-related frontal and parietal cortices. Although decision-making is a dynamic process with complex temporal characteristics, most models of normalization are time-independent and little is known about the dynamic interaction of normalization and choice. Here, we show that a simple differential equation model of normalization explains the characteristic phasic-sustained pattern of cortical decision activity and predicts specific normalization dynamics: value coding during initial transients, time-varying value modulation, and delayed onset of contextual information. Empirically, we observe these predicted dynamics in saccade-related neurons in monkey lateral intraparietal cortex. Furthermore, such models naturally incorporate a time-weighted average of past activity, implementing an intrinsic reference-dependence in value coding. These results suggest that a single network mechanism can explain both transient and sustained decision activity, emphasizing the importance of a dynamic view of normalization in neural coding. Copyright © 2014 the authors 0270-6474/14/3416046-12$15.00/0.

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.

Long-time predictions in nonlinear dynamics

NASA Technical Reports Server (NTRS)

Szebehely, V.

1980-01-01

It is known that nonintegrable dynamical systems do not allow precise predictions concerning their behavior for arbitrary long times. The available series solutions are not uniformly convergent according to Poincare's theorem and numerical integrations lose their meaningfulness after the elapse of arbitrary long times. Two approaches are the use of existing global integrals and statistical methods. This paper presents a generalized method along the first approach. As examples long-time predictions in the classical gravitational satellite and planetary problems are treated.

Prediction of energy expenditure and physical activity in preschoolers

USDA-ARS?s Scientific Manuscript database

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) ...

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.

Flight Tests of a Remaining Flying Time Prediction System for Small Electric Aircraft in the Presence of Faults

NASA Technical Reports Server (NTRS)

Hogge, Edward F.; Kulkarni, Chetan S.; Vazquez, Sixto L.; Smalling, Kyle M.; Strom, Thomas H.; Hill, Boyd L.; Quach, Cuong C.

2017-01-01

This paper addresses the problem of building trust in the online prediction of a battery powered aircraft's remaining flying time. A series of flight tests is described that make use of a small electric powered unmanned aerial vehicle (eUAV) to verify the performance of the remaining flying time prediction algorithm. The estimate of remaining flying time is used to activate an alarm when the predicted remaining time is two minutes. This notifies the pilot to transition to the landing phase of the flight. A second alarm is activated when the battery charge falls below a specified limit threshold. This threshold is the point at which the battery energy reserve would no longer safely support two repeated aborted landing attempts. During the test series, the motor system is operated with the same predefined timed airspeed profile for each test. To test the robustness of the prediction, half of the tests were performed with, and half were performed without, a simulated powertrain fault. The pilot remotely engages a resistor bank at a specified time during the test flight to simulate a partial powertrain fault. The flying time prediction system is agnostic of the pilot's activation of the fault and must adapt to the vehicle's state. The time at which the limit threshold on battery charge is reached is then used to measure the accuracy of the remaining flying time predictions. Accuracy requirements for the alarms are considered and the results discussed.

Adolescents' technology and face-to-face time use predict objective sleep outcomes.

PubMed

Tavernier, Royette; Heissel, Jennifer A; Sladek, Michael R; Grant, Kathryn E; Adam, Emma K

2017-08-01

The present study examined both within- and between-person associations between adolescents' time use (technology-based activities and face-to-face interactions with friends and family) and sleep behaviors. We also assessed whether age moderated associations between adolescents' time use with friends and family and sleep. Adolescents wore an actigraph monitor and completed brief evening surveys daily for 3 consecutive days. Adolescents (N=71; mean age=14.50 years old, SD=1.84; 43.7% female) were recruited from 3 public high schools in the Midwest. We assessed 8 technology-based activities (eg, texting, working on a computer), as well as time spent engaged in face-to-face interactions with friends and family, via questions on adolescents' evening surveys. Actigraph monitors assessed 3 sleep behaviors: sleep latency, sleep hours, and sleep efficiency. Hierarchical linear models indicated that texting and working on the computer were associated with shorter sleep, whereas time spent talking on the phone predicted longer sleep. Time spent with friends predicted shorter sleep latencies, while family time predicted longer sleep latencies. Age moderated the association between time spent with friends and sleep efficiency, as well as between family time and sleep efficiency. Specifically, longer time spent interacting with friends was associated with higher sleep efficiency but only among younger adolescents. Furthermore, longer family time was associated with higher sleep efficiency but only for older adolescents. Findings are discussed in terms of the importance of regulating adolescents' technology use and improving opportunities for face-to-face interactions with friends, particularly for younger adolescents. Copyright © 2017 National Sleep Foundation. Published by Elsevier Inc. All rights reserved.

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.

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

Scale development for measuring and predicting adolescents' leisure time physical activity behavior.

PubMed

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 pointsWhen using the structured alternative format, weak internal consistency was obtained

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

PubMed

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

2005-09-01

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

Space-Time Earthquake Prediction: The Error Diagrams

NASA Astrophysics Data System (ADS)

Molchan, G.

2010-08-01

The quality of earthquake prediction is usually characterized by a two-dimensional diagram n versus τ, where n is the rate of failures-to-predict and τ is a characteristic of space-time alarm. Unlike the time prediction case, the quantity τ is not defined uniquely. We start from the case in which τ is a vector with components related to the local alarm times and find a simple structure of the space-time diagram in terms of local time diagrams. This key result is used to analyze the usual 2-d error sets { n, τ w } in which τ w is a weighted mean of the τ components and w is the weight vector. We suggest a simple algorithm to find the ( n, τ w ) representation of all random guess strategies, the set D, and prove that there exists the unique case of w when D degenerates to the diagonal n + τ w = 1. We find also a confidence zone of D on the ( n, τ w ) plane when the local target rates are known roughly. These facts are important for correct interpretation of ( n, τ w ) diagrams when we discuss the prediction capability of the data or prediction methods.

Do unfavourable alcohol, smoking, nutrition and physical activity predict sustained leisure time sedentary behaviour? A population-based cohort study.

PubMed

Nooijen, Carla F J; Möller, Jette; Forsell, Yvonne; Ekblom, Maria; Galanti, Maria R; Engström, Karin

2017-08-01

Comparing lifestyle of people remaining sedentary during longer periods of their life with those favourably changing their behaviour can provide cues to optimize interventions targeting sedentary behaviour. The objective of this study was to determine lifestyle predictors of sustained leisure time sedentary behaviour and assess whether these predictors were dependent on gender, age, socioeconomic position and occupational sedentary behaviour. Data from a large longitudinal population-based cohort of adults (aged 18-97years) in Stockholm responding to public health surveys in 2010 and 2014 were analysed (n=49,133). Leisure time sedentary behaviour was defined as >3h per day of leisure sitting time e.g. watching TV, reading or using tablet. Individuals classified as sedentary at baseline (n=9562) were subsequently categorized as remaining sedentary (n=6357) or reduced sedentary behaviour (n=3205) at follow-up. Lifestyle predictors were unfavourable alcohol consumption, smoking, nutrition, and physical activity. Odds ratios (OR) and corresponding 95% Confidence Intervals (CI) were calculated, adjusting for potential confounders. Unfavourable alcohol consumption (OR=1.22, CI:1.11-1.34), unfavourable candy- or cake consumption (OR=1.15, CI:1.05-1.25), and unfavourable physical activity in different contexts were found to predict sustained sedentary behaviour, with negligible differences according to gender, age, socioeconomic position and occupational sedentary behaviour. People with unfavourable lifestyle profiles regarding alcohol, sweets, or physical activity are more likely to remain sedentary compared to sedentary persons with healthier lifestyle. The impact of combining interventions to reduce leisure time sedentary behaviour with reducing alcohol drinking, sweet consumption and increasing physical activity should be tested as a promising strategy for behavioural modification. Copyright © 2017 Elsevier Inc. All rights reserved.

Real-time flutter boundary prediction based on time series models

NASA Astrophysics Data System (ADS)

Gu, Wenjing; Zhou, Li

2018-03-01

For the purpose of predicting the flutter boundary in real time during flutter flight tests, two time series models accompanied with corresponding stability criterion are adopted in this paper. The first method simplifies a long nonstationary response signal as many contiguous intervals and each is considered to be stationary. The traditional AR model is then established to represent each interval of signal sequence. While the second employs a time-varying AR model to characterize actual measured signals in flutter test with progression variable speed (FTPVS). To predict the flutter boundary, stability parameters are formulated by the identified AR coefficients combined with Jury's stability criterion. The behavior of the parameters is examined using both simulated and wind-tunnel experiment data. The results demonstrate that both methods show significant effectiveness in predicting the flutter boundary at lower speed level. A comparison between the two methods is also given in this paper.

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.

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.

Time evolution of predictability of epidemics on networks.

PubMed

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.

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.

Hippocampus activation related to 'real-time' processing of visuospatial change.

PubMed

Beudel, M; Leenders, K L; de Jong, B M

2016-12-01

The delay associated with cerebral processing time implies a lack of real-time representation of changes in the observed environment. To bridge this gap for motor actions in a dynamical environment, the brain uses predictions of the most plausible future reality based on previously provided information. To optimise these predictions, adjustments to actual experiences are necessary. This requires a perceptual memory buffer. In our study we gained more insight how the brain treats (real-time) information by comparing cerebral activations related to judging past-, present- and future locations of a moving ball, respectively. Eighteen healthy subjects made these estimations while fMRI data was obtained. All three conditions evoked bilateral dorsal-parietal and premotor activations, while judgment of the location of the ball at the moment of judgment showed increased bilateral posterior hippocampus activation relative to making both future and past judgments at the one-second time-sale. Since the condition of such 'real-time' judgments implied undistracted observation of the ball's actual movements, the associated hippocampal activation is consistent with the concept that the hippocampus participates in a top-down exerted sensory gating mechanism. In this way, it may play a role in novelty (saliency) detection. Copyright © 2016 Elsevier B.V. All rights reserved.

Prediction of objectively measured physical activity and sedentariness among blue-collar workers using survey questionnaires.

PubMed

Gupta, Nidhi; Heiden, Marina; Mathiassen, Svend Erik; Holtermann, Andreas

2016-05-01

We aimed at developing and evaluating statistical models predicting objectively measured occupational time spent sedentary or in physical activity from self-reported information available in large epidemiological studies and surveys. Two-hundred-and-fourteen blue-collar workers responded to a questionnaire containing information about personal and work related variables, available in most large epidemiological studies and surveys. Workers also wore accelerometers for 1-4 days measuring time spent sedentary and in physical activity, defined as non-sedentary time. Least-squares linear regression models were developed, predicting objectively measured exposures from selected predictors in the questionnaire. A full prediction model based on age, gender, body mass index, job group, self-reported occupational physical activity (OPA), and self-reported occupational sedentary time (OST) explained 63% (R (2)adjusted) of the variance of both objectively measured time spent sedentary and in physical activity since these two exposures were complementary. Single-predictor models based only on self-reported information about either OPA or OST explained 21% and 38%, respectively, of the variance of the objectively measured exposures. Internal validation using bootstrapping suggested that the full and single-predictor models would show almost the same performance in new datasets as in that used for modelling. Both full and single-predictor models based on self-reported information typically available in most large epidemiological studies and surveys were able to predict objectively measured occupational time spent sedentary or in physical activity, with explained variances ranging from 21-63%.

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

Predictable and Adaptable Complex Real-Time Systems

DTIC Science & Technology

1993-09-30

Predictable and Adaptable Complex Real - Time Systems Grant or Contract Number: N00014-92-J-1048 Reporting Period: 1 Oct 91 - 30 Sep 93 1... Real - Time Systems Grant or Contract Number: N00014-92-J-1048 Reporting Period: 1 Oct 91 - 30 Sep 93 2. Summary of Technical Progress Our...cs.umass.edu Grant or Contract Title: Predictable and Adaptable Complex Real - Time Systems Grant or Contract Number: N00014-92-J-1048 Reporting Period: 1 Oct 91

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.

Gamma-band activation predicts both associative memory and cortical plasticity

PubMed Central

Headley, Drew B.; Weinberger, Norman M.

2011-01-01

Gamma-band oscillations are a ubiquitous phenomenon in the nervous system and have been implicated in multiple aspects of cognition. In particular, the strength of gamma oscillations at the time a stimulus is encoded predicts its subsequent retrieval, suggesting that gamma may reflect enhanced mnemonic processing. Likewise, activity in the gamma-band can modulate plasticity in vitro. However, it is unclear whether experience-dependent plasticity in vivo is also related to gamma-band activation. The aim of the present study is to determine whether gamma activation in primary auditory cortex modulates both the associative memory for an auditory stimulus during classical conditioning and its accompanying specific receptive field plasticity. Rats received multiple daily sessions of single tone/shock trace and two-tone discrimination conditioning, during which local field potentials and multiunit discharges were recorded from chronically implanted electrodes. We found that the strength of tone-induced gamma predicted the acquisition of associative memory 24 h later, and ceased to predict subsequent performance once asymptote was reached. Gamma activation also predicted receptive field plasticity that specifically enhanced representation of the signal tone. This concordance provides a long-sought link between gamma oscillations, cortical plasticity and the formation of new memories. PMID:21900554

Physical activity, but not sedentary time, influences bone strength in late adolescence.

PubMed

Tan, Vina Ps; Macdonald, Heather M; Gabel, Leigh; McKay, Heather A

2018-03-20

Physical activity is essential for optimal bone strength accrual, but we know little about interactions between physical activity, sedentary time, and bone outcomes in older adolescents. Physical activity (by accelerometer and self-report) positively predicted bone strength and the distal and midshaft tibia in 15-year-old boys and girls. Lean body mass mediated the relationship between physical activity and bone strength in adolescents. To examine the influence of physical activity (PA) and sedentary time on bone strength, structure, and density in older adolescents. We used peripheral quantitative computed tomography to estimate bone strength at the distal tibia (8% site; bone strength index, BSI) and tibial midshaft (50% site; polar strength strain index, SSI p ) in adolescent boys (n = 86; 15.3 ± 0.4 years) and girls (n = 106; 15.3 ± 0.4 years). Using accelerometers (GT1M, Actigraph), we measured moderate-to-vigorous PA (MVPA Accel ), vigorous PA (VPA Accel ), and sedentary time in addition to self-reported MVPA (MVPA PAQ-A ) and impact PA (ImpactPA PAQ-A ). We examined relations between PA and sedentary time and bone outcomes, adjusting for ethnicity, maturity, tibial length, and total body lean mass. At the distal tibia, MVPA Accel and VPA Accel positively predicted BSI (explained 6-7% of the variance, p < 0.05). After adjusting for lean mass, only VPA Accel explained residual variance in BSI. At the tibial midshaft, MVPA Accel , but not VPA Accel , positively predicted SSI p (explained 3% of the variance, p = 0.01). Lean mass attenuated this association. MVPA PAQ-A and ImpactPA PAQ-A also positively predicted BSI and SSI p (explained 2-4% of the variance, p < 0.05), but only ImpactPA PAQ-A explained residual variance in BSI after accounting for lean mass. Sedentary time did not independently predict bone strength at either site. Greater tibial bone strength in active adolescents is mediated, in part, by lean mass. Despite

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

Recent and past musical activity predicts cognitive aging variability: direct comparison with general lifestyle activities.

PubMed

Hanna-Pladdy, Brenda; Gajewski, Byron

2012-01-01

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

Predicting risky choices from brain activity patterns

PubMed Central

Helfinstein, Sarah M.; Schonberg, Tom; Congdon, Eliza; Karlsgodt, Katherine H.; Mumford, Jeanette A.; Sabb, Fred W.; Cannon, Tyrone D.; London, Edythe D.; Bilder, Robert M.; Poldrack, Russell A.

2014-01-01

Previous research has implicated a large network of brain regions in the processing of risk during decision making. However, it has not yet been determined if activity in these regions is predictive of choices on future risky decisions. Here, we examined functional MRI data from a large sample of healthy subjects performing a naturalistic risk-taking task and used a classification analysis approach to predict whether individuals would choose risky or safe options on upcoming trials. We were able to predict choice category successfully in 71.8% of cases. Searchlight analysis revealed a network of brain regions where activity patterns were reliably predictive of subsequent risk-taking behavior, including a number of regions known to play a role in control processes. Searchlights with significant predictive accuracy were primarily located in regions more active when preparing to avoid a risk than when preparing to engage in one, suggesting that risk taking may be due, in part, to a failure of the control systems necessary to initiate a safe choice. Additional analyses revealed that subject choice can be successfully predicted with minimal decrements in accuracy using highly condensed data, suggesting that information relevant for risky choice behavior is encoded in coarse global patterns of activation as well as within highly local activation within searchlights. PMID:24550270

Cognitive emotion regulation enhances aversive prediction error activity while reducing emotional responses.

PubMed

Mulej Bratec, Satja; Xie, Xiyao; Schmid, Gabriele; Doll, Anselm; Schilbach, Leonhard; Zimmer, Claus; Wohlschläger, Afra; Riedl, Valentin; Sorg, Christian

2015-12-01

Cognitive emotion regulation is a powerful way of modulating emotional responses. However, despite the vital role of emotions in learning, it is unknown whether the effect of cognitive emotion regulation also extends to the modulation of learning. Computational models indicate prediction error activity, typically observed in the striatum and ventral tegmental area, as a critical neural mechanism involved in associative learning. We used model-based fMRI during aversive conditioning with and without cognitive emotion regulation to test the hypothesis that emotion regulation would affect prediction error-related neural activity in the striatum and ventral tegmental area, reflecting an emotion regulation-related modulation of learning. Our results show that cognitive emotion regulation reduced emotion-related brain activity, but increased prediction error-related activity in a network involving ventral tegmental area, hippocampus, insula and ventral striatum. While the reduction of response activity was related to behavioral measures of emotion regulation success, the enhancement of prediction error-related neural activity was related to learning performance. Furthermore, functional connectivity between the ventral tegmental area and ventrolateral prefrontal cortex, an area involved in regulation, was specifically increased during emotion regulation and likewise related to learning performance. Our data, therefore, provide first-time evidence that beyond reducing emotional responses, cognitive emotion regulation affects learning by enhancing prediction error-related activity, potentially via tegmental dopaminergic pathways. Copyright © 2015 Elsevier Inc. All rights reserved.

Drift in Neural Population Activity Causes Working Memory to Deteriorate Over Time.

PubMed

Schneegans, Sebastian; Bays, Paul M

2018-05-23

Short-term memories are thought to be maintained in the form of sustained spiking activity in neural populations. Decreases in recall precision observed with increasing number of memorized items can be accounted for by a limit on total spiking activity, resulting in fewer spikes contributing to the representation of each individual item. Longer retention intervals likewise reduce recall precision, but it is unknown what changes in population activity produce this effect. One possibility is that spiking activity becomes attenuated over time, such that the same mechanism accounts for both effects of set size and retention duration. Alternatively, reduced performance may be caused by drift in the encoded value over time, without a decrease in overall spiking activity. Human participants of either sex performed a variable-delay cued recall task with a saccadic response, providing a precise measure of recall latency. Based on a spike integration model of decision making, if the effects of set size and retention duration are both caused by decreased spiking activity, we would predict a fixed relationship between recall precision and response latency across conditions. In contrast, the drift hypothesis predicts no systematic changes in latency with increasing delays. Our results show both an increase in latency with set size, and a decrease in response precision with longer delays within each set size, but no systematic increase in latency for increasing delay durations. These results were quantitatively reproduced by a model based on a limited neural resource in which working memories drift rather than decay with time. SIGNIFICANCE STATEMENT Rapid deterioration over seconds is a defining feature of short-term memory, but what mechanism drives this degradation of internal representations? Here, we extend a successful population coding model of working memory by introducing possible mechanisms of delay effects. We show that a decay in neural signal over time predicts that

Leisure-Time Physical Activity and Academic Performance: Cross-Lagged Associations from Adolescence to Young Adulthood

PubMed Central

Aaltonen, Sari; Latvala, Antti; Rose, Richard J.; Kujala, Urho M.; Kaprio, Jaakko; Silventoinen, Karri

2016-01-01

Physical activity and academic performance are positively associated, but the direction of the association is poorly understood. This longitudinal study examined the direction and magnitude of the associations between leisure-time physical activity and academic performance throughout adolescence and young adulthood. The participants were Finnish twins (from 2,859 to 4,190 individuals/study wave) and their families. In a cross-lagged path model, higher academic performance at ages 12, 14 and 17 predicted higher leisure-time physical activity at subsequent time-points (standardized path coefficient at age 14: 0.07 (p < 0.001), age 17: 0.12 (p < 0.001) and age 24: 0.06 (p < 0.05)), whereas physical activity did not predict future academic performance. A cross-lagged model of co-twin differences suggested that academic performance and subsequent physical activity were not associated due to the environmental factors shared by co-twins. Our findings suggest that better academic performance in adolescence modestly predicts more frequent leisure-time physical activity in late adolescence and young adulthood. PMID:27976699

Leisure-Time Physical Activity and Academic Performance: Cross-Lagged Associations from Adolescence to Young Adulthood.

PubMed

Aaltonen, Sari; Latvala, Antti; Rose, Richard J; Kujala, Urho M; Kaprio, Jaakko; Silventoinen, Karri

2016-12-15

Physical activity and academic performance are positively associated, but the direction of the association is poorly understood. This longitudinal study examined the direction and magnitude of the associations between leisure-time physical activity and academic performance throughout adolescence and young adulthood. The participants were Finnish twins (from 2,859 to 4,190 individuals/study wave) and their families. In a cross-lagged path model, higher academic performance at ages 12, 14 and 17 predicted higher leisure-time physical activity at subsequent time-points (standardized path coefficient at age 14: 0.07 (p < 0.001), age 17: 0.12 (p < 0.001) and age 24: 0.06 (p < 0.05)), whereas physical activity did not predict future academic performance. A cross-lagged model of co-twin differences suggested that academic performance and subsequent physical activity were not associated due to the environmental factors shared by co-twins. Our findings suggest that better academic performance in adolescence modestly predicts more frequent leisure-time physical activity in late adolescence and young adulthood.

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

PubMed

Ben-Yakov, Aya; Dudai, Yadin

2011-06-15

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

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

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. Copyright © 2015 Elsevier Inc. All rights reserved.

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.

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. Crown Copyright © 2012. Published by Elsevier Ltd. All rights reserved.

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

Predictive Mining of Time Series Data

NASA Astrophysics Data System (ADS)

Java, A.; Perlman, E. S.

2002-05-01

All-sky monitors are a relatively new development in astronomy, and their data represent a largely untapped resource. Proper utilization of this resource could lead to important discoveries not only in the physics of variable objects, but in how one observes such objects. We discuss the development of a Java toolbox for astronomical time series data. Rather than using methods conventional in astronomy (e.g., power spectrum and cross-correlation analysis) we employ rule discovery techniques commonly used in analyzing stock-market data. By clustering patterns found within the data, rule discovery allows one to build predictive models, allowing one to forecast when a given event might occur or whether the occurrence of one event will trigger a second. We have tested the toolbox and accompanying display tool on datasets (representing several classes of objects) from the RXTE All Sky Monitor. We use these datasets to illustrate the methods and functionality of the toolbox. We have found predictive patterns in several ASM datasets. We also discuss problems faced in the development process, particularly the difficulties of dealing with discretized and irregularly sampled data. A possible application would be in scheduling target of opportunity observations where the astronomer wants to observe an object when a certain event or series of events occurs. By combining such a toolbox with an automatic, Java query tool which regularly gathers data on objects of interest, the astronomer or telescope operator could use the real-time datastream to efficiently predict the occurrence of (for example) a flare or other event. By combining the toolbox with dynamic time warping data-mining tools, one could predict events which may happen on variable time scales.

The Predictive Brain State: Timing Deficiency in Traumatic Brain Injury?

PubMed Central

Ghajar, Jamshid; Ivry, Richard B.

2015-01-01

Attention and memory deficits observed in traumatic brain injury (TBI) are postulated to result from the shearing of white matter connections between the prefrontal cortex, parietal lobe, and cerebellum that are critical in the generation, maintenance, and precise timing of anticipatory neural activity. These fiber tracts are part of a neural network that generates predictions of future states and events, processes that are required for optimal performance on attention and working memory tasks. The authors discuss the role of this anticipatory neural system for understanding the varied symptoms and potential rehabilitation interventions for TBI. Preparatory neural activity normally allows the efficient integration of sensory information with goal-based representations. It is postulated that an impairment in the generation of this activity in traumatic brain injury (TBI) leads to performance variability as the brain shifts from a predictive to reactive mode. This dysfunction may constitute a fundamental defect in TBI as well as other attention disorders, causing working memory deficits, distractibility, a loss of goal-oriented behavior, and decreased awareness. “The future is not what is coming to meet us, but what we are moving forward to meet.” —Jean-Marie Guyau1 PMID:18460693

Nonstationary time series prediction combined with slow feature analysis

NASA Astrophysics Data System (ADS)

Wang, G.; Chen, X.

2015-07-01

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

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

Predicted Biological Activity of Purchasable Chemical Space

PubMed Central

2017-01-01

Whereas 400 million distinct compounds are now purchasable within the span of a few weeks, the biological activities of most are unknown. To facilitate access to new chemistry for biology, we have combined the Similarity Ensemble Approach (SEA) with the maximum Tanimoto similarity to the nearest bioactive to predict activity for every commercially available molecule in ZINC. This method, which we label SEA+TC, outperforms both SEA and a naïve-Bayesian classifier via predictive performance on a 5-fold cross-validation of ChEMBL’s bioactivity data set (version 21). Using this method, predictions for over 40% of compounds (>160 million) have either high significance (pSEA ≥ 40), high similarity (ECFP4MaxTc ≥ 0.4), or both, for one or more of 1382 targets well described by ligands in the literature. Using a further 1347 less-well-described targets, we predict activities for an additional 11 million compounds. To gauge whether these predictions are sensible, we investigate 75 predictions for 50 drugs lacking a binding affinity annotation in ChEMBL. The 535 million predictions for over 171 million compounds at 2629 targets are linked to purchasing information and evidence to support each prediction and are freely available via https://zinc15.docking.org and https://files.docking.org. PMID:29193970

Stated Uptake of Physical Activity Rewards Programmes Among Active and Insufficiently Active Full-Time Employees.

PubMed

Ozdemir, Semra; Bilger, Marcel; Finkelstein, Eric A

2017-10-01

Employers are increasingly relying on rewards programmes in an effort to promote greater levels of activity among employees; however, if enrolment in these programmes is dominated by active employees, then they are unlikely to be a good use of resources. This study uses a stated-preference survey to better understand who participates in rewards-based physical activity programmes, and to quantify stated uptake by active and insufficiently active employees. The survey was fielded to a national sample of 950 full-time employees in Singapore between 2012 and 2013. Participants were asked to choose between hypothetical rewards programmes that varied along key dimensions and whether or not they would join their preferred programme if given the opportunity. A mixed logit model was used to analyse the data and estimate predicted uptake for specific programmes. We then simulated employer payments based on predictions for the percentage of each type of employee likely to meet the activity goal. Stated uptake ranged from 31 to 67% of employees, depending on programme features. For each programme, approximately two-thirds of those likely to enrol were insufficiently active. Results showed that insufficiently active employees, who represent the majority, are attracted to rewards-based physical activity programmes, and at approximately the same rate as active employees, even when enrolment fees are required. This suggests that a programme with generous rewards and a modest enrolment fee may have strong employee support and be within the range of what employers may be willing to spend.

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.

Nonstationary time series prediction combined with slow feature analysis

NASA Astrophysics Data System (ADS)

Wang, G.; Chen, X.

2015-01-01

Almost all climate time series have some degree of nonstationarity due to external driving forces perturbations of the observed system. Therefore, these external driving forces should be taken into account when reconstructing the climate dynamics. This paper presents a new technique of combining the driving force of a time series obtained using the Slow Feature Analysis (SFA) approach, then introducing the driving force into a predictive model to predict non-stationary time series. In essence, the main idea of the technique is to consider the driving forces as state variables and incorporate them into the prediction model. To test the method, experiments using a modified logistic time series and winter ozone data in Arosa, Switzerland, were conducted. The results showed improved and effective prediction skill.

Improved Short-Term Clock Prediction Method for Real-Time Positioning.

PubMed

Lv, Yifei; Dai, Zhiqiang; Zhao, Qile; Yang, Sheng; Zhou, Jinning; Liu, Jingnan

2017-06-06

The application of real-time precise point positioning (PPP) requires real-time precise orbit and clock products that should be predicted within a short time to compensate for the communication delay or data gap. Unlike orbit correction, clock correction is difficult to model and predict. The widely used linear model hardly fits long periodic trends with a small data set and exhibits significant accuracy degradation in real-time prediction when a large data set is used. This study proposes a new prediction model for maintaining short-term satellite clocks to meet the high-precision requirements of real-time clocks and provide clock extrapolation without interrupting the real-time data stream. Fast Fourier transform (FFT) is used to analyze the linear prediction residuals of real-time clocks. The periodic terms obtained through FFT are adopted in the sliding window prediction to achieve a significant improvement in short-term prediction accuracy. This study also analyzes and compares the accuracy of short-term forecasts (less than 3 h) by using different length observations. Experimental results obtained from International GNSS Service (IGS) final products and our own real-time clocks show that the 3-h prediction accuracy is better than 0.85 ns. The new model can replace IGS ultra-rapid products in the application of real-time PPP. It is also found that there is a positive correlation between the prediction accuracy and the short-term stability of on-board clocks. Compared with the accuracy of the traditional linear model, the accuracy of the static PPP using the new model of the 2-h prediction clock in N, E, and U directions is improved by about 50%. Furthermore, the static PPP accuracy of 2-h clock products is better than 0.1 m. When an interruption occurs in the real-time model, the accuracy of the kinematic PPP solution using 1-h clock prediction product is better than 0.2 m, without significant accuracy degradation. This model is of practical significance

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.

State-space prediction model for chaotic time series

NASA Astrophysics Data System (ADS)

Alparslan, A. K.; Sayar, M.; Atilgan, A. R.

1998-08-01

A simple method for predicting the continuation of scalar chaotic time series ahead in time is proposed. The false nearest neighbors technique in connection with the time-delayed embedding is employed so as to reconstruct the state space. A local forecasting model based upon the time evolution of the topological neighboring in the reconstructed phase space is suggested. A moving root-mean-square error is utilized in order to monitor the error along the prediction horizon. The model is tested for the convection amplitude of the Lorenz model. The results indicate that for approximately 100 cycles of the training data, the prediction follows the actual continuation very closely about six cycles. The proposed model, like other state-space forecasting models, captures the long-term behavior of the system due to the use of spatial neighbors in the state space.

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

CERAPP: Collaborative Estrogen Receptor Activity Prediction ...

EPA Pesticide Factsheets

Humans potentially 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. Many of these chemicals never have been tested for their ability to interact with the estrogen receptor (ER). Risk assessors need tools to prioritize chemicals for assessment in costly in vivo tests, for instance, within the EPA Endocrine Disruptor Screening Program. Here, we describe a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) demonstrating the efficacy of using predictive computational models on high-throughput screening data to screen thousands of chemicals against the ER. CERAPP combined multiple models developed in collaboration among 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 1677 compounds provided by EPA, to build a total of 40 categorical and 8 continuous models for binding, agonist, and antagonist ER activity. All predictions were tested using an evaluation set of 7522 chemicals collected from the literature. To overcome the limitations of single models, a consensus was built weighting models using a scoring function (0 to 1) based on their accuracies. Individual model scores ranged from 0.69 to 0.85, showing

A New Tool for CME Arrival Time Prediction using Machine Learning Algorithms: CAT-PUMA

NASA Astrophysics Data System (ADS)

Liu, Jiajia; Ye, Yudong; Shen, Chenglong; Wang, Yuming; Erdélyi, Robert

2018-03-01

Coronal mass ejections (CMEs) are arguably the most violent eruptions in the solar system. CMEs can cause severe disturbances in interplanetary space and can even affect human activities in many aspects, causing damage to infrastructure and loss of revenue. Fast and accurate prediction of CME arrival time is vital to minimize the disruption that CMEs may cause when interacting with geospace. In this paper, we propose a new approach for partial-/full halo CME Arrival Time Prediction Using Machine learning Algorithms (CAT-PUMA). Via detailed analysis of the CME features and solar-wind parameters, we build a prediction engine taking advantage of 182 previously observed geo-effective partial-/full halo CMEs and using algorithms of the Support Vector Machine. We demonstrate that CAT-PUMA is accurate and fast. In particular, predictions made after applying CAT-PUMA to a test set unknown to the engine show a mean absolute prediction error of ∼5.9 hr within the CME arrival time, with 54% of the predictions having absolute errors less than 5.9 hr. Comparisons with other models reveal that CAT-PUMA has a more accurate prediction for 77% of the events investigated that can be carried out very quickly, i.e., within minutes of providing the necessary input parameters of a CME. A practical guide containing the CAT-PUMA engine and the source code of two examples are available in the Appendix, allowing the community to perform their own applications for prediction using CAT-PUMA.

Glass Fibre/Epoxy Resin Interface Life-Time Prediction.

DTIC Science & Technology

1983-04-01

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

Univariate Time Series Prediction of Solar Power Using a Hybrid Wavelet-ARMA-NARX Prediction Method

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

Nazaripouya, Hamidreza; Wang, Yubo; Chu, Chi-Cheng

This paper proposes a new hybrid method for super short-term solar power prediction. Solar output power usually has a complex, nonstationary, and nonlinear characteristic due to intermittent and time varying behavior of solar radiance. In addition, solar power dynamics is fast and is inertia less. An accurate super short-time prediction is required to compensate for the fluctuations and reduce the impact of solar power penetration on the power system. The objective is to predict one step-ahead solar power generation based only on historical solar power time series data. The proposed method incorporates discrete wavelet transform (DWT), Auto-Regressive Moving Average (ARMA)more » models, and Recurrent Neural Networks (RNN), while the RNN architecture is based on Nonlinear Auto-Regressive models with eXogenous inputs (NARX). The wavelet transform is utilized to decompose the solar power time series into a set of richer-behaved forming series for prediction. ARMA model is employed as a linear predictor while NARX is used as a nonlinear pattern recognition tool to estimate and compensate the error of wavelet-ARMA prediction. The proposed method is applied to the data captured from UCLA solar PV panels and the results are compared with some of the common and most recent solar power prediction methods. The results validate the effectiveness of the proposed approach and show a considerable improvement in the prediction precision.« less

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.

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

Time estimation predicts mathematical intelligence.

PubMed

Kramer, Peter; Bressan, Paola; Grassi, Massimo

2011-01-01

Performing mental subtractions affects time (duration) estimates, and making time estimates disrupts mental subtractions. This interaction has been attributed to the concurrent involvement of time estimation and arithmetic with general intelligence and working memory. Given the extant evidence of a relationship between time and number, here we test the stronger hypothesis that time estimation correlates specifically with mathematical intelligence, and not with general intelligence or working-memory capacity. Participants performed a (prospective) time estimation experiment, completed several subtests of the WAIS intelligence test, and self-rated their mathematical skill. For five different durations, we found that time estimation correlated with both arithmetic ability and self-rated mathematical skill. Controlling for non-mathematical intelligence (including working memory capacity) did not change the results. Conversely, correlations between time estimation and non-mathematical intelligence either were nonsignificant, or disappeared after controlling for mathematical intelligence. We conclude that time estimation specifically predicts mathematical intelligence. On the basis of the relevant literature, we furthermore conclude that the relationship between time estimation and mathematical intelligence is likely due to a common reliance on spatial ability.

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

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

Anggraeni, Novia Antika, E-mail: novia.antika.a@gmail.com

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 ofmore » 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.« less

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

PubMed

Burke, M R; Barnes, G R

2008-12-15

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

Prediction-Correction Algorithms for Time-Varying Constrained Optimization

DOE PAGES

Simonetto, Andrea; Dall'Anese, Emiliano

2017-07-26

This article develops online algorithms to track solutions of time-varying constrained optimization problems. Particularly, resembling workhorse Kalman filtering-based approaches for dynamical systems, the proposed methods involve prediction-correction steps to provably track the trajectory of the optimal solutions of time-varying convex problems. The merits of existing prediction-correction methods have been shown for unconstrained problems and for setups where computing the inverse of the Hessian of the cost function is computationally affordable. This paper addresses the limitations of existing methods by tackling constrained problems and by designing first-order prediction steps that rely on the Hessian of the cost function (and do notmore » require the computation of its inverse). In addition, the proposed methods are shown to improve the convergence speed of existing prediction-correction methods when applied to unconstrained problems. Numerical simulations corroborate the analytical results and showcase performance and benefits of the proposed algorithms. A realistic application of the proposed method to real-time control of energy resources is presented.« less

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. © 2014 Wiley Periodicals, Inc.

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.

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.

Real-time stylistic prediction for whole-body human motions.

PubMed

Matsubara, Takamitsu; Hyon, Sang-Ho; Morimoto, Jun

2012-01-01

The ability to predict human motion is crucial in several contexts such as human tracking by computer vision and the synthesis of human-like computer graphics. Previous work has focused on off-line processes with well-segmented data; however, many applications such as robotics require real-time control with efficient computation. In this paper, we propose a novel approach called real-time stylistic prediction for whole-body human motions to satisfy these requirements. This approach uses a novel generative model to represent a whole-body human motion including rhythmic motion (e.g., walking) and discrete motion (e.g., jumping). The generative model is composed of a low-dimensional state (phase) dynamics and a two-factor observation model, allowing it to capture the diversity of motion styles in humans. A real-time adaptation algorithm was derived to estimate both state variables and style parameter of the model from non-stationary unlabeled sequential observations. Moreover, with a simple modification, the algorithm allows real-time adaptation even from incomplete (partial) observations. Based on the estimated state and style, a future motion sequence can be accurately predicted. In our implementation, it takes less than 15 ms for both adaptation and prediction at each observation. Our real-time stylistic prediction was evaluated for human walking, running, and jumping behaviors. Copyright © 2011 Elsevier Ltd. All rights reserved.

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.

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

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.

The EXCITE Trial: Predicting a Clinically Meaningful Motor Activity Log Outcome

PubMed Central

Park, Si-Woon; Wolf, Steven L.; Blanton, Sarah; Winstein, Carolee; Nichols-Larsen, Deborah S.

2013-01-01

Background and Objective This study determined which baseline clinical measurements best predicted a predefined clinically meaningful outcome on the Motor Activity Log (MAL) and developed a predictive multivariate model to determine outcome after 2 weeks of constraint-induced movement therapy (CIMT) and 12 months later using the database from participants in the Extremity Constraint Induced Therapy Evaluation (EXCITE) Trial. Methods A clinically meaningful CIMT outcome was defined as achieving higher than 3 on the MAL Quality of Movement (QOM) scale. Predictive variables included baseline MAL, Wolf Motor Function Test (WMFT), the sensory and motor portion of the Fugl-Meyer Assessment (FMA), spasticity, visual perception, age, gender, type of stroke, concordance, and time after stroke. Significant predictors identified by univariate analysis were used to develop the multivariate model. Predictive equations were generated and odds ratios for predictors were calculated from the multivariate model. Results Pretreatment motor function measured by MAL QOM, WMFT, and FMA were significantly associated with outcome immediately after CIMT. Pretreatment MAL QOM, WMFT, proprioception, and age were significantly associated with outcome after 12 months. Each unit of higher pretreatment MAL QOM score and each unit of faster pretreatment WMFT log mean time improved the probability of achieving a clinically meaningful outcome by 7 and 3 times at posttreatment, and 5 and 2 times after 12 months, respectively. Patients with impaired proprioception had a 20% probability of achieving a clinically meaningful outcome compared with those with intact proprioception. Conclusions Baseline clinical measures of motor and sensory function can be used to predict a clinically meaningful outcome after CIMT. PMID:18780883

The EXCITE Trial: Predicting a clinically meaningful motor activity log outcome.

PubMed

Park, Si-Woon; Wolf, Steven L; Blanton, Sarah; Winstein, Carolee; Nichols-Larsen, Deborah S

2008-01-01

This study determined which baseline clinical measurements best predicted a predefined clinically meaningful outcome on the Motor Activity Log (MAL) and developed a predictive multivariate model to determine outcome after 2 weeks of constraint-induced movement therapy (CIMT) and 12 months later using the database from participants in the Extremity Constraint Induced Therapy Evaluation (EXCITE) Trial. A clinically meaningful CIMT outcome was defined as achieving higher than 3 on the MAL Quality of Movement (QOM) scale. Predictive variables included baseline MAL, Wolf Motor Function Test (WMFT), the sensory and motor portion of the Fugl-Meyer Assessment (FMA), spasticity, visual perception, age, gender, type of stroke, concordance, and time after stroke. Significant predictors identified by univariate analysis were used to develop the multivariate model. Predictive equations were generated and odds ratios for predictors were calculated from the multivariate model. Pretreatment motor function measured by MAL QOM, WMFT, and FMA were significantly associated with outcome immediately after CIMT. Pretreatment MAL QOM, WMFT, proprioception, and age were significantly associated with outcome after 12 months. Each unit of higher pretreatment MAL QOM score and each unit of faster pretreatment WMFT log mean time improved the probability of achieving a clinically meaningful outcome by 7 and 3 times at posttreatment, and 5 and 2 times after 12 months, respectively. Patients with impaired proprioception had a 20% probability of achieving a clinically meaningful outcome compared with those with intact proprioception. Baseline clinical measures of motor and sensory function can be used to predict a clinically meaningful outcome after CIMT.

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

ERIC Educational Resources Information Center

Heil, Daniel P.

2006-01-01

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

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.

Predicting Time to Hospital Discharge for Extremely Preterm Infants

PubMed Central

Hintz, Susan R.; Bann, Carla M.; Ambalavanan, Namasivayam; Cotten, C. Michael; Das, Abhik; Higgins, Rosemary D.

2010-01-01

As extremely preterm infant mortality rates have decreased, concerns regarding resource utilization have intensified. Accurate models to predict time to hospital discharge could aid in resource planning, family counseling, and perhaps stimulate quality improvement initiatives. Objectives For infants <27 weeks estimated gestational age (EGA), to develop, validate and compare several models to predict time to hospital discharge based on time-dependent covariates, and based on the presence of 5 key risk factors as predictors. Patients and Methods This was a retrospective analysis of infants <27 weeks EGA, born 7/2002-12/2005 and surviving to discharge from a NICHD Neonatal Research Network site. Time to discharge was modeled as continuous (postmenstrual age at discharge, PMAD), and categorical variables (“Early” and “Late” discharge). Three linear and logistic regression models with time-dependent covariate inclusion were developed (perinatal factors only, perinatal+early neonatal factors, perinatal+early+later factors). Models for Early and Late discharge using the cumulative presence of 5 key risk factors as predictors were also evaluated. Predictive capabilities were compared using coefficient of determination (R2) for linear models, and AUC of ROC curve for logistic models. Results Data from 2254 infants were included. Prediction of PMAD was poor, with only 38% of variation explained by linear models. However, models incorporating later clinical characteristics were more accurate in predicting “Early” or “Late” discharge (full models: AUC 0.76-0.83 vs. perinatal factor models: AUC 0.56-0.69). In simplified key risk factors models, predicted probabilities for Early and Late discharge compared favorably with observed rates. Furthermore, the AUC (0.75-0.77) were similar to those of models including the full factor set. Conclusions Prediction of Early or Late discharge is poor if only perinatal factors are considered, but improves substantially with

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...

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.

Mechanism by which BMI influences leisure-time physical activity behavior.

PubMed

Godin, Gaston; Bélanger-Gravel, Ariane; Nolin, Bertrand

2008-06-01

The objective of this prospective study was to clarify the mechanism by which BMI influences leisure-time physical activity. This was achieved in accordance with the assumptions underlying the Theory of Planned Behavior (TPB), considered as one of the most useful theories to predict behavior adoption. At baseline, a sample of 1,530 respondents completed a short questionnaire to measure intention and perceived behavioral control (PBC), the two proximal determinants of behavior of TPB. Past behavior, sociodemographic variables, and weight and height were also assessed. The dependent variable, leisure-time physical activity was assessed 3 months later. Hierarchical multiple regression analyses revealed that BMI is a direct predictor of future leisure-time physical activity, not mediated by the variables of TPB. Additional hierarchical analyses indicated that BMI was not a moderator of the intention-behavior and PBC-behavior relationships. The results of this study suggest that high BMI is a significant negative determinant of leisure-time physical activity. This observation reinforces the importance of preventing weight gain as a health promotion strategy for avoiding a sedentary lifestyle.

PSO-MISMO modeling strategy for multistep-ahead time series prediction.

PubMed

Bao, Yukun; Xiong, Tao; Hu, Zhongyi

2014-05-01

Multistep-ahead time series prediction is one of the most challenging research topics in the field of time series modeling and prediction, and is continually under research. Recently, the multiple-input several multiple-outputs (MISMO) modeling strategy has been proposed as a promising alternative for multistep-ahead time series prediction, exhibiting advantages compared with the two currently dominating strategies, the iterated and the direct strategies. Built on the established MISMO strategy, this paper proposes a particle swarm optimization (PSO)-based MISMO modeling strategy, which is capable of determining the number of sub-models in a self-adaptive mode, with varying prediction horizons. Rather than deriving crisp divides with equal-size s prediction horizons from the established MISMO, the proposed PSO-MISMO strategy, implemented with neural networks, employs a heuristic to create flexible divides with varying sizes of prediction horizons and to generate corresponding sub-models, providing considerable flexibility in model construction, which has been validated with simulated and real datasets.

An active balance board system with real-time control of stiffness and time-delay to assess mechanisms of postural stability.

PubMed

Cruise, Denise R; Chagdes, James R; Liddy, Joshua J; Rietdyk, Shirley; Haddad, Jeffrey M; Zelaznik, Howard N; Raman, Arvind

2017-07-26

Increased time-delay in the neuromuscular system caused by neurological disorders, concussions, or advancing age is an important factor contributing to balance loss (Chagdes et al., 2013, 2016a,b). We present the design and fabrication of an active balance board system that allows for a systematic study of stiffness and time-delay induced instabilities in standing posture. Although current commercial balance boards allow for variable stiffness, they do not allow for manipulation of time-delay. Having two controllable parameters can more accurately determine the cause of balance deficiencies, and allows us to induce instabilities even in healthy populations. An inverted pendulum model of human posture on such an active balance board predicts that reduced board rotational stiffness destabilizes upright posture through board tipping, and limit cycle oscillations about the upright position emerge as feedback time-delay is increased. We validate these two mechanisms of instability on the designed balance board, showing that rotational stiffness and board time-delay induced the predicted postural instabilities in healthy, young adults. Although current commercial balance boards utilize control of rotational stiffness, real-time control of both stiffness and time-delay on an active balance board is a novel and innovative manipulation to reveal balance deficiencies and potentially improve individualized balance training by targeting multiple dimensions contributing to standing balance. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

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.

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

Cortical ensemble activity increasingly predicts behaviour outcomes during learning of a motor task

NASA Astrophysics Data System (ADS)

Laubach, Mark; Wessberg, Johan; Nicolelis, Miguel A. L.

2000-06-01

When an animal learns to make movements in response to different stimuli, changes in activity in the motor cortex seem to accompany and underlie this learning. The precise nature of modifications in cortical motor areas during the initial stages of motor learning, however, is largely unknown. Here we address this issue by chronically recording from neuronal ensembles located in the rat motor cortex, throughout the period required for rats to learn a reaction-time task. Motor learning was demonstrated by a decrease in the variance of the rats' reaction times and an increase in the time the animals were able to wait for a trigger stimulus. These behavioural changes were correlated with a significant increase in our ability to predict the correct or incorrect outcome of single trials based on three measures of neuronal ensemble activity: average firing rate, temporal patterns of firing, and correlated firing. This increase in prediction indicates that an association between sensory cues and movement emerged in the motor cortex as the task was learned. Such modifications in cortical ensemble activity may be critical for the initial learning of motor tasks.

Clinical time series prediction: towards a hierarchical dynamical system framework

PubMed Central

Liu, Zitao; Hauskrecht, Milos

2014-01-01

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

Clinical time series prediction: Toward a hierarchical dynamical system framework.

PubMed

Liu, Zitao; Hauskrecht, Milos

2015-09-01

Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. We tested our framework by first learning the time series model from data for the patients in the training set, and then using it to predict future time series values for the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive performance. Copyright © 2014 Elsevier B.V. All rights reserved.

Predictability of CFSv2 in the tropical Indo-Pacific region, at daily and subseasonal time scales

NASA Astrophysics Data System (ADS)

Krishnamurthy, V.

2018-06-01

The predictability of a coupled climate model is evaluated at daily and intraseasonal time scales in the tropical Indo-Pacific region during boreal summer and winter. This study has assessed the daily retrospective forecasts of the Climate Forecast System version 2 from the National Centers of Environmental Prediction for the period 1982-2010. The growth of errors in the forecasts of daily precipitation, monsoon intraseasonal oscillation (MISO) and the Madden-Julian oscillation (MJO) is studied. The seasonal cycle of the daily climatology of precipitation is reasonably well predicted except for the underestimation during the peak of summer. The anomalies follow the typical pattern of error growth in nonlinear systems and show no difference between summer and winter. The initial errors in all the cases are found to be in the nonlinear phase of the error growth. The doubling time of small errors is estimated by applying Lorenz error formula. For summer and winter, the doubling time of the forecast errors is in the range of 4-7 and 5-14 days while the doubling time of the predictability errors is 6-8 and 8-14 days, respectively. The doubling time in MISO during the summer and MJO during the winter is in the range of 12-14 days, indicating higher predictability and providing optimism for long-range prediction. There is no significant difference in the growth of forecasts errors originating from different phases of MISO and MJO, although the prediction of the active phase seems to be slightly better.

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.

PREDICTING EVAPORATION RATES AND TIMES FOR SPILLS OF CHEMICAL MIXTURES

EPA Science Inventory

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

Compound Structure-Independent Activity Prediction in High-Dimensional Target Space.

PubMed

Balfer, Jenny; Hu, Ye; Bajorath, Jürgen

2014-08-01

Profiling of compound libraries against arrays of targets has become an important approach in pharmaceutical research. The prediction of multi-target compound activities also represents an attractive task for machine learning with potential for drug discovery applications. Herein, we have explored activity prediction in high-dimensional target space. Different types of models were derived to predict multi-target activities. The models included naïve Bayesian (NB) and support vector machine (SVM) classifiers based upon compound structure information and NB models derived on the basis of activity profiles, without considering compound structure. Because the latter approach can be applied to incomplete training data and principally depends on the feature independence assumption, SVM modeling was not applicable in this case. Furthermore, iterative hybrid NB models making use of both activity profiles and compound structure information were built. In high-dimensional target space, NB models utilizing activity profile data were found to yield more accurate activity predictions than structure-based NB and SVM models or hybrid models. An in-depth analysis of activity profile-based models revealed the presence of correlation effects across different targets and rationalized prediction accuracy. Taken together, the results indicate that activity profile information can be effectively used to predict the activity of test compounds against novel targets. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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

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.

Scalable Prediction of Energy Consumption using Incremental Time Series Clustering

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

Simmhan, Yogesh; Noor, Muhammad Usman

2013-10-09

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

Do Urinary Cystine Parameters Predict Clinical Stone Activity?

PubMed

Friedlander, Justin I; Antonelli, Jodi A; Canvasser, Noah E; Morgan, Monica S C; Mollengarden, Daniel; Best, Sara; Pearle, Margaret S

2018-02-01

An accurate urinary predictor of stone recurrence would be clinically advantageous for patients with cystinuria. A proprietary assay (Litholink, Chicago, Illinois) measures cystine capacity as a potentially more reliable estimate of stone forming propensity. The recommended capacity level to prevent stone formation, which is greater than 150 mg/l, has not been directly correlated with clinical stone activity. We investigated the relationship between urinary cystine parameters and clinical stone activity. We prospectively followed 48 patients with cystinuria using 24-hour urine collections and serial imaging, and recorded stone activity. We compared cystine urinary parameters at times of stone activity with those obtained during periods of stone quiescence. We then performed correlation and ROC analysis to evaluate the performance of cystine parameters to predict stone activity. During a median followup of 70.6 months (range 2.2 to 274.6) 85 stone events occurred which could be linked to a recent urine collection. Cystine capacity was significantly greater for quiescent urine than for stone event urine (mean ± SD 48 ± 107 vs -38 ± 163 mg/l, p <0.001). Cystine capacity significantly correlated inversely with stone activity (r = -0.29, p <0.001). Capacity also correlated highly negatively with supersaturation (r = -0.88, p <0.001) and concentration (r = -0.87, p <0.001). Using the suggested cutoff of greater than 150 mg/l had only 8.0% sensitivity to predict stone quiescence. Decreasing the cutoff to 90 mg/l or greater improved sensitivity to 25.2% while maintaining specificity at 90.9%. Our results suggest that the target for capacity should be lower than previously advised. Copyright © 2018 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

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

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

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

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 experimentmore » 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.« less

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.

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

NASA Astrophysics Data System (ADS)

Xu, Tao; Li, Xiang; Claramunt, Christophe

2018-06-01

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

Time perspective and the theory of planned behavior: moderate predictors of physical activity among central Appalachian adolescents.

PubMed

Gulley, Tauna; Boggs, Dusta

2014-01-01

The purpose of this study was to determine how well time perspective and the Theory of Planned Behavior (TPB) predicted physical activity among adolescents residing in the central Appalachian region of the United States. A descriptive, correlational design was used. The setting was a rural high school in central Appalachia. The sample included 185 students in grades 9 through 12. Data were collected in school. Variables included components of the TPB, time perspective, and various levels of exercise. Data were analyzed using Pearson's correlation coefficients and multiple regression analysis. The TPB was a moderate predictor of exercise frequency among central Appalachian adolescents, accounting for 42% of the variance. Time perspective did not add to the predictive ability of the TPB to predict exercise frequency in this sample. This study provides support for the TPB for predicting frequency of exercise among central Appalachian adolescents. By understanding the role of the TPB in predicting physical activity among adolescents, nurse practitioners will be able to adapt intervention strategies to improve the physical activity behaviors of this population. Copyright © 2014 National Association of Pediatric Nurse Practitioners. Published by Mosby, Inc. All rights reserved.

Real-time prediction of hand trajectory by ensembles of cortical neurons in primates

NASA Astrophysics Data System (ADS)

Wessberg, Johan; Stambaugh, Christopher R.; Kralik, Jerald D.; Beck, Pamela D.; Laubach, Mark; Chapin, John K.; Kim, Jung; Biggs, S. James; Srinivasan, Mandayam A.; Nicolelis, Miguel A. L.

2000-11-01

Signals derived from the rat motor cortex can be used for controlling one-dimensional movements of a robot arm. It remains unknown, however, whether real-time processing of cortical signals can be employed to reproduce, in a robotic device, the kind of complex arm movements used by primates to reach objects in space. Here we recorded the simultaneous activity of large populations of neurons, distributed in the premotor, primary motor and posterior parietal cortical areas, as non-human primates performed two distinct motor tasks. Accurate real-time predictions of one- and three-dimensional arm movement trajectories were obtained by applying both linear and nonlinear algorithms to cortical neuronal ensemble activity recorded from each animal. In addition, cortically derived signals were successfully used for real-time control of robotic devices, both locally and through the Internet. These results suggest that long-term control of complex prosthetic robot arm movements can be achieved by simple real-time transformations of neuronal population signals derived from multiple cortical areas in primates.

Dynamic imaging of adaptive stress response pathway activation for prediction of drug induced liver injury.

PubMed

Wink, Steven; Hiemstra, Steven W; Huppelschoten, Suzanne; Klip, Janna E; van de Water, Bob

2018-05-01

Drug-induced liver injury remains a concern during drug treatment and development. There is an urgent need for improved mechanistic understanding and prediction of DILI liabilities using in vitro approaches. We have established and characterized a panel of liver cell models containing mechanism-based fluorescent protein toxicity pathway reporters to quantitatively assess the dynamics of cellular stress response pathway activation at the single cell level using automated live cell imaging. We have systematically evaluated the application of four key adaptive stress pathway reporters for the prediction of DILI liability: SRXN1-GFP (oxidative stress), CHOP-GFP (ER stress/UPR response), p21 (p53-mediated DNA damage-related response) and ICAM1 (NF-κB-mediated inflammatory signaling). 118 FDA-labeled drugs in five human exposure relevant concentrations were evaluated for reporter activation using live cell confocal imaging. Quantitative data analysis revealed activation of single or multiple reporters by most drugs in a concentration and time dependent manner. Hierarchical clustering of time course dynamics and refined single cell analysis allowed the allusion of key events in DILI liability. Concentration response modeling was performed to calculate benchmark concentrations (BMCs). Extracted temporal dynamic parameters and BMCs were used to assess the predictive power of sub-lethal adaptive stress pathway activation. Although cellular adaptive responses were activated by non-DILI and severe-DILI compounds alike, dynamic behavior and lower BMCs of pathway activation were sufficiently distinct between these compound classes. The high-level detailed temporal- and concentration-dependent evaluation of the dynamics of adaptive stress pathway activation adds to the overall understanding and prediction of drug-induced liver liabilities.

Data-adaptive Harmonic Decomposition and Real-time Prediction of Arctic Sea Ice Extent

NASA Astrophysics Data System (ADS)

Kondrashov, Dmitri; Chekroun, Mickael; Ghil, Michael

2017-04-01

Decline in the Arctic sea ice extent (SIE) has profound socio-economic implications and is a focus of active scientific research. Of particular interest is prediction of SIE on subseasonal time scales, i.e. from early summer into fall, when sea ice coverage in Arctic reaches its minimum. However, subseasonal forecasting of SIE is very challenging due to the high variability of ocean and atmosphere over Arctic in summer, as well as shortness of observational data and inadequacies of the physics-based models to simulate sea-ice dynamics. The Sea Ice Outlook (SIO) by Sea Ice Prediction Network (SIPN, http://www.arcus.org/sipn) is a collaborative effort to facilitate and improve subseasonal prediction of September SIE by physics-based and data-driven statistical models. Data-adaptive Harmonic Decomposition (DAH) and Multilayer Stuart-Landau Models (MSLM) techniques [Chekroun and Kondrashov, 2017], have been successfully applied to the nonlinear stochastic modeling, as well as retrospective and real-time forecasting of Multisensor Analyzed Sea Ice Extent (MASIE) dataset in key four Arctic regions. In particular, DAH-MSLM predictions outperformed most statistical models and physics-based models in real-time 2016 SIO submissions. The key success factors are associated with DAH ability to disentangle complex regional dynamics of MASIE by data-adaptive harmonic spatio-temporal patterns that reduce the data-driven modeling effort to elemental MSLMs stacked per frequency with fixed and small number of model coefficients to estimate.

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. Copyright © 2015 IBRO. Published by Elsevier Ltd. All rights reserved.

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.

Fixed recurrence and slip models better predict earthquake behavior than the time- and slip-predictable models: 2. Laboratory earthquakes

NASA Astrophysics Data System (ADS)

Rubinstein, Justin L.; Ellsworth, William L.; Beeler, Nicholas M.; Kilgore, Brian D.; Lockner, David A.; Savage, Heather M.

2012-02-01

The behavior of individual stick-slip events observed in three different laboratory experimental configurations is better explained by a "memoryless" earthquake model with fixed inter-event time or fixed slip than it is by the time- and slip-predictable models for earthquake occurrence. We make similar findings in the companion manuscript for the behavior of natural repeating earthquakes. Taken together, these results allow us to conclude that the predictions of a characteristic earthquake model that assumes either fixed slip or fixed recurrence interval should be preferred to the predictions of the time- and slip-predictable models for all earthquakes. Given that the fixed slip and recurrence models are the preferred models for all of the experiments we examine, we infer that in an event-to-event sense the elastic rebound model underlying the time- and slip-predictable models does not explain earthquake behavior. This does not indicate that the elastic rebound model should be rejected in a long-term-sense, but it should be rejected for short-term predictions. The time- and slip-predictable models likely offer worse predictions of earthquake behavior because they rely on assumptions that are too simple to explain the behavior of earthquakes. Specifically, the time-predictable model assumes a constant failure threshold and the slip-predictable model assumes that there is a constant minimum stress. There is experimental and field evidence that these assumptions are not valid for all earthquakes.

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

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

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

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

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

DOE PAGES

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

2016-10-11

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

Trial-to-trial Adaptation: Parsing out the Roles of Cerebellum and BG in Predictive Motor Timing.

PubMed

Lungu, Ovidiu V; Bares, Martin; Liu, Tao; Gomez, Christopher M; Cechova, Ivica; Ashe, James

2016-07-01

We previously demonstrated that predictive motor timing (i.e., timing requiring visuomotor coordination in anticipation of a future event, such as catching or batting a ball) is impaired in patients with spinocerebellar ataxia (SCA) types 6 and 8 relative to healthy controls. Specifically, SCA patients had difficulties postponing their motor response while estimating the target kinematics. This behavioral difference relied on the activation of both cerebellum and striatum in healthy controls, but not in cerebellar patients, despite both groups activating certain parts of cerebellum during the task. However, the role of these two key structures in the dynamic adaptation of the motor timing to target kinematic properties remained unexplored. In the current paper, we analyzed these data with the aim of characterizing the trial-by-trial changes in brain activation. We found that in healthy controls alone, and in comparison with SCA patients, the activation in bilateral striatum was exclusively associated with past successes and that in the left putamen, with maintaining a successful performance across successive trials. In healthy controls, relative to SCA patients, a larger network was involved in maintaining a successful trial-by-trial strategy; this included cerebellum and fronto-parieto-temporo-occipital regions that are typically part of attentional network and action monitoring. Cerebellum was also part of a network of regions activated when healthy participants postponed their motor response from one trial to the next; SCA patients showed reduced activation relative to healthy controls in both cerebellum and striatum in the same contrast. These findings support the idea that cerebellum and striatum play complementary roles in the trial-by-trial adaptation in predictive motor timing. In addition to expanding our knowledge of brain structures involved in time processing, our results have implications for the understanding of BG disorders, such as Parkinson disease

Parturition prediction and timing of canine pregnancy

PubMed Central

Kim, YeunHee; Travis, Alexander J.; Meyers-Wallen, Vicki N.

2007-01-01

An accurate method of predicting the date of parturition in the bitch is clinically useful to minimize or prevent reproductive losses by timely intervention. Similarly, an accurate method of timing canine ovulation and gestation is critical for development of assisted reproductive technologies, e.g. estrous synchronization and embryo transfer. This review discusses present methods for accurately timing canine gestational age and outlines their use in clinical management of high-risk pregnancies and embryo transfer research. PMID:17904630

CAT-PUMA: CME Arrival Time Prediction Using Machine learning Algorithms

NASA Astrophysics Data System (ADS)

Liu, Jiajia; Ye, Yudong; Shen, Chenglong; Wang, Yuming; Erdélyi, Robert

2018-04-01

CAT-PUMA (CME Arrival Time Prediction Using Machine learning Algorithms) quickly and accurately predicts the arrival of Coronal Mass Ejections (CMEs) of CME arrival time. The software was trained via detailed analysis of CME features and solar wind parameters using 182 previously observed geo-effective partial-/full-halo CMEs and uses algorithms of the Support Vector Machine (SVM) to make its predictions, which can be made within minutes of providing the necessary input parameters of a CME.

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

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

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.

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

Body size and activity times mediate mammalian responses to climate change.

PubMed

McCain, Christy M; King, Sarah R B

2014-06-01

Model predictions of extinction risks from anthropogenic climate change are dire, but still overly simplistic. To reliably predict at-risk species we need to know which species are currently responding, which are not, and what traits are mediating the responses. For mammals, we have yet to identify overarching physiological, behavioral, or biogeographic traits determining species' responses to climate change, but they must exist. To date, 73 mammal species in North America and eight additional species worldwide have been assessed for responses to climate change, including local extirpations, range contractions and shifts, decreased abundance, phenological shifts, morphological or genetic changes. Only 52% of those species have responded as expected, 7% responded opposite to expectations, and the remaining 41% have not responded. Which mammals are and are not responding to climate change is mediated predominantly by body size and activity times (phylogenetic multivariate logistic regressions, P < 0.0001). Large mammals respond more, for example, an elk is 27 times more likely to respond to climate change than a shrew. Obligate diurnal and nocturnal mammals are more than twice as likely to respond as mammals with flexible activity times (P < 0.0001). Among the other traits examined, species with higher latitudinal and elevational ranges were more likely to respond to climate change in some analyses, whereas hibernation, heterothermy, burrowing, nesting, and study location did not influence responses. These results indicate that some mammal species can behaviorally escape climate change whereas others cannot, analogous to paleontology's climate sheltering hypothesis. Including body size and activity flexibility traits into future extinction risk forecasts should substantially improve their predictive utility for conservation and management. © 2014 John Wiley & Sons Ltd.

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

ERIC Educational Resources Information Center

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

2012-01-01

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

Taxi-Out Time Prediction for Departures at Charlotte Airport Using Machine Learning Techniques

NASA Technical Reports Server (NTRS)

Lee, Hanbong; Malik, Waqar; Jung, Yoon C.

2016-01-01

Predicting the taxi-out times of departures accurately is important for improving airport efficiency and takeoff time predictability. In this paper, we attempt to apply machine learning techniques to actual traffic data at Charlotte Douglas International Airport for taxi-out time prediction. To find the key factors affecting aircraft taxi times, surface surveillance data is first analyzed. From this data analysis, several variables, including terminal concourse, spot, runway, departure fix and weight class, are selected for taxi time prediction. Then, various machine learning methods such as linear regression, support vector machines, k-nearest neighbors, random forest, and neural networks model are applied to actual flight data. Different traffic flow and weather conditions at Charlotte airport are also taken into account for more accurate prediction. The taxi-out time prediction results show that linear regression and random forest techniques can provide the most accurate prediction in terms of root-mean-square errors. We also discuss the operational complexity and uncertainties that make it difficult to predict the taxi times accurately.

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.

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

Temporal predictive mechanisms modulate motor reaction time during initiation and inhibition of speech and hand movement.

PubMed

Johari, Karim; Behroozmand, Roozbeh

2017-08-01

Skilled movement is mediated by motor commands executed with extremely fine temporal precision. The question of how the brain incorporates temporal information to perform motor actions has remained unanswered. This study investigated the effect of stimulus temporal predictability on response timing of speech and hand movement. Subjects performed a randomized vowel vocalization or button press task in two counterbalanced blocks in response to temporally-predictable and unpredictable visual cues. Results indicated that speech and hand reaction time was decreased for predictable compared with unpredictable stimuli. This finding suggests that a temporal predictive code is established to capture temporal dynamics of sensory cues in order to produce faster movements in responses to predictable stimuli. In addition, results revealed a main effect of modality, indicating faster hand movement compared with speech. We suggest that this effect is accounted for by the inherent complexity of speech production compared with hand movement. Lastly, we found that movement inhibition was faster than initiation for both hand and speech, suggesting that movement initiation requires a longer processing time to coordinate activities across multiple regions in the brain. These findings provide new insights into the mechanisms of temporal information processing during initiation and inhibition of speech and hand movement. Copyright © 2017 Elsevier B.V. All rights reserved.

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

PubMed

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

2002-01-01

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

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).

Prediction of Enzyme Mutant Activity Using Computational Mutagenesis and Incremental Transduction

PubMed Central

Basit, Nada; Wechsler, Harry

2011-01-01

Wet laboratory mutagenesis to determine enzyme activity changes is expensive and time consuming. This paper expands on standard one-shot learning by proposing an incremental transductive method (T2bRF) for the prediction of enzyme mutant activity during mutagenesis using Delaunay tessellation and 4-body statistical potentials for representation. Incremental learning is in tune with both eScience and actual experimentation, as it accounts for cumulative annotation effects of enzyme mutant activity over time. The experimental results reported, using cross-validation, show that overall the incremental transductive method proposed, using random forest as base classifier, yields better results compared to one-shot learning methods. T2bRF is shown to yield 90% on T4 and LAC (and 86% on HIV-1). This is significantly better than state-of-the-art competing methods, whose performance yield is at 80% or less using the same datasets. PMID:22007208

Predicting analysis time in events-driven clinical trials using accumulating time-to-event surrogate information.

PubMed

Wang, Jianming; Ke, Chunlei; Yu, Zhinuan; Fu, Lei; Dornseif, Bruce

2016-05-01

For clinical trials with time-to-event endpoints, predicting the accrual of the events of interest with precision is critical in determining the timing of interim and final analyses. For example, overall survival (OS) is often chosen as the primary efficacy endpoint in oncology studies, with planned interim and final analyses at a pre-specified number of deaths. Often, correlated surrogate information, such as time-to-progression (TTP) and progression-free survival, are also collected as secondary efficacy endpoints. It would be appealing to borrow strength from the surrogate information to improve the precision of the analysis time prediction. Currently available methods in the literature for predicting analysis timings do not consider utilizing the surrogate information. In this article, using OS and TTP as an example, a general parametric model for OS and TTP is proposed, with the assumption that disease progression could change the course of the overall survival. Progression-free survival, related both to OS and TTP, will be handled separately, as it can be derived from OS and TTP. The authors seek to develop a prediction procedure using a Bayesian method and provide detailed implementation strategies under certain assumptions. Simulations are performed to evaluate the performance of the proposed method. An application to a real study is also provided. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Predicting reading and mathematics from neural activity for feedback learning.

PubMed

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

2017-01-01

Although many studies use feedback learning paradigms to study the process of learning in laboratory settings, little is known about their relevance for real-world learning settings such as school. In a large developmental sample (N = 228, 8-25 years), we investigated whether performance and neural activity during a feedback learning task predicted reading and mathematics performance 2 years later. The results indicated that feedback learning performance predicted both reading and mathematics performance. Activity during feedback learning in left superior dorsolateral prefrontal cortex (DLPFC) predicted reading performance, whereas activity in presupplementary motor area/anterior cingulate cortex (pre-SMA/ACC) predicted mathematical performance. Moreover, left superior DLPFC and pre-SMA/ACC activity predicted unique variance in reading and mathematics ability over behavioral testing of feedback learning performance alone. These results provide valuable insights into the relationship between laboratory-based learning tasks and learning in school settings, and the value of neural assessments for prediction of school performance over behavioral testing alone. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Prediction of hourly PM2.5 using a space-time support vector regression model

NASA Astrophysics Data System (ADS)

Yang, Wentao; Deng, Min; Xu, Feng; Wang, Hang

2018-05-01

Real-time air quality prediction has been an active field of research in atmospheric environmental science. The existing methods of machine learning are widely used to predict pollutant concentrations because of their enhanced ability to handle complex non-linear relationships. However, because pollutant concentration data, as typical geospatial data, also exhibit spatial heterogeneity and spatial dependence, they may violate the assumptions of independent and identically distributed random variables in most of the machine learning methods. As a result, a space-time support vector regression model is proposed to predict hourly PM2.5 concentrations. First, to address spatial heterogeneity, spatial clustering is executed to divide the study area into several homogeneous or quasi-homogeneous subareas. To handle spatial dependence, a Gauss vector weight function is then developed to determine spatial autocorrelation variables as part of the input features. Finally, a local support vector regression model with spatial autocorrelation variables is established for each subarea. Experimental data on PM2.5 concentrations in Beijing are used to verify whether the results of the proposed model are superior to those of other methods.

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.

Using Time Series Analysis to Predict Cardiac Arrest in a PICU.

PubMed

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

2015-11-01

To build and test cardiac arrest prediction models in a PICU, using time series analysis as input, and to measure changes in prediction accuracy attributable to different classes of time series data. Retrospective cohort study. Thirty-one bed academic PICU that provides care for medical and general surgical (not congenital heart surgery) patients. Patients experiencing a cardiac arrest in the PICU and requiring external cardiac massage for at least 2 minutes. None. One hundred three cases of cardiac arrest and 109 control cases were used to prepare a baseline dataset that consisted of 1,025 variables in four data classes: multivariate, raw time series, clinical calculations, and time series trend analysis. We trained 20 arrest prediction models using a matrix of five feature sets (combinations of data classes) with four modeling algorithms: linear regression, decision tree, neural network, and support vector machine. The reference model (multivariate data with regression algorithm) had an accuracy of 78% and 87% area under the receiver operating characteristic curve. The best model (multivariate + trend analysis data with support vector machine algorithm) had an accuracy of 94% and 98% area under the receiver operating characteristic curve. Cardiac arrest predictions based on a traditional model built with multivariate data and a regression algorithm misclassified cases 3.7 times more frequently than predictions that included time series trend analysis and built with a support vector machine algorithm. Although the final model lacks the specificity necessary for clinical application, we have demonstrated how information from time series data can be used to increase the accuracy of clinical prediction models.

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.

Prediction of Geomagnetic Activity and Key Parameters in High-latitude Ionosphere

NASA Technical Reports Server (NTRS)

Khazanov, George V.; Lyatsky, Wladislaw; Tan, Arjun; Ridley, Aaron

2007-01-01

Prediction of geomagnetic activity and related events in the Earth's magnetosphere and ionosphere are important tasks of US Space Weather Program. Prediction reliability is dependent on the prediction method, and elements included in the prediction scheme. Two of the main elements of such prediction scheme are: an appropriate geomagnetic activity index, and an appropriate coupling function (the combination of solar wind parameters providing the best correlation between upstream solar wind data and geomagnetic activity). We have developed a new index of geomagnetic activity, the Polar Magnetic (PM) index and an improved version of solar wind coupling function. PM index is similar to the existing polar cap PC index but it shows much better correlation with upstream solar wind/IMF data and other events in the magnetosphere and ionosphere. We investigate the correlation of PM index with upstream solar wind/IMF data for 10 years (1995-2004) that include both low and high solar activity. We also have introduced a new prediction function for the predicting of cross-polar-cap voltage and Joule heating based on using both PM index and upstream solar wind/IMF data. As we show such prediction function significantly increase the reliability of prediction of these important parameters. The correlation coefficients between the actual and predicted values of these parameters are approx. 0.9 and higher.

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.

Artificial neural networks to predict activity type and energy expenditure in youth.

PubMed

Trost, Stewart G; Wong, Weng-Keen; Pfeiffer, Karen A; Zheng, Yonglei

2012-09-01

Previous studies have demonstrated that pattern recognition approaches to accelerometer data reduction are feasible and moderately accurate in classifying activity type in children. Whether pattern recognition techniques can be used to provide valid estimates of physical activity (PA) energy expenditure in youth remains unexplored in the research literature. The objective of this study is to develop and test artificial neural networks (ANNs) to predict PA type and energy expenditure (PAEE) from processed accelerometer data collected in children and adolescents. One hundred participants between the ages of 5 and 15 yr completed 12 activity trials that were categorized into five PA types: sedentary, walking, running, light-intensity household activities or games, and moderate-to-vigorous-intensity games or sports. During each trial, participants wore an ActiGraph GT1M on the right hip, and VO2 was measured using the Oxycon Mobile (Viasys Healthcare, Yorba Linda, CA) portable metabolic system. ANNs to predict PA type and PAEE (METs) were developed using the following features: 10th, 25th, 50th, 75th, and 90th percentiles and the lag one autocorrelation. To determine the highest time resolution achievable, we extracted features from 10-, 15-, 20-, 30-, and 60-s windows. Accuracy was assessed by calculating the percentage of windows correctly classified and root mean square error (RMSE). As window size increased from 10 to 60 s, accuracy for the PA-type ANN increased from 81.3% to 88.4%. RMSE for the MET prediction ANN decreased from 1.1 METs to 0.9 METs. At any given window size, RMSE values for the MET prediction ANN were 30-40% lower than the conventional regression-based approaches. ANNs can be used to predict both PA type and PAEE in children and adolescents using count data from a single waist mounted accelerometer.

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

DOE PAGES

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

2016-01-19

A wealth of recent laboratory and field experiments demonstrate that organic aerosol composition evolves with time in the atmosphere, leading to changes in the influence of the organic fraction to cloud condensation nuclei (CCN) spectra. There is a need for tools that can realistically represent the evolution of CCN activity to better predict indirect effects of organic aerosol on clouds and climate. This work describes a model to predict the CCN activity of organic compounds from functional group composition. Following previous methods in the literature, we test the ability of semi-empirical group contribution methods in Kohler 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. Furthermore, 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

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).

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

PubMed Central

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

2015-01-01

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

Hypoglycemia prediction with subject-specific recursive time-series models.

PubMed

Eren-Oruklu, Meriyan; Cinar, Ali; Quinn, Lauretta

2010-01-01

Avoiding hypoglycemia while keeping glucose within the narrow normoglycemic range (70-120 mg/dl) is a major challenge for patients with type 1 diabetes. Continuous glucose monitors can provide hypoglycemic alarms when the measured glucose decreases below a threshold. However, a better approach is to provide an early alarm that predicts a hypoglycemic episode before it occurs, allowing enough time for the patient to take the necessary precaution to avoid hypoglycemia. We have previously proposed subject-specific recursive models for the prediction of future glucose concentrations and evaluated their prediction performance. In this work, our objective was to evaluate this algorithm further to predict hypoglycemia and provide early hypoglycemic alarms. Three different methods were proposed for alarm decision, where (A) absolute predicted glucose values, (B) cumulative-sum (CUSUM) control chart, and (C) exponentially weighted moving-average (EWMA) control chart were used. Each method was validated using data from the Diabetes Research in Children Network, which consist of measurements from a continuous glucose sensor during an insulin-induced hypoglycemia. Reference serum glucose measurements were used to determine the sensitivity to predict hypoglycemia and the false alarm rate. With the hypoglycemic threshold set to 60 mg/dl, sensitivity of 89, 87.5, and 89% and specificity of 67, 74, and 78% were reported for methods A, B, and C, respectively. Mean values for time to detection were 30 +/- 5.51 (A), 25.8 +/- 6.46 (B), and 27.7 +/- 5.32 (C) minutes. Compared to the absolute value method, both CUSUM and EWMA methods behaved more conservatively before raising an alarm (reduced time to detection), which significantly decreased the false alarm rate and increased the specificity. 2010 Diabetes Technology Society.

Horticultural activity predicts later localized limb status in a contemporary pre-industrial population.

PubMed

Stieglitz, Jonathan; Trumble, Benjamin C; Kaplan, Hillard; Gurven, Michael

2017-07-01

Modern humans may have gracile skeletons due to low physical activity levels and mechanical loading. Tests using pre-historic skeletons are limited by the inability to assess behavior directly, while modern industrialized societies possess few socio-ecological features typical of human evolutionary history. Among Tsimane forager-horticulturalists, we test whether greater activity levels and, thus, increased loading earlier in life are associated with greater later-life bone status and diminished age-related bone loss. We used quantitative ultrasonography to assess radial and tibial status among adults aged 20+ years (mean ± SD age = 49 ± 15; 52% female). We conducted systematic behavioral observations to assess earlier-life activity patterns (mean time lag between behavioural observation and ultrasound = 12 years). For a subset of participants, physical activity was again measured later in life, via accelerometry, to determine whether earlier-life time use is associated with later-life activity levels. Anthropometric and demographic data were collected during medical exams. Structural decline with age is reduced for the tibia (female: -0.25 SDs/decade; male: 0.05 SDs/decade) versus radius (female: -0.56 SDs/decade; male: -0.20 SDs/decade), which is expected if greater loading mitigates bone loss. Time allocation to horticulture, but not hunting, positively predicts later-life radial status (β Horticulture  = 0.48, p = 0.01), whereas tibial status is not significantly predicted by subsistence or sedentary leisure participation. Patterns of activity- and age-related change in bone status indicate localized osteogenic responses to loading, and are generally consistent with the logic of bone functional adaptation. Nonmechanical factors related to subsistence lifestyle moderate the association between activity patterns and bone structure. © 2017 Wiley Periodicals, Inc.

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). Copyright © 2015 Elsevier Inc. All rights reserved.

Real-time prediction of the occurrence of GLE events

NASA Astrophysics Data System (ADS)

Núñez, Marlon; Reyes-Santiago, Pedro J.; Malandraki, Olga E.

2017-07-01

A tool for predicting the occurrence of Ground Level Enhancement (GLE) events using the UMASEP scheme is presented. This real-time tool, called HESPERIA UMASEP-500, is based on the detection of the magnetic connection, along which protons arrive in the near-Earth environment, by estimating the lag correlation between the time derivatives of 1 min soft X-ray flux (SXR) and 1 min near-Earth proton fluxes observed by the GOES satellites. Unlike current GLE warning systems, this tool can predict GLE events before the detection by any neutron monitor (NM) station. The prediction performance measured for the period from 1986 to 2016 is presented for two consecutive periods, because of their notable difference in performance. For the 2000-2016 period, this prediction tool obtained a probability of detection (POD) of 53.8% (7 of 13 GLE events), a false alarm ratio (FAR) of 30.0%, and average warning times (AWT) of 8 min with respect to the first NM station's alert and 15 min to the GLE Alert Plus's warning. We have tested the model by replacing the GOES proton data with SOHO/EPHIN proton data, and the results are similar in terms of POD, FAR, and AWT for the same period. The paper also presents a comparison with a GLE warning system.

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.

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

PubMed

Kazanina, Nina

2017-11-01

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

Increasing potential predictability of Indian Summer monsoon active and break spells

NASA Astrophysics Data System (ADS)

Mani, N. J.; Goswami, B.

2009-12-01

An understanding of the limit on potential predictability is crucial for developing appropriate tools for extended range prediction of active/break spells of Indian summer monsoon (ISM). The global low frequency changes in climate modulate the annual cycle of the ISM and can influence the intrinsic predictability limit of the ISM intraseasonal oscillations (ISOs). Using 104 year (1901-2004) long daily rainfall data, the change in potential predictability of active and break spells are estimated by an empirical method. Using an ISO index based on 10-90 day filtered precipitation, Goswami and Xavier (2003)showed that the monsoon breaks are intrinsically more predictable (20-25 days) than the active conditions (10-15 days. In the present study, employing the same method in 15 year sliding windows, we found that the potential predictability of both active and break spells have undergone a rapid increase during the recent three decades. The potential predictability of active spells has shown an increase from 1 week to 2 weeks while that for break spells increased from 2 weeks to 3 weeks. This result is interesting and intriguing in the backdrop of recent finding that the potential predictability of monsoon weather has decreased substantially over the same period compared to earlier decades due to increased potential instability of the atmosphere. The possible role of internal dynamics and external forcing in producing this change has been explored. The variance among peak active/break conditions shows a steady decrease over the years, indicating a lesser event to event variability in the magnitude of ISO peak phases in recent years. The ISO predictability may be closely linked to the error energy cascading from the synoptic scales and the interaction between these scales. Computation of nonlinear kinetic energy exchange between synoptic and ISO scales in frequency domain, also support the notion of ineffectual influence of synoptic scale errors on the ISO scale

Real-time Kp predictions from ACE real time solar wind

NASA Astrophysics Data System (ADS)

Detman, Thomas; Joselyn, Joann

1999-06-01

The Advanced Composition Explorer (ACE) spacecraft provides nearly continuous monitoring of solar wind plasma, magnetic fields, and energetic particles from the Sun-Earth L1 Lagrange point upstream of Earth in the solar wind. The Space Environment Center (SEC) in Boulder receives ACE telemetry from a group of international network of tracking stations. One-minute, and 1-hour averages of solar wind speed, density, temperature, and magnetic field components are posted on SEC's World Wide Web page within 3 to 5 minutes after they are measured. The ACE Real Time Solar Wind (RTSW) can be used to provide real-time warnings and short term forecasts of geomagnetic storms based on the (traditional) Kp index. Here, we use historical data to evaluate the performance of the first real-time Kp prediction algorithm to become operational.

Managing distribution changes in time series prediction

NASA Astrophysics Data System (ADS)

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

2006-07-01

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

Prediction and selection of vocabulary for two leisure activities.

PubMed

Dark, Leigha; Balandin, Susan

2007-01-01

People who use augmentative or alternative communication (AAC) need access to a relevant, socially valid vocabulary if they are to communicate successfully in a variety of contexts. Many people with complex communication needs who utilize some form of high technology or low technology AAC rely on others to predict and select vocabulary for them. In this study the ability of one speech pathologist, nine leisure support workers, and six people with cerebral palsy to accurately predict context-specific vocabulary was explored. Participants predicted vocabulary for two leisure activities - sailing session and Internet café - using the blank page method of vocabulary selection to identify the vocabulary items they considered important for each activity. This predicted vocabulary was then compared with the actual vocabulary used in each of the activities. A total of 187 (68%) of the words predicted for the sailing session were used during recorded conversations, with 88 words (32%) not appearing in the recorded samples. During the visit to the Internet café only 104 (47%) of the words predicted occurred in the recorded samples, with 117 words (53%) not occurring at all. These results support the need to socially validate any vocabulary in order to ensure that it is relevant and useful for the person using the AAC system.

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

PubMed

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

2016-11-22

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

CERAPP: Collaborative Estrogen Receptor Activity Prediction Project

EPA Pesticide Factsheets

Data from a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) demonstrating using predictive computational models on high-throughput screening data to screen thousands of chemicals against the estrogen receptor.This dataset is associated with the following publication:Mansouri , K., A. Abdelaziz, A. Rybacka, A. Roncaglioni, A. Tropsha, A. Varnek, A. Zakharov, A. Worth, A. Richard , C. Grulke , D. Trisciuzzi, D. Fourches, D. Horvath, E. Benfenati , E. Muratov, E.B. Wedebye, F. Grisoni, G.F. Mangiatordi, G.M. Incisivo, H. Hong, H.W. Ng, I.V. Tetko, I. Balabin, J. Kancherla , J. Shen, J. Burton, M. Nicklaus, M. Cassotti, N.G. Nikolov, O. Nicolotti, P.L. Andersson, Q. Zang, R. Politi, R.D. Beger , R. Todeschini, R. Huang, S. Farag, S.A. Rosenberg, S. Slavov, X. Hu, and R. Judson. (Environmental Health Perspectives) CERAPP: Collaborative Estrogen Receptor Activity Prediction Project. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 1-49, (2016).

Feature Selection, Flaring Size and Time-to-Flare Prediction Using Support Vector Regression, and Automated Prediction of Flaring Behavior Based on Spatio-Temporal Measures Using Hidden Markov Models

NASA Astrophysics Data System (ADS)

Al-Ghraibah, Amani

Solar flares release stored magnetic energy in the form of radiation and can have significant detrimental effects on earth including damage to technological infrastructure. Recent work has considered methods to predict future flare activity on the basis of quantitative measures of the solar magnetic field. Accurate advanced warning of solar flare occurrence is an area of increasing concern and much research is ongoing in this area. Our previous work 111] utilized standard pattern recognition and classification techniques to determine (classify) whether a region is expected to flare within a predictive time window, using a Relevance Vector Machine (RVM) classification method. We extracted 38 features which describing the complexity of the photospheric magnetic field, the result classification metrics will provide the baseline against which we compare our new work. We find a true positive rate (TPR) of 0.8, true negative rate (TNR) of 0.7, and true skill score (TSS) of 0.49. This dissertation proposes three basic topics; the first topic is an extension to our previous work [111, where we consider a feature selection method to determine an appropriate feature subset with cross validation classification based on a histogram analysis of selected features. Classification using the top five features resulting from this analysis yield better classification accuracies across a large unbalanced dataset. In particular, the feature subsets provide better discrimination of the many regions that flare where we find a TPR of 0.85, a TNR of 0.65 sightly lower than our previous work, and a TSS of 0.5 which has an improvement comparing with our previous work. In the second topic, we study the prediction of solar flare size and time-to-flare using support vector regression (SVR). When we consider flaring regions only, we find an average error in estimating flare size of approximately half a GOES class. When we additionally consider non-flaring regions, we find an increased average

Predictive coding of visual object position ahead of moving objects revealed by time-resolved EEG decoding.

PubMed

Hogendoorn, Hinze; Burkitt, Anthony N

2018-05-01

Due to the delays inherent in neuronal transmission, our awareness of sensory events necessarily lags behind the occurrence of those events in the world. If the visual system did not compensate for these delays, we would consistently mislocalize moving objects behind their actual position. Anticipatory mechanisms that might compensate for these delays have been reported in animals, and such mechanisms have also been hypothesized to underlie perceptual effects in humans such as the Flash-Lag Effect. However, to date no direct physiological evidence for anticipatory mechanisms has been found in humans. Here, we apply multivariate pattern classification to time-resolved EEG data to investigate anticipatory coding of object position in humans. By comparing the time-course of neural position representation for objects in both random and predictable apparent motion, we isolated anticipatory mechanisms that could compensate for neural delays when motion trajectories were predictable. As well as revealing an early neural position representation (lag 80-90 ms) that was unaffected by the predictability of the object's trajectory, we demonstrate a second neural position representation at 140-150 ms that was distinct from the first, and that was pre-activated ahead of the moving object when it moved on a predictable trajectory. The latency advantage for predictable motion was approximately 16 ± 2 ms. To our knowledge, this provides the first direct experimental neurophysiological evidence of anticipatory coding in human vision, revealing the time-course of predictive mechanisms without using a spatial proxy for time. The results are numerically consistent with earlier animal work, and suggest that current models of spatial predictive coding in visual cortex can be effectively extended into the temporal domain. Copyright © 2018 Elsevier Inc. All rights reserved.

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.

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

Model predictive control of P-time event graphs

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

Psychological factors related to physical education classes as predictors of students' intention to partake in leisure-time physical activity.

PubMed

Baena-Extremera, Antonio; Granero-Gallegos, Antonio; Ponce-de-León-Elizondo, Ana; Sanz-Arazuri, Eva; Valdemoros-San-Emeterio, María de Los Ángeles; Martínez-Molina, Marina

2016-04-01

In view of the rise in sedentary lifestyle amongst young people, knowledge regarding their intention to partake in physical activity can be decisive when it comes to instilling physical activity habits to improve the current and future health of school students. Therefore, the object of this study was to find a predictive model of the intention to partake in leisure- time physical activity based on motivation, satisfaction and competence. The sample consisted of 347 Spanish, male, high school students and 411 female students aged between 13 and 18 years old. We used a questionnaire made up of the Sport Motivation Scale, Sport Satisfaction Instrument, and the competence factor in the Basic Psychological Needs in Exercise Scale and Intention to Partake in Leisure-Time Physical Activity, all of them adapted to school Physical Education. We carried out confirmatory factor analyses and structural equation models. The intention to partake in leisure-time physical activity was predicted by competence and the latter by satisfaction/fun. Intrinsic motivation was revealed to be the best predictor of satisfaction/fun. Intrinsic motivation should be enhanced in order to predict an intention to partake in physical activity in Physical Education students.

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

PubMed

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

2014-01-01

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

Fatness predicts decreased physical activity and increased sedentary time, but not vice versa: support from a longitudinal study in 8- to 11-year-old children.

PubMed

Hjorth, M F; Chaput, J-P; Ritz, C; Dalskov, S-M; Andersen, R; Astrup, A; Tetens, I; Michaelsen, K F; Sjödin, A

2014-07-01

To examine independent and combined cross-sectional associations between movement behaviors (physical activity (PA), sedentary time, sleep duration, screen time and sleep disturbance) and fat mass index (FMI), as well as to examine longitudinal associations between movement behaviors and FMI. Cross-sectional and longitudinal analyses were done using data from the OPUS school meal study on 785 children (52% boys, 13.4% overweight, ages 8-11 years). Total PA, moderate-to-vigorous PA (MVPA), sedentary time and sleep duration (7 days and 8 nights) were assessed by an accelerometer and FMI was determined by dual-energy X-ray absorptiometry (DXA) on three occasions over 200 days. Demographic characteristics, screen time and sleep disturbance (Children's Sleep Habits Questionnaire) were also obtained. Total PA, MVPA and sleep duration were negatively associated with FMI, while sedentary time and sleep disturbances were positively associated with FMI (P⩽0.01). However, only total PA, MVPA and sleep duration were independently associated with FMI after adjustment for multiple covariates (P<0.001). Nevertheless, combined associations revealed synergistic effects among the different movement behaviors. Changes over time in MVPA were negatively associated with changes in FMI (P<0.001). However, none of the movement behaviors at baseline predicted changes in FMI (P>0.05), but higher FMI at baseline predicted a decrease in total PA and MVPA, and an increase in sedentary time (P⩽0.001), even in normal-weight children (P⩽0.03). Total PA, MVPA and sleep duration were independently associated with FMI, and combined associations of movement behaviors showed a synergistic effect with FMI. In the longitudinal study design, a high FMI at baseline was associated with lower PA and higher sedentary time after 200 days but not vice versa, even in normal-weight children. Our results suggest that adiposity is a better predictor of PA and sedentary behavior changes than the other way

Developing a Degree-Day Model to Predict Billbug (Coleoptera: Curculionidae) Seasonal Activity in Utah and Idaho Turfgrass.

PubMed

Dupuy, Madeleine M; Powell, James A; Ramirez, Ricardo A

2017-10-01

Billbugs are native pests of turfgrass throughout North America, primarily managed with preventive, calendar-based insecticide applications. An existing degree-day model (lower development threshold of 10°C, biofix 1 March) developed in the eastern United States for bluegrass billbug, Sphenophorus parvulus (Gyllenhal; Coleoptera: Curculionidae), may not accurately predict adult billbug activity in the western United States, where billbugs occur as a species complex. The objectives of this study were 1) to track billbug phenology and species composition in managed Utah and Idaho turfgrass and 2) to evaluate model parameters that best predict billbug activity, including those of the existing bluegrass billbug model. Tracking billbugs with linear pitfall traps at two sites each in Utah and Idaho, we confirmed a complex of three univoltine species damaging turfgrass consisting of (in descending order of abundance) bluegrass billbug, hunting billbug (Sphenophorus venatus vestitus Chittenden; Coleoptera: Curculionidae), and Rocky Mountain billbug (Sphenophorus cicatristriatus Fabraeus; Coleoptera: Curculionidae). This complex was active from February through mid-October, with peak activity in mid-June. Based on linear regression analysis, we found that the existing bluegrass billbug model was not robust in predicting billbug activity in Utah and Idaho. Instead, the model that best predicts adult activity of the billbug complex accumulates degree-days above 3°C after 13 January. This model predicts adult activity levels important for management within 11 d of observed activity at 77% of sites. In conjunction with outreach and cooperative networking, this predictive degree-day model may assist end users to better time monitoring efforts and insecticide applications against billbug pests in Utah and Idaho by predicting adult activity. © The Author 2017. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For

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…

Predicting mortality over different time horizons: which data elements are needed?

PubMed

Goldstein, Benjamin A; Pencina, Michael J; Montez-Rath, Maria E; Winkelmayer, Wolfgang C

2017-01-01

Electronic health records (EHRs) are a resource for "big data" analytics, containing a variety of data elements. We investigate how different categories of information contribute to prediction of mortality over different time horizons among patients undergoing hemodialysis treatment. We derived prediction models for mortality over 7 time horizons using EHR data on older patients from a national chain of dialysis clinics linked with administrative data using LASSO (least absolute shrinkage and selection operator) regression. We assessed how different categories of information relate to risk assessment and compared discrete models to time-to-event models. The best predictors used all the available data (c-statistic ranged from 0.72-0.76), with stronger models in the near term. While different variable groups showed different utility, exclusion of any particular group did not lead to a meaningfully different risk assessment. Discrete time models performed better than time-to-event models. Different variable groups were predictive over different time horizons, with vital signs most predictive for near-term mortality and demographic and comorbidities more important in long-term mortality. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Predictive Model of Linear Antimicrobial Peptides Active against Gram-Negative Bacteria.

PubMed

Vishnepolsky, Boris; Gabrielian, Andrei; Rosenthal, Alex; Hurt, Darrell E; Tartakovsky, Michael; Managadze, Grigol; Grigolava, Maya; Makhatadze, George I; Pirtskhalava, Malak

2018-05-29

Antimicrobial peptides (AMPs) have been identified as a potential new class of anti-infectives for drug development. There are a lot of computational methods that try to predict AMPs. Most of them can only predict if a peptide will show any antimicrobial potency, but to the best of our knowledge, there are no tools which can predict antimicrobial potency against particular strains. Here we present a predictive model of linear AMPs being active against particular Gram-negative strains relying on a semi-supervised machine-learning approach with a density-based clustering algorithm. The algorithm can well distinguish peptides active against particular strains from others which may also be active but not against the considered strain. The available AMP prediction tools cannot carry out this task. The prediction tool based on the algorithm suggested herein is available on https://dbaasp.org.

Queuing Time Prediction Using WiFi Positioning Data in an Indoor Scenario

PubMed Central

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

2016-01-01

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

Correlates of daily leisure-time physical activity in a community sample: Narrow personality traits and practical barriers.

PubMed

Gallagher, Patrick; Yancy, William S; Denissen, Jaap J A; Kühnel, Anja; Voils, Corrine I

2013-12-01

Previous studies examining correlates of leisure time physical activity (LTPA) have identified personality factors that are correlated with LTPA and practical factors that impede LTPA. The purpose of the present study was to test how several narrow traits predict daily reports of LTPA and to test whether traits that predict LTPA moderate the effects of practical barriers. 1192 participants completed baseline measures of personality, then reported their LTPA and several situational and environmental factors daily for 25 days. We used generalized estimating equations to measure how personality traits, practical barriers, and interactions between these factors affected (1) the odds of engaging in LTPA and (2) the duration of daily LTPA. Higher standing on Activity and Discipline and lower standing on Assertiveness predicted greater odds of engaging in LTPA and longer duration of LTPA, and higher standing on Aesthetics predicted shorter duration of LTPA. Poor weather conditions and less leisure time were associated with less LTPA, and effects of these barriers were generally greater among participants 30 and older. In participants older than 30, poor weather was associated with less LTPA among those with lower standing on Activity but was not associated with LTPA among those high in Activity. Despite Discipline's overall positive association with LTPA, less leisure time and less routineness were greater barriers for those high in Discipline. Assessing narrow personality traits could help target LTPA interventions to individual patients' needs and could help identify important new personality dynamics that affect LTPA.

Robust and Adaptive Online Time Series Prediction with Long Short-Term Memory

PubMed Central

Tao, Qing

2017-01-01

Online time series prediction is the mainstream method in a wide range of fields, ranging from speech analysis and noise cancelation to stock market analysis. However, the data often contains many outliers with the increasing length of time series in real world. These outliers can mislead the learned model if treated as normal points in the process of prediction. To address this issue, in this paper, we propose a robust and adaptive online gradient learning method, RoAdam (Robust Adam), for long short-term memory (LSTM) to predict time series with outliers. This method tunes the learning rate of the stochastic gradient algorithm adaptively in the process of prediction, which reduces the adverse effect of outliers. It tracks the relative prediction error of the loss function with a weighted average through modifying Adam, a popular stochastic gradient method algorithm for training deep neural networks. In our algorithm, the large value of the relative prediction error corresponds to a small learning rate, and vice versa. The experiments on both synthetic data and real time series show that our method achieves better performance compared to the existing methods based on LSTM. PMID:29391864

Robust and Adaptive Online Time Series Prediction with Long Short-Term Memory.

PubMed

Yang, Haimin; Pan, Zhisong; Tao, Qing

2017-01-01

Online time series prediction is the mainstream method in a wide range of fields, ranging from speech analysis and noise cancelation to stock market analysis. However, the data often contains many outliers with the increasing length of time series in real world. These outliers can mislead the learned model if treated as normal points in the process of prediction. To address this issue, in this paper, we propose a robust and adaptive online gradient learning method, RoAdam (Robust Adam), for long short-term memory (LSTM) to predict time series with outliers. This method tunes the learning rate of the stochastic gradient algorithm adaptively in the process of prediction, which reduces the adverse effect of outliers. It tracks the relative prediction error of the loss function with a weighted average through modifying Adam, a popular stochastic gradient method algorithm for training deep neural networks. In our algorithm, the large value of the relative prediction error corresponds to a small learning rate, and vice versa. The experiments on both synthetic data and real time series show that our method achieves better performance compared to the existing methods based on LSTM.

Real-time prediction of rain-triggered lahars: incorporating seasonality and catchment recovery

NASA Astrophysics Data System (ADS)

Jones, Robbie; Manville, Vern; Peakall, Jeff; Froude, Melanie J.; Odbert, Henry M.

2017-12-01

Rain-triggered lahars are a significant secondary hydrological and geomorphic hazard at volcanoes where unconsolidated pyroclastic material produced by explosive eruptions is exposed to intense rainfall, often occurring for years to decades after the initial eruptive activity. Previous studies have shown that secondary lahar initiation is a function of rainfall parameters, source material characteristics and time since eruptive activity. In this study, probabilistic rain-triggered lahar forecasting models are developed using the lahar occurrence and rainfall record of the Belham River valley at the Soufrière Hills volcano (SHV), Montserrat, collected between April 2010 and April 2012. In addition to the use of peak rainfall intensity (PRI) as a base forecasting parameter, considerations for the effects of rainfall seasonality and catchment evolution upon the initiation of rain-triggered lahars and the predictability of lahar generation are also incorporated into these models. Lahar probability increases with peak 1 h rainfall intensity throughout the 2-year dataset and is higher under given rainfall conditions in year 1 than year 2. The probability of lahars is also enhanced during the wet season, when large-scale synoptic weather systems (including tropical cyclones) are more common and antecedent rainfall and thus levels of deposit saturation are typically increased. The incorporation of antecedent conditions and catchment evolution into logistic-regression-based rain-triggered lahar probability estimation models is shown to enhance model performance and displays the potential for successful real-time prediction of lahars, even in areas featuring strongly seasonal climates and temporal catchment recovery.

Substantia nigra activity level predicts trial-to-trial adjustments in cognitive control

PubMed Central

Boehler, C.N.; Bunzeck, N.; Krebs, R.M.; Noesselt, T.; Schoenfeld, M.A.; Heinze, H.-J.; Münte, T.F.; Woldorff, M.G.; Hopf, J.-M.

2011-01-01

Effective adaptation to the demands of a changing environment requires flexible cognitive control. The medial and lateral frontal cortices are involved in such control processes, putatively in close interplay with the basal ganglia. In particular, dopaminergic projections from the midbrain (i.e., from the substantia nigra (SN) and the ventral tegmental area (VTA)) have been proposed to play a pivotal role in modulating the activity in these areas for cognitive control purposes. In that dopaminergic involvement has been strongly implicated in reinforcement learning, these ideas suggest functional links between reinforcement learning, where the outcome of actions shapes behavior over time, and cognitive control in a more general context, where no direct reward is involved. Here, we provide evidence from functional MRI in humans that activity in the SN predicts systematic subsequent trial-to-trial response time (RT) prolongations that are thought to reflect cognitive control in a Stop-signal paradigm. In particular, variations in the activity level of the SN in one trial predicted the degree of RT prolongation on the subsequent trial, consistent with a modulating output signal from the SN being involved in enhancing cognitive control. This link between SN activity and subsequent behavioral adjustments lends support to theoretical accounts that propose dopaminergic control signals that shape behavior both in the presence and absence of direct reward. This SN-based modulatory mechanism is presumably mediated via a wider network that determines response speed in this task, including frontal and parietal control regions, along with the basal ganglia and the associated subthalamic nucleus. PMID:20465358

Preparatory neural activity predicts performance on a conflict task.

PubMed

Stern, Emily R; Wager, Tor D; Egner, Tobias; Hirsch, Joy; Mangels, Jennifer A

2007-10-24

Advance preparation has been shown to improve the efficiency of conflict resolution. Yet, with little empirical work directly linking preparatory neural activity to the performance benefits of advance cueing, it is not clear whether this relationship results from preparatory activation of task-specific networks, or from activity associated with general alerting processes. Here, fMRI data were acquired during a spatial Stroop task in which advance cues either informed subjects of the upcoming relevant feature of conflict stimuli (spatial or semantic) or were neutral. Informative cues decreased reaction time (RT) relative to neutral cues, and cues indicating that spatial information would be task-relevant elicited greater activity than neutral cues in multiple areas, including right anterior prefrontal and bilateral parietal cortex. Additionally, preparatory activation in bilateral parietal cortex and right dorsolateral prefrontal cortex predicted faster RT when subjects responded to spatial location. No regions were found to be specific to semantic cues at conventional thresholds, and lowering the threshold further revealed little overlap between activity associated with spatial and semantic cueing effects, thereby demonstrating a single dissociation between activations related to preparing a spatial versus semantic task-set. This relationship between preparatory activation of spatial processing networks and efficient conflict resolution suggests that advance information can benefit performance by leading to domain-specific biasing of task-relevant information.

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.

Real-time Interplanetary Shock Prediction System

NASA Astrophysics Data System (ADS)

Vandegriff, J. D.; Ho, G. C.; Plauger, J. M.

2002-05-01

We are creating a system to predict the arrival times and maximum intensities of energetic storm particle (ESP) events at the earth using particle fluxes measured by the EPAM instrument aboard NASA's ACE spacecraft. Real-time flux measurements, consisting of 5 minute averages made available 24 hours per day by the NOAA Space Environment Center, are fed into algorithms looking for characteristic changes in flux, velocity dispersion, and anisotropy. These quantities typically show changes up to 3 hours before shock passage, and thus we expect our system to deliver enhanced probabilities for shock arrival with approximately the same lead time. Forecasting information will be made publicly available through http://sd-www.jhuapl.edu/ACE/EPAM/, the Johns Hopkins University Applied Physics Lab web site for the ACE/EPAM instrument. Early results on the training of our algorithms and comparisons with past shock data will be presented.

A Personalized Predictive Framework for Multivariate Clinical Time Series via Adaptive Model Selection.

PubMed

Liu, Zitao; Hauskrecht, Milos

2017-11-01

Building of an accurate predictive model of clinical time series for a patient is critical for understanding of the patient condition, its dynamics, and optimal patient management. Unfortunately, this process is not straightforward. First, patient-specific variations are typically large and population-based models derived or learned from many different patients are often unable to support accurate predictions for each individual patient. Moreover, time series observed for one patient at any point in time may be too short and insufficient to learn a high-quality patient-specific model just from the patient's own data. To address these problems we propose, develop and experiment with a new adaptive forecasting framework for building multivariate clinical time series models for a patient and for supporting patient-specific predictions. The framework relies on the adaptive model switching approach that at any point in time selects the most promising time series model out of the pool of many possible models, and consequently, combines advantages of the population, patient-specific and short-term individualized predictive models. We demonstrate that the adaptive model switching framework is very promising approach to support personalized time series prediction, and that it is able to outperform predictions based on pure population and patient-specific models, as well as, other patient-specific model adaptation strategies.

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

Compositional data analysis for physical activity, sedentary time and sleep research.

PubMed

Dumuid, Dorothea; Stanford, Tyman E; Martin-Fernández, Josep-Antoni; Pedišić, Željko; Maher, Carol A; Lewis, Lucy K; Hron, Karel; Katzmarzyk, Peter T; Chaput, Jean-Philippe; Fogelholm, Mikael; Hu, Gang; Lambert, Estelle V; Maia, José; Sarmiento, Olga L; Standage, Martyn; Barreira, Tiago V; Broyles, Stephanie T; Tudor-Locke, Catrine; Tremblay, Mark S; Olds, Timothy

2017-01-01

The health effects of daily activity behaviours (physical activity, sedentary time and sleep) are widely studied. While previous research has largely examined activity behaviours in isolation, recent studies have adjusted for multiple behaviours. However, the inclusion of all activity behaviours in traditional multivariate analyses has not been possible due to the perfect multicollinearity of 24-h time budget data. The ensuing lack of adjustment for known effects on the outcome undermines the validity of study findings. We describe a statistical approach that enables the inclusion of all daily activity behaviours, based on the principles of compositional data analysis. Using data from the International Study of Childhood Obesity, Lifestyle and the Environment, we demonstrate the application of compositional multiple linear regression to estimate adiposity from children's daily activity behaviours expressed as isometric log-ratio coordinates. We present a novel method for predicting change in a continuous outcome based on relative changes within a composition, and for calculating associated confidence intervals to allow for statistical inference. The compositional data analysis presented overcomes the lack of adjustment that has plagued traditional statistical methods in the field, and provides robust and reliable insights into the health effects of daily activity behaviours.

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.

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

Compensation of Physical Activity and Sedentary Time in Primary School Children

PubMed Central

RIDGERS, NICOLA D.; TIMPERIO, ANNA; CERIN, ESTER; SALMON, JO

2014-01-01

ABSTRACT Purpose There is considerable debate about the possibility of physical activity compensation. This study examined whether increased levels in physical activity and/or sedentary behavior on 1 d were predictive of lower levels in these behaviors on the following day (compensatory mechanisms) among children. Methods Two hundred and forty-eight children (121 boys and 127 girls) age 8–11 yr from nine primary schools in Melbourne, Australia, wore a GT3X+ ActiGraph for seven consecutive days. Time spent in light physical activity (LPA) and moderate- to vigorous-intensity physical activity (MVPA) was derived using age-specific cut points. Sedentary time was defined as 100 counts per minute. Meteorological data (temperature, precipitation, relative humidity, and daylight hours) were obtained daily and matched to accelerometer wear days. Multilevel analyses (day, child, and school) were conducted using generalized linear latent and mixed models. Results On any given day, every additional 10 min spent in MVPA was associated with approximately 25 min less LPA and 5 min less MVPA the following day. Similarly, additional time spent in LPA on any given day was associated with less time in LPA and MVPA the next day. Time spent sedentary was associated with less sedentary time the following day. Adjusting for meteorological variables did not change observed compensation effects. No significant moderating effect of sex was observed. Conclusion The results are consistent with the compensation hypothesis, whereby children appear to compensate their physical activity or sedentary time between days. Additional adjustment for meteorological variables did not change the observed associations. Further research is needed to examine what factors may explain apparent compensatory changes in children’s physical activity and sedentary time. PMID:24492632

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.

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

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.

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

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.

Cure modeling in real-time prediction: How much does it help?

PubMed

Ying, Gui-Shuang; Zhang, Qiang; Lan, Yu; Li, Yimei; Heitjan, Daniel F

2017-08-01

Various parametric and nonparametric modeling approaches exist for real-time prediction in time-to-event clinical trials. Recently, Chen (2016 BMC Biomedical Research Methodology 16) proposed a prediction method based on parametric cure-mixture modeling, intending to cover those situations where it appears that a non-negligible fraction of subjects is cured. In this article we apply a Weibull cure-mixture model to create predictions, demonstrating the approach in RTOG 0129, a randomized trial in head-and-neck cancer. We compare the ultimate realized data in RTOG 0129 to interim predictions from a Weibull cure-mixture model, a standard Weibull model without a cure component, and a nonparametric model based on the Bayesian bootstrap. The standard Weibull model predicted that events would occur earlier than the Weibull cure-mixture model, but the difference was unremarkable until late in the trial when evidence for a cure became clear. Nonparametric predictions often gave undefined predictions or infinite prediction intervals, particularly at early stages of the trial. Simulations suggest that cure modeling can yield better-calibrated prediction intervals when there is a cured component, or the appearance of a cured component, but at a substantial cost in the average width of the intervals. Copyright © 2017 Elsevier Inc. All rights reserved.

Prediction of Time Response of Electrowetting

NASA Astrophysics Data System (ADS)

Lee, Seung Jun; Hong, Jiwoo; Kang, Kwan Hyoung

2009-11-01

It is very important to predict the time response of electrowetting-based devices, such as liquid lenses, reflective displays, and optical switches. We investigated the time response of electrowetting, based on an analytical and a numerical method, to find out characteristic scales and a scaling law for the switching time. For this, spreading process of a sessile droplet was analyzed based on the domain perturbation method. First, we considered the case of weakly viscous fluids. The analytical result for the spreading process was compared with experimental results, which showed very good agreement in overall time response. It was shown that the overall dynamics is governed by P2 shape mode. We derived characteristic scales combining the droplet volume, density, and surface tension. The overall dynamic process was scaled quite well by the scales. A scaling law was derived from the analytical solution and was verified experimentally. We also suggest a scaling law for highly viscous liquids, based on results of numerical analysis for the electrowetting-actuated spreading process.

Assessment of leisure-time physical activity for the prediction of inflammatory status and cardiometabolic profile.

PubMed

Pires, Milena Monfort; Salvador, Emanuel P; Siqueira-Catania, Antonela; Folchetti, Luciana D; Cezaretto, Adriana; Ferreira, Sandra Roberta G

2012-11-01

Associations of leisure-time physical activity (LTPA), commuting and total physical activity with inflammatory markers, insulin resistance and metabolic profile in individuals at high cardiometabolic risk were investigated. This was a cross-sectional study. A total of 193 prediabetic adults were compared according to physical activity levels measured by the international physical activity questionnaire; p for trend and logistic regression was employed. The most active subset showed lower BMI and abdominal circumference, reaching significance only for LTPA (p for trend=0.02). Lipid profile improved with increased physical activity levels. Interleukin-6 decreased with increased total physical activity and LTPA (p for trend=0.02 and 0.03, respectively), while adiponectin increased in more active subsets for LTPA (p for trend=0.03). Elevation in adjusted OR for hypercholesterolemia was significant for lower LTPA durations (p for trend=0.04). High apolipoprotein B/apolipoprotein A ratio was inversely associated with LTPA, commuting and total physical activity. Increase in adjusted OR for insulin resistance was found from the highest to the lowest category of LTPA (p for trend=0.04) but significance disappeared after adjustments for BMI and energy intake. No association of increased C-reactive protein with physical activity domains was observed. In general, the associations of LTPA, but not commuting or total physical activity, with markers of cardiometabolic risk reinforces the importance of initiatives to increase this domain in programs for the prevention of lifestyle-related diseases. Copyright © 2012 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Prestimulus brain activity predicts primacy in list learning

PubMed Central

Galli, Giulia; Choy, Tsee Leng; Otten, Leun J.

2012-01-01

Brain activity immediately before an event can predict whether the event will later be remembered. This indicates that memory formation is influenced by anticipatory mechanisms engaged ahead of stimulus presentation. Here, we asked whether anticipatory processes affect the learning of short word lists, and whether such activity varies as a function of serial position. Participants memorized lists of intermixed visual and auditory words with either an elaborative or rote rehearsal strategy. At the end of each list, a distraction task was performed followed by free recall. Recall performance was better for words in initial list positions and following elaborative rehearsal. Electrical brain activity before auditory words predicted later recall in the elaborative rehearsal condition. Crucially, anticipatory activity only affected recall when words occurred in initial list positions. This indicates that anticipatory processes, possibly related to general semantic preparation, contribute to primacy effects. PMID:22888370

Real-time predictive seasonal influenza model in Catalonia, Spain

PubMed Central

Basile, Luca; Oviedo de la Fuente, Manuel; Torner, Nuria; Martínez, Ana; Jané, Mireia

2018-01-01

Influenza surveillance is critical to monitoring the situation during epidemic seasons and predictive mathematic models may aid the early detection of epidemic patterns. The objective of this study was to design a real-time spatial predictive model of ILI (Influenza Like Illness) incidence rate in Catalonia using one- and two-week forecasts. The available data sources used to select explanatory variables to include in the model were the statutory reporting disease system and the sentinel surveillance system in Catalonia for influenza incidence rates, the official climate service in Catalonia for meteorological data, laboratory data and Google Flu Trend. Time series for every explanatory variable with data from the last 4 seasons (from 2010–2011 to 2013–2014) was created. A pilot test was conducted during the 2014–2015 season to select the explanatory variables to be included in the model and the type of model to be applied. During the 2015–2016 season a real-time model was applied weekly, obtaining the intensity level and predicted incidence rates with 95% confidence levels one and two weeks away for each health region. At the end of the season, the confidence interval success rate (CISR) and intensity level success rate (ILSR) were analysed. For the 2015–2016 season a CISR of 85.3% at one week and 87.1% at two weeks and an ILSR of 82.9% and 82% were observed, respectively. The model described is a useful tool although it is hard to evaluate due to uncertainty. The accuracy of prediction at one and two weeks was above 80% globally, but was lower during the peak epidemic period. In order to improve the predictive power, new explanatory variables should be included. PMID:29513710

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

The effect of concurrent hand movement on estimated time to contact in a prediction motion task.

PubMed

Zheng, Ran; Maraj, Brian K V

2018-04-27

In many activities, we need to predict the arrival of an occluded object. This action is called prediction motion or motion extrapolation. Previous researchers have found that both eye tracking and the internal clocking model are involved in the prediction motion task. Additionally, it is reported that concurrent hand movement facilitates the eye tracking of an externally generated target in a tracking task, even if the target is occluded. The present study examined the effect of concurrent hand movement on the estimated time to contact in a prediction motion task. We found different (accurate/inaccurate) concurrent hand movements had the opposite effect on the eye tracking accuracy and estimated TTC in the prediction motion task. That is, the accurate concurrent hand tracking enhanced eye tracking accuracy and had the trend to increase the precision of estimated TTC, but the inaccurate concurrent hand tracking decreased eye tracking accuracy and disrupted estimated TTC. However, eye tracking accuracy does not determine the precision of estimated TTC.

Striatal Activation Predicts Differential Therapeutic Responses to Methylphenidate and Atomoxetine.

PubMed

Schulz, Kurt P; Bédard, Anne-Claude V; Fan, Jin; Hildebrandt, Thomas B; Stein, Mark A; Ivanov, Iliyan; Halperin, Jeffrey M; Newcorn, Jeffrey H

2017-07-01

Methylphenidate has prominent effects in the dopamine-rich striatum that are absent for the selective norepinephrine transporter inhibitor atomoxetine. This study tested whether baseline striatal activation would predict differential response to the two medications in youth with attention-deficit/hyperactivity disorder (ADHD). A total of 36 youth with ADHD performed a Go/No-Go test during functional magnetic resonance imaging at baseline and were treated with methylphenidate and atomoxetine using a randomized cross-over design. Whole-brain task-related activation was regressed on clinical response. Task-related activation in right caudate nucleus was predicted by an interaction of clinical responses to methylphenidate and atomoxetine (F 1,30  = 17.00; p < .001). Elevated caudate activation was associated with robust improvement for methylphenidate and little improvement for atomoxetine. The rate of robust response was higher for methylphenidate than for atomoxetine in youth with high (94.4% vs. 38.8%; p = .003; number needed to treat = 2, 95% CI = 1.31-3.73) but not low (33.3% vs. 50.0%; p = .375) caudate activation. Furthermore, response to atomoxetine predicted motor cortex activation (F 1,30  = 14.99; p < .001). Enhanced caudate activation for response inhibition may be a candidate biomarker of superior response to methylphenidate over atomoxetine in youth with ADHD, purportedly reflecting the dopaminergic effects of methylphenidate but not atomoxetine in the striatum, whereas motor cortex activation may predict response to atomoxetine. These data do not yet translate directly to the clinical setting, but the approach is potentially important for informing future research and illustrates that it may be possible to predict differential treatment response using a biomarker-driven approach. Stimulant Versus Nonstimulant Medication for Attention Deficit Hyperactivity Disorder in Children; https://clinicaltrials.gov/; NCT00183391. Copyright © 2017 American

Predicting nuclear gene coalescence from mitochondrial data: the three-times rule.

PubMed

Palumbi, S R; Cipriano, F; Hare, M P

2001-05-01

Coalescence theory predicts when genetic drift at nuclear loci will result in fixation of sequence differences to produce monophyletic gene trees. However, the theory is difficult to apply to particular taxa because it hinges on genetically effective population size, which is generally unknown. Neutral theory also predicts that evolution of monophyly will be four times slower in nuclear than in mitochondrial genes primarily because genetic drift is slower at nuclear loci. Variation in mitochondrial DNA (mtDNA) within and between species has been studied extensively, but can these mtDNA data be used to predict coalescence in nuclear loci? Comparison of neutral theories of coalescence of mitochondrial and nuclear loci suggests a simple rule of thumb. The "three-times rule" states that, on average, most nuclear loci will be monophyletic when the branch length leading to the mtDNA sequences of a species is three times longer than the average mtDNA sequence diversity observed within that species. A test using mitochondrial and nuclear intron data from seven species of whales and dolphins suggests general agreement with predictions of the three-times rule. We define the coalescence ratio as the mitochondrial branch length for a species divided by intraspecific mtDNA diversity. We show that species with high coalescence ratios show nuclear monophyly, whereas species with low ratios have polyphyletic nuclear gene trees. As expected, species with intermediate coalescence ratios show a variety of patterns. Especially at very high or low coalescence ratios, the three-times rule predicts nuclear gene patterns that can help detect the action of selection. The three-times rule may be useful as an empirical benchmark for evaluating evolutionary processes occurring at multiple loci.

Work and Non-Work Physical Activity Predict Real-Time Smoking Level and Urges in Young Adults.

PubMed

Nadell, Melanie J; Mermelstein, Robin J; Hedeker, Donald; Marquez, David X

2015-07-01

Physical activity (PA) and smoking are inversely related. However, evidence suggests that some types of PA, namely work-related PA, may show an opposite effect. Despite growing knowledge, there remains a paucity of studies examining the context of these behaviors in naturalistic settings or in young adults, a high-risk group for escalation. Participants were 188 young adults (mean age = 21.32; 53.2% female; 91% current smokers) who participated in an electronic diary week to assess daily smoking and urges and a PA recall to examine daily PA. PA was coded into non-work-related and work-related activity to examine differential effects. We considered both participants' weekly average PA and their daily deviations from their average. Mixed-effects regression models revealed that higher weekly average non-work PA was associated with lower smoking level and urges. Daily deviations in non-work PA did not predict urges; however, increased daily non-work PA relative to participants' weekly average was associated with lower smoking for females but higher levels for males. Regarding work PA, only higher weekly average work PA was associated with higher smoking level for both genders; work PA did not predict urges. Results extend previous literature by documenting differential associations between non-work and work PA and young adult smoking and suggest that young adults engaged in work PA should be considered a high-risk group for escalation. Findings provide theoretical and clinical implications for the use of PA in intervention and highlight the necessity of considering PA as a multidimensional construct when examining its links to health behavior. © The Author 2014. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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.

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

PubMed Central

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

2015-01-01

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 can 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 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. PMID:24912135

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. Copyright © 2014 Elsevier Inc. All rights reserved.

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

Autonomous Motivation Predicts 7-Day Physical Activity in Hong Kong Students.

PubMed

Ha, Amy S; Ng, Johan Y Y

2015-07-01

Autonomous motivation predicts positive health behaviors such as physical activity. However, few studies have examined the relation between motivational regulations and objectively measured physical activity and sedentary behaviors. Thus, we investigated whether different motivational regulations (autonomous motivation, controlled motivation, and amotivation) predicted 7-day physical activity, sedentary behaviors, and health-related quality of life (HRQoL) of students. A total of 115 students (mean age = 11.6 years, 55.7% female) self-reported their motivational regulations and health-related quality of life. Physical activity and sedentary behaviors were measured using accelerometers for seven days. Using multilevel modeling, we found that autonomous motivation predicted higher levels of moderate-to-vigorous physical activity, less sedentary behaviors, and better HRQoL. Controlled motivation and amotivation each only negatively predicted one facet of HRQoL. Results suggested that autonomous motivation could be an important predictor of physical activity behaviors in Hong Kong students. Promotion of this form of motivational regulation may also increase HRQoL. © 2015 The International Association of Applied Psychology.

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. © 2013 Blackwell Verlag GmbH.

Early functional MRI activation predicts motor outcome after ischemic stroke: a longitudinal, multimodal study.

PubMed

Du, Juan; Yang, Fang; Zhang, Zhiqiang; Hu, Jingze; Xu, Qiang; Hu, Jianping; Zeng, Fanyong; Lu, Guangming; Liu, Xinfeng

2018-05-15

An accurate prediction of long term outcome after stroke is urgently required to provide early individualized neurorehabilitation. This study aimed to examine the added value of early neuroimaging measures and identify the best approaches for predicting motor outcome after stroke. This prospective study involved 34 first-ever ischemic stroke patients (time since stroke: 1-14 days) with upper limb impairment. All patients underwent baseline multimodal assessments that included clinical (age, motor impairment), neurophysiological (motor-evoked potentials, MEP) and neuroimaging (diffusion tensor imaging and motor task-based fMRI) measures, and also underwent reassessment 3 months after stroke. Bivariate analysis and multivariate linear regression models were used to predict the motor scores (Fugl-Meyer assessment, FMA) at 3 months post-stroke. With bivariate analysis, better motor outcome significantly correlated with (1) less initial motor impairment and disability, (2) less corticospinal tract injury, (3) the initial presence of MEPs, (4) stronger baseline motor fMRI activations. In multivariate analysis, incorporating neuroimaging data improved the predictive accuracy relative to only clinical and neurophysiological assessments. Baseline fMRI activation in SMA was an independent predictor of motor outcome after stroke. A multimodal model incorporating fMRI and clinical measures best predicted the motor outcome following stroke. fMRI measures obtained early after stroke provided independent prediction of long-term motor outcome.

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

PubMed

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

2012-06-01

Prodromal Huntington's 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. The present study sought to characterise cognitive domains underlying a large test battery and for the first time, evaluate their ability to predict time to diagnosis. 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. 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 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 Huntington's disease trials where they may be more sensitive than individual tests.

Evaluation of the NCEP CFSv2 45-day Forecasts for Predictability of Intraseasonal Tropical Storm Activities

NASA Astrophysics Data System (ADS)

Schemm, J. E.; Long, L.; Baxter, S.

2013-12-01

Evaluation of the NCEP CFSv2 45-day Forecasts for Predictability of Intraseasonal Tropical Storm Activities Jae-Kyung E. Schemm, Lindsey Long and Stephen Baxter Climate Prediction Center, NCEP/NWS/NOAA Predictability of intraseasonal tropical storm (TS) activities is assessed using the 1999-2010 CFSv2 hindcast suite. 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 6, and also the 30-day mean. 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 - 2010 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 number of storms over the Atlantic Basin with the temporal correlation scores ranging from 0.73 for Week 1 forecasts to 0.63 for Week 6, and the average RMS errors ranging from 0.86 to 1.07 during the 1999-2010 hurricane season. Also, the forecast track density distribution and false alarm statistics are compiled using the hindcast analyses. In real-time applications of the intraseasonal TS activity forecasts, the climatological TS forecast statistics will be used to make the model bias corrections in terms of the storm counts, track distribution and removal of false alarms. An operational implementation of the weekly TS activity prediction is planned for early 2014 to provide an objective input for the CPC's Global Tropical Hazards

Predicting the Timing of Maturational Spurts in Skeletal Age

PubMed Central

Nahhas, Ramzi W.; Sherwood, Richard J.; Chumlea, Wm. Cameron; Towne, Bradford; Duren, Dana L.

2014-01-01

Measures of maturity provide windows into the timing and tempo of childhood growth and maturation. Delayed maturation in a single child, or systemically in a population, can result from either genetic or environmental factors. In terms of the skeleton, delayed maturation may result in short stature or indicate another underlying issue. Thus, prediction of the timing of a maturational spurt is often desirable in order to determine the likelihood that a child will catch up to their chronological age peers. Serial data from the Fels Longitudinal Study were used to predict future skeletal age conditional on current skeletal age and to predict the timing of maturational spurts. For children who were delayed relative to their chronological age peers, the like-lihood of catch-up maturation increased through the average age of onset of puberty and decreased prior to the average age of peak height velocity. For boys, the probability of an imminent maturational spurt was higher for those who were less mature. For girls aged 11 to 13 years, however, this probability was higher for those who were more mature, potentially indicating the presence of a skeletal maturation plateau between multiple spurts. The prediction model, available on the web, is most relevant to children of European ancestry living in the Midwestern US. Our model may also provide insight into the tempo of maturation for children in other populations, but must be applied with caution if those populations are known to have high burdens of environmental stressors not typical of the Midwestern US. Am J Phys Anthropol 150:68–75, 2013. PMID:23283666

Prediction of Ripple Properties in Shelf Seas. Mark 2 Predictor for Time Evolution

DTIC Science & Technology

2005-12-01

respectively. It is seen in Figure 15 that the time -evolving ripple predictor manages to predict many of the features seen in the data: the growth from a...UNCLASSIFIED Prediction of Ripple Properties in Shelf Seas Mark 2 Predictor for Time Evolution Final Technical Report Prepared for US Office of Naval...distribution is unlimited j~j HR Wallingford UNCLASSIFIED Prediction of Ripple Properties in Shelf Seas Mark 2 Predictor for Time Evolution

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

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

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

PubMed

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

2017-03-07

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

Do self- or parent-reported dietary, physical activity, and sedentary behaviors predict worsening obesity in children?

PubMed

Dorsey, Karen B; Mauldon, Maria; Magraw, Ruth; Yu, Sunkyung; Krumholz, Harlan M

2010-10-01

To determine whether information gathered during routine healthcare visits regarding obesity related risk factors and risk behaviors predicts increases in BMI z-score over time among overweight and obese children. Medical records from 168 overweight and 441 obese patients seen for repeated visits between September 2003 and April 2006 were examined for reported dietary, physical activity, and sedentary behaviors, family history of obesity and diabetes mellitus, documented Acanthosis nigricans, and BMI values. Random-effects regression analysis was done to determine whether demographic, familial, or behavioral data predicted changes in BMI z-score over time. The presence of A nigricans and a family history of obesity were associated with an increase in BMI z-score (beta=0.56, SE=0.09, P<.001 and beta=0.31, SE=0.13, P=.021). These risk factors explained 8% and 7% of the variation in BMI z-score respectively. Self- or parent-reported dietary and physical activity behaviors did not predict change in BMI z-score. Our findings suggest that the risk factors and self- or parent-reported risk behaviors routinely assessed by pediatric clinicians have limited ability to predict future growth trends, demonstrating the difficulty in determining which patients have the greatest risk of progression of obesity. Copyright (c) 2010 Mosby, Inc. All rights reserved.

Improving the prediction of African savanna vegetation variables using time series of MODIS products

NASA Astrophysics Data System (ADS)

Tsalyuk, Miriam; Kelly, Maggi; Getz, Wayne M.

2017-09-01

African savanna vegetation is subject to extensive degradation as a result of rapid climate and land use change. To better understand these changes detailed assessment of vegetation structure is needed across an extensive spatial scale and at a fine temporal resolution. Applying remote sensing techniques to savanna vegetation is challenging due to sparse cover, high background soil signal, and difficulty to differentiate between spectral signals of bare soil and dry vegetation. In this paper, we attempt to resolve these challenges by analyzing time series of four MODIS Vegetation Products (VPs): Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation (FPAR) for Etosha National Park, a semiarid savanna in north-central Namibia. We create models to predict the density, cover, and biomass of the main savanna vegetation forms: grass, shrubs, and trees. To calibrate remote sensing data we developed an extensive and relatively rapid field methodology and measured herbaceous and woody vegetation during both the dry and wet seasons. We compared the efficacy of the four MODIS-derived VPs in predicting vegetation field measured variables. We then compared the optimal time span of VP time series to predict ground-measured vegetation. We found that Multiyear Partial Least Square Regression (PLSR) models were superior to single year or single date models. Our results show that NDVI-based PLSR models yield robust prediction of tree density (R2 = 0.79, relative Root Mean Square Error, rRMSE = 1.9%) and tree cover (R2 = 0.78, rRMSE = 0.3%). EVI provided the best model for shrub density (R2 = 0.82) and shrub cover (R2 = 0.83), but was only marginally superior over models based on other VPs. FPAR was the best predictor of vegetation biomass of trees (R2 = 0.76), shrubs (R2 = 0.83), and grass (R2 = 0.91). Finally, we addressed an enduring challenge in the remote sensing of semiarid

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

NASA Technical Reports Server (NTRS)

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

1972-01-01

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

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.

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…

Prediction of Geomagnetic Activity and Key Parameters in High-Latitude Ionosphere-Basic Elements

NASA Technical Reports Server (NTRS)

Lyatsky, W.; Khazanov, G. V.

2007-01-01

Prediction of geomagnetic activity and related events in the Earth's magnetosphere and ionosphere is an important task of the Space Weather program. Prediction reliability is dependent on the prediction method and elements included in the prediction scheme. Two main elements are a suitable geomagnetic activity index and coupling function -- the combination of solar wind parameters providing the best correlation between upstream solar wind data and geomagnetic activity. The appropriate choice of these two elements is imperative for any reliable prediction model. The purpose of this work was to elaborate on these two elements -- the appropriate geomagnetic activity index and the coupling function -- and investigate the opportunity to improve the reliability of the prediction of geomagnetic activity and other events in the Earth's magnetosphere. The new polar magnetic index of geomagnetic activity and the new version of the coupling function lead to a significant increase in the reliability of predicting the geomagnetic activity and some key parameters, such as cross-polar cap voltage and total Joule heating in high-latitude ionosphere, which play a very important role in the development of geomagnetic and other activity in the Earth s magnetosphere, and are widely used as key input parameters in modeling magnetospheric, ionospheric, and thermospheric processes.

Intrinsic resting-state activity predicts working memory brain activation and behavioral performance.

PubMed

Zou, Qihong; Ross, Thomas J; Gu, Hong; Geng, Xiujuan; Zuo, Xi-Nian; Hong, L Elliot; Gao, Jia-Hong; Stein, Elliot A; Zang, Yu-Feng; Yang, Yihong

2013-12-01

Although resting-state brain activity has been demonstrated to correspond with task-evoked brain activation, the relationship between intrinsic and evoked brain activity has not been fully characterized. For example, it is unclear whether intrinsic activity can also predict task-evoked deactivation and whether the rest-task relationship is dependent on task load. In this study, we addressed these issues on 40 healthy control subjects using resting-state and task-driven [N-back working memory (WM) task] functional magnetic resonance imaging data collected in the same session. Using amplitude of low-frequency fluctuation (ALFF) as an index of intrinsic resting-state activity, we found that ALFF in the middle frontal gyrus and inferior/superior parietal lobules was positively correlated with WM task-evoked activation, while ALFF in the medial prefrontal cortex, posterior cingulate cortex, superior frontal gyrus, superior temporal gyrus, and fusiform gyrus was negatively correlated with WM task-evoked deactivation. Further, the relationship between the intrinsic resting-state activity and task-evoked activation in lateral/superior frontal gyri, inferior/superior parietal lobules, superior temporal gyrus, and midline regions was stronger at higher WM task loads. In addition, both resting-state activity and the task-evoked activation in the superior parietal lobule/precuneus were significantly correlated with the WM task behavioral performance, explaining similar portions of intersubject performance variance. Together, these findings suggest that intrinsic resting-state activity facilitates or is permissive of specific brain circuit engagement to perform a cognitive task, and that resting activity can predict subsequent task-evoked brain responses and behavioral performance. Copyright © 2012 Wiley Periodicals, Inc.

Evaluation of a real-time travel time prediction system in a freeway construction work zone : executive summary.

DOT National Transportation Integrated Search

2001-03-01

A real-time travel time prediction system (TIPS) was evaluated in a construction work : zone. TIPS includes changeable message signs (CMSs) displaying the travel time and : distance to the end of the work zone to motorists. The travel times displayed...

A real-time prediction model for post-irradiation malignant cervical lymph nodes.

PubMed

Lo, W-C; Cheng, P-W; Shueng, P-W; Hsieh, C-H; Chang, Y-L; Liao, L-J

2018-04-01

To establish a real-time predictive scoring model based on sonographic characteristics for identifying malignant cervical lymph nodes (LNs) in cancer patients after neck irradiation. One-hundred forty-four irradiation-treated patients underwent ultrasonography and ultrasound-guided fine-needle aspirations (USgFNAs), and the resultant data were used to construct a real-time and computerised predictive scoring model. This scoring system was further compared with our previously proposed prediction model. A predictive scoring model, 1.35 × (L axis) + 2.03 × (S axis) + 2.27 × (margin) + 1.48 × (echogenic hilum) + 3.7, was generated by stepwise multivariate logistic regression analysis. Neck LNs were considered to be malignant when the score was ≥ 7, corresponding to a sensitivity of 85.5%, specificity of 79.4%, positive predictive value (PPV) of 82.3%, negative predictive value (NPV) of 83.1%, and overall accuracy of 82.6%. When this new model and the original model were compared, the areas under the receiver operating characteristic curve (c-statistic) were 0.89 and 0.81, respectively (P < .05). A real-time sonographic predictive scoring model was constructed to provide prompt and reliable guidance for USgFNA biopsies to manage cervical LNs after neck irradiation. © 2017 John Wiley & Sons Ltd.

Machine-learning-based calving prediction from activity, lying, and ruminating behaviors in dairy cattle.

PubMed

Borchers, M R; Chang, Y M; Proudfoot, K L; Wadsworth, B A; Stone, A E; Bewley, J M

2017-07-01

The objective of this study was to use automated activity, lying, and rumination monitors to characterize prepartum behavior and predict calving in dairy cattle. Data were collected from 20 primiparous and 33 multiparous Holstein dairy cattle from September 2011 to May 2013 at the University of Kentucky Coldstream Dairy. The HR Tag (SCR Engineers Ltd., Netanya, Israel) automatically collected neck activity and rumination data in 2-h increments. The IceQube (IceRobotics Ltd., South Queensferry, United Kingdom) automatically collected number of steps, lying time, standing time, number of transitions from standing to lying (lying bouts), and total motion, summed in 15-min increments. IceQube data were summed in 2-h increments to match HR Tag data. All behavioral data were collected for 14 d before the predicted calving date. Retrospective data analysis was performed using mixed linear models to examine behavioral changes by day in the 14 d before calving. Bihourly behavioral differences from baseline values over the 14 d before calving were also evaluated using mixed linear models. Changes in daily rumination time, total motion, lying time, and lying bouts occurred in the 14 d before calving. In the bihourly analysis, extreme values for all behaviors occurred in the final 24 h, indicating that the monitored behaviors may be useful in calving prediction. To determine whether technologies were useful at predicting calving, random forest, linear discriminant analysis, and neural network machine-learning techniques were constructed and implemented using R version 3.1.0 (R Foundation for Statistical Computing, Vienna, Austria). These methods were used on variables from each technology and all combined variables from both technologies. A neural network analysis that combined variables from both technologies at the daily level yielded 100.0% sensitivity and 86.8% specificity. A neural network analysis that combined variables from both technologies in bihourly increments was

Predictors of Segmented School Day Physical Activity and Sedentary Time in Children from a Northwest England Low-Income Community

PubMed Central

Taylor, Sarah L.; Curry, Whitney B.; Knowles, Zoe R.; Noonan, Robert J.; McGrane, Bronagh; Fairclough, Stuart J.

2017-01-01

Background: Schools have been identified as important settings for health promotion through physical activity participation, particularly as children are insufficiently active for health. The aim of this study was to investigate the child and school-level influences on children′s physical activity levels and sedentary time during school hours in a sample of children from a low-income community; Methods: One hundred and eighty-six children (110 boys) aged 9–10 years wore accelerometers for 7 days, with 169 meeting the inclusion criteria of 16 h∙day−1 for a minimum of three week days. Multilevel prediction models were constructed to identify significant predictors of sedentary time, light, and moderate to vigorous physical activity during school hour segments. Child-level predictors (sex, weight status, maturity offset, cardiorespiratory fitness, physical activity self-efficacy, physical activity enjoyment) and school-level predictors (number on roll, playground area, provision score) were entered into the models; Results: Maturity offset, fitness, weight status, waist circumference-to-height ratio, sedentary time, moderate to vigorous physical activity, number of children on roll and playground area significantly predicted physical activity and sedentary time; Conclusions: Research should move towards considering context-specific physical activity and its correlates to better inform intervention strategies. PMID:28509887

Evaluation of the ability of three physical activity monitors to predict weight change and estimate energy expenditure.

PubMed

Correa, John B; Apolzan, John W; Shepard, Desti N; Heil, Daniel P; Rood, Jennifer C; Martin, Corby K

2016-07-01

Activity monitors such as the Actical accelerometer, the Sensewear armband, and the Intelligent Device for Energy Expenditure and Activity (IDEEA) are commonly validated against gold standards (e.g., doubly labeled water, or DLW) to determine whether they accurately measure total daily energy expenditure (TEE) or activity energy expenditure (AEE). However, little research has assessed whether these parameters or others (e.g., posture allocation) predict body weight change over time. The aims of this study were to (i) test whether estimated energy expenditure or posture allocation from the devices was associated with weight change during and following a low-calorie diet (LCD) and (ii) compare free-living TEE and AEE predictions from the devices against DLW before weight change. Eighty-seven participants from 2 clinical trials wore 2 of the 3 devices simultaneously for 1 week of a 2-week DLW period. Participants then completed an 8-week LCD and were weighed at the start and end of the LCD and 6 and 12 months after the LCD. More time spent walking at baseline, measured by the IDEEA, significantly predicted greater weight loss during the 8-week LCD. Measures of posture allocation demonstrated medium effect sizes in their relationships with weight change. Bland-Altman analyses indicated that the Sensewear and the IDEEA accurately estimated TEE, and the IDEEA accurately measured AEE. The results suggest that the ability of energy expenditure and posture allocation to predict weight change is limited, and the accuracy of TEE and AEE measurements varies across activity monitoring devices, with multi-sensor monitors demonstrating stronger validity.

Evaluation of the ability of three physical activity monitors to predict weight change and estimate energy expenditure

PubMed Central

Correa, John B.; Apolzan, John W.; Shepard, Desti N.; Heil, Daniel P.; Rood, Jennifer C.; Martin, Corby K.

2016-01-01

Activity monitors such as the Actical accelerometer, the Sensewear armband, and the Intelligent Device for Energy Expenditure and Activity (IDEEA) are commonly validated against gold standards (e.g., doubly labeled water, or DLW) to determine whether they accurately measure total daily energy expenditure (TEE) or activity energy expenditure (AEE). However, little research has assessed whether these parameters or others (e.g., posture allocation) predict body weight change over time. The aims of this study were to (i) test whether estimated energy expenditure or posture allocation from the devices was associated with weight change during and following a low-calorie diet (LCD) and (ii) compare free-living TEE and AEE predictions from the devices against DLW before weight change. Eighty-seven participants from 2 clinical trials wore 2 of the 3 devices simultaneously for 1 week of a 2-week DLW period. Participants then completed an 8-week LCD and were weighed at the start and end of the LCD and 6 and 12 months after the LCD. More time spent walking at baseline, measured by the IDEEA, significantly predicted greater weight loss during the 8-week LCD. Measures of posture allocation demonstrated medium effect sizes in their relationships with weight change. Bland–Altman analyses indicated that the Sensewear and the IDEEA accurately estimated TEE, and the IDEEA accurately measured AEE. The results suggest that the ability of energy expenditure and posture allocation to predict weight change is limited, and the accuracy of TEE and AEE measurements varies across activity monitoring devices, with multi-sensor monitors demonstrating stronger validity. PMID:27270210

SU-E-T-629: Prediction of the ViewRay Radiotherapy Treatment Time for Clinical Logistics

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

Liu, S; Wooten, H; Wu, Y

Purpose: An algorithm is developed in our clinic, given a new treatment plan, to predict treatment delivery time for radiation therapy (RT) treatments of patients on ViewRay magnetic resonance-image guided radiation therapy (MR-IGRT) delivery system. This algorithm is necessary for managing patient treatment appointments, and is useful as an indicator to assess the treatment plan complexity. Methods: A patient’s total treatment delivery time, not including time required for localization, may be described as the sum of four components: (1) the treatment initialization time; (2) the total beam-on time; (3) the gantry rotation time; and (4) the multileaf collimator (MLC) motionmore » time. Each of the four components is predicted separately. The total beam-on time can be calculated using both the planned beam-on time and the decay-corrected delivery dose rate. To predict the remaining components, we quantitatively analyze the patient treatment delivery record files. The initialization time is demonstrated to be random since it depends on the final gantry angle and MLC leaf positions of the previous treatment. Based on modeling the relationships between the gantry rotation angles and the corresponding rotation time, and between the furthest MLC leaf moving distance and the corresponding MLC motion time, the total delivery time is predicted using linear regression. Results: The proposed algorithm has demonstrated the feasibility of predicting the ViewRay treatment delivery time for any treatment plan of any patient. The average prediction error is 0.89 minutes or 5.34%, and the maximal prediction error is 2.09 minutes or 13.87%. Conclusion: We have developed a treatment delivery time prediction algorithm based on the analysis of previous patients’ treatment delivery records. The accuracy of our prediction is sufficient for guiding and arranging patient treatment appointments on a daily basis. The predicted delivery time could also be used as an indicator to assess

Predicting Physical Time Series Using Dynamic Ridge Polynomial Neural Networks

PubMed Central

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

2014-01-01

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

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.

Predicting Physical Activity in Arab American School Children

ERIC Educational Resources Information Center

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

2008-01-01

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

Temporal and environmental patterns of sedentary and active behaviors during adolescents' leisure time.

PubMed

Biddle, Stuart J H; Marshall, Simon J; Gorely, Trish; Cameron, Noel

2009-01-01

There is great interest in young people's overweight and obesity. Few data, however, describe when sedentary and physically active behaviors are likely to occur during the day or how these behaviors are related to location. The purpose of this study was to describe sedentary and active leisure-time behaviors of adolescents across the day and setting. Adolescents (male n = 579, female n = 967; aged 13-16 years) completed time-use diaries for three weekdays and one weekend day. At 15 min intervals, participants recorded what they were doing and where they were. TV viewing and sports/exercise peaked at different times in the day, although TV viewing was two to three times more likely to occur than sports/exercise. TV viewing was most likely to occur during the middle to late evening. The playing of computer games was low, particularly for girls. Weekend data showed TV viewing was the most reported activity throughout the day. For boys, "being in the garden" was highly predictive of engaging in sports/exercise, but this declined rapidly with age. Motorized travel to school was reported twice as often as active travel. Momentary assessments of behavior, in conjunction with contemporaneous reports of environmental factors, describe important patterns of leisure-time active and sedentary behaviors in youth.

Automatic intraaortic balloon pump timing using an intrabeat dicrotic notch prediction algorithm.

PubMed

Schreuder, Jan J; Castiglioni, Alessandro; Donelli, Andrea; Maisano, Francesco; Jansen, Jos R C; Hanania, Ramzi; Hanlon, Pat; Bovelander, Jan; Alfieri, Ottavio

2005-03-01

The efficacy of intraaortic balloon counterpulsation (IABP) during arrhythmic episodes is questionable. A novel algorithm for intrabeat prediction of the dicrotic notch was used for real time IABP inflation timing control. A windkessel model algorithm was used to calculate real-time aortic flow from aortic pressure. The dicrotic notch was predicted using a percentage of calculated peak flow. Automatic inflation timing was set at intrabeat predicted dicrotic notch and was combined with automatic IAB deflation. Prophylactic IABP was applied in 27 patients with low ejection fraction (< 35%) undergoing cardiac surgery. Analysis of IABP at a 1:4 ratio revealed that IAB inflation occurred at a mean of 0.6 +/- 5 ms from the dicrotic notch. In all patients accurate automatic timing at a 1:1 assist ratio was performed. Seventeen patients had episodes of severe arrhythmia, the novel IABP inflation algorithm accurately assisted 318 of 320 arrhythmic beats at a 1:1 ratio. The novel real-time intrabeat IABP inflation timing algorithm performed accurately in all patients during both regular rhythms and severe arrhythmia, allowing fully automatic intrabeat IABP timing.

A New Hybrid-Multiscale SSA Prediction of Non-Stationary Time Series

NASA Astrophysics Data System (ADS)

Ghanbarzadeh, Mitra; Aminghafari, Mina

2016-02-01

Singular spectral analysis (SSA) is a non-parametric method used in the prediction of non-stationary time series. It has two parameters, which are difficult to determine and very sensitive to their values. Since, SSA is a deterministic-based method, it does not give good results when the time series is contaminated with a high noise level and correlated noise. Therefore, we introduce a novel method to handle these problems. It is based on the prediction of non-decimated wavelet (NDW) signals by SSA and then, prediction of residuals by wavelet regression. The advantages of our method are the automatic determination of parameters and taking account of the stochastic structure of time series. As shown through the simulated and real data, we obtain better results than SSA, a non-parametric wavelet regression method and Holt-Winters method.

Remaining dischargeable time prediction for lithium-ion batteries using unscented Kalman filter

NASA Astrophysics Data System (ADS)

Dong, Guangzhong; Wei, Jingwen; Chen, Zonghai; Sun, Han; Yu, Xiaowei

2017-10-01

To overcome the range anxiety, one of the important strategies is to accurately predict the range or dischargeable time of the battery system. To accurately predict the remaining dischargeable time (RDT) of a battery, a RDT prediction framework based on accurate battery modeling and state estimation is presented in this paper. Firstly, a simplified linearized equivalent-circuit-model is developed to simulate the dynamic characteristics of a battery. Then, an online recursive least-square-algorithm method and unscented-Kalman-filter are employed to estimate the system matrices and SOC at every prediction point. Besides, a discrete wavelet transform technique is employed to capture the statistical information of past dynamics of input currents, which are utilized to predict the future battery currents. Finally, the RDT can be predicted based on the battery model, SOC estimation results and predicted future battery currents. The performance of the proposed methodology has been verified by a lithium-ion battery cell. Experimental results indicate that the proposed method can provide an accurate SOC and parameter estimation and the predicted RDT can solve the range anxiety issues.

Implementation and testing of the travel time prediction system (TIPS) : final report, May 2001.

DOT National Transportation Integrated Search

2001-05-01

The Travel Time Prediction System (TIPS) is a portable automated system for predicting and displaying travel time for motorists in advance of and through freeway construction work zones, on a real-time basis. It collects real-time traffic flow data u...

Implementation and testing of the travel time prediction system (TIPS) : executive summary, May 2001.

DOT National Transportation Integrated Search

2001-05-01

The Travel Time Prediction System (TIPS) is a portable automated system for predicting and displaying travel time for motorists in advance of and through freeway construction work zones, on a real-time basis. It collects real-time traffic flow data u...

Application of a time-magnitude prediction model for earthquakes

NASA Astrophysics Data System (ADS)

An, Weiping; Jin, Xueshen; Yang, Jialiang; Dong, Peng; Zhao, Jun; Zhang, He

2007-06-01

In this paper we discuss the physical meaning of the magnitude-time model parameters for earthquake prediction. The gestation process for strong earthquake in all eleven seismic zones in China can be described by the magnitude-time prediction model using the computations of the parameters of the model. The average model parameter values for China are: b = 0.383, c=0.154, d = 0.035, B = 0.844, C = -0.209, and D = 0.188. The robustness of the model parameters is estimated from the variation in the minimum magnitude of the transformed data, the spatial extent, and the temporal period. Analysis of the spatial and temporal suitability of the model indicates that the computation unit size should be at least 4° × 4° for seismic zones in North China, at least 3° × 3° in Southwest and Northwest China, and the time period should be as long as possible.

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.

Predicting changes in volcanic activity through modelling magma ascent rate.

NASA Astrophysics Data System (ADS)

Thomas, Mark; Neuberg, Jurgen

2013-04-01

It is a simple fact that changes in volcanic activity happen and in retrospect they are easy to spot, the dissimilar eruption dynamics between an effusive and explosive event are not hard to miss. However to be able to predict such changes is a much more complicated process. To cause altering styles of activity we know that some part or combination of parts within the system must vary with time, as if there is no physical change within the system, why would the change in eruptive activity occur? What is unknown is which parts or how big a change is needed. We present the results of a suite of conduit flow models that aim to answer these questions by assessing the influence of individual model parameters such as the dissolved water content or magma temperature. By altering these variables in a systematic manner we measure the effect of the changes by observing the modelled ascent rate. We use the ascent rate as we believe it is a very important indicator that can control the style of eruptive activity. In particular, we found that the sensitivity of the ascent rate to small changes in model parameters surprising. Linking these changes to observable monitoring data in a way that these data could be used as a predictive tool is the ultimate goal of this work. We will show that changes in ascent rate can be estimated by a particular type of seismicity. Low frequency seismicity, thought to be caused by the brittle failure of melt is often linked with the movement of magma within a conduit. We show that acceleration in the rate of low frequency seismicity can correspond to an increase in the rate of magma movement and be used as an indicator for potential changes in eruptive activity.

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.

Predicting macropores in space and time by earthworms and abiotic controls

NASA Astrophysics Data System (ADS)

Hohenbrink, Tobias Ludwig; Schneider, Anne-Kathrin; Zangerlé, Anne; Reck, Arne; Schröder, Boris; van Schaik, Loes

2017-04-01

Macropore flow increases infiltration and solute leaching. The macropore density and connectivity, and thereby the hydrological effectiveness, vary in space and time due to earthworms' burrowing activity and their ability to refill their burrows in order to survive drought periods. The aim of our study was to predict the spatiotemporal variability of macropore distributions by a set of potentially controlling abiotic variables and abundances of different earthworm species. We measured earthworm abundances and effective macropore distributions using tracer rainfall infiltration experiments in six measurement campaigns during one year at six field sites in Luxembourg. Hydrologically effective macropores were counted in three soil depths (3, 10, 30 cm) and distinguished into three diameter classes (<2, 2-6, >6 mm). Earthworms were sampled and determined to species-level. In a generalized linear modelling framework, we related macropores to potential spatial and temporal controlling factors. Earthworm species such as Lumbricus terrestris and Aporrectodea longa, local abiotic site conditions (land use, TWI, slope), temporally varying weather conditions (temperature, humidity, precipitation) and soil moisture affected the number of effective macropores. Main controlling factors and explanatory power of the models (uncertainty and model performance) varied depending on the depth and diameter class of macropores. We present spatiotemporal predictions of macropore density by daily-resolved, one year time series of macropore numbers and maps of macropore distributions at specific dates in a small-scale catchment with 5 m resolution.

An accelerating precursor to predict "time-to-failure" in creep and volcanic eruptions

NASA Astrophysics Data System (ADS)

Hao, Shengwang; Yang, Hang; Elsworth, Derek

2017-09-01

Real-time prediction by monitoring of the evolution of response variables is a central goal in predicting rock failure. A linear relation Ω˙Ω¨-1 = C(tf - t) has been developed to describe the time to failure, where Ω represents a response quantity, C is a constant and tf represents the failure time. Observations from laboratory creep failure experiments and precursors to volcanic eruptions are used to test the validity of the approach. Both cumulative and simple moving window techniques are developed to perform predictions and to illustrate the effects of data selection on the results. Laboratory creep failure experiments on granites show that the linear relation works well during the final approach to failure. For blind prediction, the simple moving window technique is preferred because it always uses the most recent data and excludes effects of early data deviating significantly from the predicted trend. When the predicted results show only small fluctuations, failure is imminent.

Development of measures from the theory of planned behavior applied to leisure-time physical activity.

PubMed

Kerner, Matthew S

2005-06-01

Using the theory of planned behavior as a conceptual framework, scales assessing Attitude to Leisure-time Physical Activity, Expectations of Others, Perceived Control, and Intention to Engage in Leisure-time Physical Activity were developed for use among middle-school students. The study sample included 349 boys and 400 girls, 10 to 14 years of age (M=11.9 yr., SD=.9). Unipolar and bipolar scales with seven response choices were developed, with each scale item phrased in a Likert-type format. Following revisions, 22 items were retained in the Attitude to Leisure-time Physical Activity Scale, 10 items in the Expectations of Others Scale, 3 items in the Perceived Control Scale, and 17 items in the Intention to Engage in Leisure-time Physical Activity Scale. Adequate internal consistency was indicated by standardized coefficients alpha ranging from .75 to .89. Current results must be extended to assess discriminant and predictive validities and to check various reliabilities with new samples, then evaluation of intervention techniques for promotion of positive attitudes about leisure-time physical activity, including perception of control and intentions to engage in leisure-time physical activity.

How many days of monitoring predict physical activity and sedentary behaviour in older adults?

PubMed Central

2011-01-01

Background The number of days of pedometer or accelerometer data needed to reliably assess physical activity (PA) is important for research that examines the relationship with health. While this important research has been completed in young to middle-aged adults, data is lacking in older adults. Further, data determining the number of days of self-reports PA data is also void. The purpose of this study was to examine the number of days needed to predict habitual PA and sedentary behaviour across pedometer, accelerometer, and physical activity log (PA log) data in older adults. Methods Participants (52 older men and women; age = 69.3 ± 7.4 years, range= 55-86 years) wore a Yamax Digiwalker SW-200 pedometer and an ActiGraph 7164 accelerometer while completing a PA log for 21 consecutive days. Mean differences each instrument and intensity between days of the week were examined using separate repeated measures analysis of variance for with pairwise comparisons. Spearman-Brown Prophecy Formulae based on Intraclass Correlations of .80, .85, .90 and .95 were used to predict the number of days of accelerometer or pedometer wear or PA log daily records needed to represent total PA, light PA, moderate-to-vigorous PA, and sedentary behaviour. Results Results of this study showed that three days of accelerometer data, four days of pedometer data, or four days of completing PA logs are needed to accurately predict PA levels in older adults. When examining time spent in specific intensities of PA, fewer days of data are needed for accurate prediction of time spent in that activity for ActiGraph but more for the PA log. To accurately predict average daily time spent in sedentary behaviour, five days of ActiGraph data are needed. Conclusions The number days of objective (pedometer and ActiGraph) and subjective (PA log) data needed to accurately estimate daily PA in older adults was relatively consistent. Despite no statistical differences between days for total PA by the

How many days of monitoring predict physical activity and sedentary behaviour in older adults?

PubMed

Hart, Teresa L; Swartz, Ann M; Cashin, Susan E; Strath, Scott J

2011-06-16

The number of days of pedometer or accelerometer data needed to reliably assess physical activity (PA) is important for research that examines the relationship with health. While this important research has been completed in young to middle-aged adults, data is lacking in older adults. Further, data determining the number of days of self-reports PA data is also void. The purpose of this study was to examine the number of days needed to predict habitual PA and sedentary behaviour across pedometer, accelerometer, and physical activity log (PA log) data in older adults. Participants (52 older men and women; age = 69.3 ± 7.4 years, range= 55-86 years) wore a Yamax Digiwalker SW-200 pedometer and an ActiGraph 7164 accelerometer while completing a PA log for 21 consecutive days. Mean differences each instrument and intensity between days of the week were examined using separate repeated measures analysis of variance for with pairwise comparisons. Spearman-Brown Prophecy Formulae based on Intraclass Correlations of .80, .85, .90 and .95 were used to predict the number of days of accelerometer or pedometer wear or PA log daily records needed to represent total PA, light PA, moderate-to-vigorous PA, and sedentary behaviour. Results of this study showed that three days of accelerometer data, four days of pedometer data, or four days of completing PA logs are needed to accurately predict PA levels in older adults. When examining time spent in specific intensities of PA, fewer days of data are needed for accurate prediction of time spent in that activity for ActiGraph but more for the PA log. To accurately predict average daily time spent in sedentary behaviour, five days of ActiGraph data are needed. The number days of objective (pedometer and ActiGraph) and subjective (PA log) data needed to accurately estimate daily PA in older adults was relatively consistent. Despite no statistical differences between days for total PA by the pedometer and ActiGraph, the magnitude of

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

PubMed Central

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

2017-01-01

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

Predicting variations of perceptual performance across individuals from neural activity using pattern classifiers.

PubMed

Das, Koel; Giesbrecht, Barry; Eckstein, Miguel P

2010-07-15

Within the past decade computational approaches adopted from the field of machine learning have provided neuroscientists with powerful new tools for analyzing neural data. For instance, previous studies have applied pattern classification algorithms to electroencephalography data to predict the category of presented visual stimuli, human observer decision choices and task difficulty. Here, we quantitatively compare the ability of pattern classifiers and three ERP metrics (peak amplitude, mean amplitude, and onset latency of the face-selective N170) to predict variations across individuals' behavioral performance in a difficult perceptual task identifying images of faces and cars embedded in noise. We investigate three different pattern classifiers (Classwise Principal Component Analysis, CPCA; Linear Discriminant Analysis, LDA; and Support Vector Machine, SVM), five training methods differing in the selection of training data sets and three analyses procedures for the ERP measures. We show that all three pattern classifier algorithms surpass traditional ERP measurements in their ability to predict individual differences in performance. Although the differences across pattern classifiers were not large, the CPCA method with training data sets restricted to EEG activity for trials in which observers expressed high confidence about their decisions performed the highest at predicting perceptual performance of observers. We also show that the neural activity predicting the performance across individuals was distributed through time starting at 120ms, and unlike the face-selective ERP response, sustained for more than 400ms after stimulus presentation, indicating that both early and late components contain information correlated with observers' behavioral performance. Together, our results further demonstrate the potential of pattern classifiers compared to more traditional ERP techniques as an analysis tool for modeling spatiotemporal dynamics of the human brain and

The Case For Prediction-based Best-effort Real-time Systems.

DTIC Science & Technology

1999-01-01

Real - time Systems Peter A. Dinda Loukas Kallivokas January...DISTRIBUTION STATEMENT A Approved for Public Release Distribution Unlimited DTIG QUALBR DISSECTED X The Case For Prediction-based Best-effort Real - time Systems Peter...Mellon University Pittsburgh, PA 15213 A version of this paper appeared in the Seventh Workshop on Parallel and Distributed Real - Time Systems

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

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

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

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 surfacemore » 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.« less

Predicting vigorous physical activity using social cognitive theory.

PubMed

Petosa, R Lingyak; Suminski, Rick; Hortz, Brian

2003-01-01

To test Social Cognitive Theory (SCT) in predicting future vigorous physical activity among college students. College students (n=350) completed a set of instruments measuring SCT constructs. Their vigorous physical activity was tracked for 4 weeks. Exercise role identity, self-regulation, outcome expectancy value, social support, self-efficacy, and positive exercise experience accounted for 27% of the variance in days of vigorous physical activity. The results supported the use of SCT in understanding factors associated with vigorous physical activity rates among college students.

Jump neural network for real-time prediction of glucose concentration.

PubMed

Zecchin, Chiara; Facchinetti, Andrea; Sparacino, Giovanni; Cobelli, Claudio

2015-01-01

Prediction of the future value of a variable is of central importance in a wide variety of fields, including economy and finance, meteorology, informatics, and, last but not least important, medicine. For example, in the therapy of Type 1 Diabetes (T1D), in which, for patient safety, glucose concentration in the blood should be maintained in a defined normoglycemic range, the ability to forecast glucose concentration in the short-term (with a prediction horizon of around 30 min) might be sufficient to reduce the incidence of hypoglycemic and hyperglycemic events. Neural Network (NN) approaches are suitable for prediction purposes because of their ability to model nonlinear dynamics and handle in their inputs signals coming from different domains. In this chapter we illustrate the design of a jump NN glucose prediction algorithm that exploits past glucose concentration data, measured in real-time by a minimally invasive continuous glucose monitoring (CGM) sensor, and information on ingested carbohydrates, supplied by the patient himself or herself. The methodology is assessed by tuning the NN on data of ten T1D individuals and then testing it on a dataset of ten different subjects. Results with a prediction horizon of 30 min show that prediction of glucose concentration in T1D via NN is feasible and sufficiently accurate. The average time anticipation obtained is compatible with the generation of preventive hypoglycemic and hyperglycemic alerts and the improvement of artificial pancreas performance.

Individualized relapse prediction: Personality measures and striatal and insular activity during reward-processing robustly predict relapse.

PubMed

Gowin, Joshua L; Ball, Tali M; Wittmann, Marc; Tapert, Susan F; Paulus, Martin P

2015-07-01

Nearly half of individuals with substance use disorders relapse in the year after treatment. A diagnostic tool to help clinicians make decisions regarding treatment does not exist for psychiatric conditions. Identifying individuals with high risk for relapse to substance use following abstinence has profound clinical consequences. This study aimed to develop neuroimaging as a robust tool to predict relapse. 68 methamphetamine-dependent adults (15 female) were recruited from 28-day inpatient treatment. During treatment, participants completed a functional MRI scan that examined brain activation during reward processing. Patients were followed 1 year later to assess abstinence. We examined brain activation during reward processing between relapsing and abstaining individuals and employed three random forest prediction models (clinical and personality measures, neuroimaging measures, a combined model) to generate predictions for each participant regarding their relapse likelihood. 18 individuals relapsed. There were significant group by reward-size interactions for neural activation in the left insula and right striatum for rewards. Abstaining individuals showed increased activation for large, risky relative to small, safe rewards, whereas relapsing individuals failed to show differential activation between reward types. All three random forest models yielded good test characteristics such that a positive test for relapse yielded a likelihood ratio 2.63, whereas a negative test had a likelihood ratio of 0.48. These findings suggest that neuroimaging can be developed in combination with other measures as an instrument to predict relapse, advancing tools providers can use to make decisions about individualized treatment of substance use disorders. Published by Elsevier Ireland Ltd.

Forecasting Occurrences of Activities.

PubMed

Minor, Bryan; Cook, Diane J

2017-07-01

While activity recognition has been shown to be valuable for pervasive computing applications, less work has focused on techniques for forecasting the future occurrence of activities. We present an activity forecasting method to predict the time that will elapse until a target activity occurs. This method generates an activity forecast using a regression tree classifier and offers an advantage over sequence prediction methods in that it can predict expected time until an activity occurs. We evaluate this algorithm on real-world smart home datasets and provide evidence that our proposed approach is most effective at predicting activity timings.

Automatic prediction of tongue muscle activations using a finite element model.

PubMed

Stavness, Ian; Lloyd, John E; Fels, Sidney

2012-11-15

Computational modeling has improved our understanding of how muscle forces are coordinated to generate movement in musculoskeletal systems. Muscular-hydrostat systems, such as the human tongue, involve very different biomechanics than musculoskeletal systems, and modeling efforts to date have been limited by the high computational complexity of representing continuum-mechanics. In this study, we developed a computationally efficient tracking-based algorithm for prediction of muscle activations during dynamic 3D finite element simulations. The formulation uses a local quadratic-programming problem at each simulation time-step to find a set of muscle activations that generated target deformations and movements in finite element muscular-hydrostat models. We applied the technique to a 3D finite element tongue model for protrusive and bending movements. Predicted muscle activations were consistent with experimental recordings of tongue strain and electromyography. Upward tongue bending was achieved by recruitment of the superior longitudinal sheath muscle, which is consistent with muscular-hydrostat theory. Lateral tongue bending, however, required recruitment of contralateral transverse and vertical muscles in addition to the ipsilateral margins of the superior longitudinal muscle, which is a new proposition for tongue muscle coordination. Our simulation framework provides a new computational tool for systematic analysis of muscle forces in continuum-mechanics models that is complementary to experimental data and shows promise for eliciting a deeper understanding of human tongue function. Copyright © 2012 Elsevier Ltd. All rights reserved.

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…

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.

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

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

Song, H; Liu, W; Ruan, D

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.more » 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

Development of a real time activity monitoring Android application utilizing SmartStep.

PubMed

Hegde, Nagaraj; Melanson, Edward; Sazonov, Edward

2016-08-01

Footwear based activity monitoring systems are becoming popular in academic research as well as consumer industry segments. In our previous work, we had presented developmental aspects of an insole based activity and gait monitoring system-SmartStep, which is a socially acceptable, fully wireless and versatile insole. The present work describes the development of an Android application that captures the SmartStep data wirelessly over Bluetooth Low energy (BLE), computes features on the received data, runs activity classification algorithms and provides real time feedback. The development of activity classification methods was based on the the data from a human study involving 4 participants. Participants were asked to perform activities of sitting, standing, walking, and cycling while they wore SmartStep insole system. Multinomial Logistic Discrimination (MLD) was utilized in the development of machine learning model for activity prediction. The resulting classification model was implemented in an Android Smartphone. The Android application was benchmarked for power consumption and CPU loading. Leave one out cross validation resulted in average accuracy of 96.9% during model training phase. The Android application for real time activity classification was tested on a human subject wearing SmartStep resulting in testing accuracy of 95.4%.

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

PubMed

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

Prediction of Losartan-Active Carboxylic Acid Metabolite Exposure Following Losartan Administration Using Static and Physiologically Based Pharmacokinetic Models.

PubMed

Nguyen, Hoa Q; Lin, Jian; Kimoto, Emi; Callegari, Ernesto; Tse, Susanna; Obach, R Scott

2017-09-01

The aim of this study was to evaluate a strategy based on static and dynamic physiologically based pharmacokinetic (PBPK) modeling for the prediction of metabolite and parent drug area under the time-concentration curve ratio (AUC m /AUC p ) and their PK profiles in humans using in vitro data when active transport processes are involved in disposition. The strategy was applied to losartan and its pharmacologically active metabolite carboxylosartan as test compounds. Hepatobiliary transport including transport-mediated uptake, canilicular and basolateral efflux, and metabolic clearance estimates were obtained from in vitro studies using human liver microsomes and sandwich-cultured hepatocytes. Human renal clearance of carboxylosartan was estimated from dog renal clearance using allometric scaling approach. All clearance mechanisms were mechanistically incorporated in a static model to predict the relative exposure of carboxylosartan versus losartan (AUC m /AUC p ). The predicted AUC m /AUC p were consistent with the observed data following intravenous and oral administration of losartan. Moreover, the in vitro parameters were used as initial parameters in PBPK permeability-limited disposition models to predict the concentration-time profiles for both parent and its active metabolite after oral administration of losartan. The PBPK model was able to recover the plasma profiles of both losartan and carboxylosartan, further substantiating the validity of this approach. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Predicting survival times for neuroblastoma patients using RNA-seq expression profiles.

PubMed

Grimes, Tyler; Walker, Alejandro R; Datta, Susmita; Datta, Somnath

2018-05-30

Neuroblastoma is the most common tumor of early childhood and is notorious for its high variability in clinical presentation. Accurate prognosis has remained a challenge for many patients. In this study, expression profiles from RNA-sequencing are used to predict survival times directly. Several models are investigated using various annotation levels of expression profiles (genes, transcripts, and introns), and an ensemble predictor is proposed as a heuristic for combining these different profiles. The use of RNA-seq data is shown to improve accuracy in comparison to using clinical data alone for predicting overall survival times. Furthermore, clinically high-risk patients can be subclassified based on their predicted overall survival times. In this effort, the best performing model was the elastic net using both transcripts and introns together. This model separated patients into two groups with 2-year overall survival rates of 0.40±0.11 (n=22) versus 0.80±0.05 (n=68). The ensemble approach gave similar results, with groups 0.42±0.10 (n=25) versus 0.82±0.05 (n=65). This suggests that the ensemble is able to effectively combine the individual RNA-seq datasets. Using predicted survival times based on RNA-seq data can provide improved prognosis by subclassifying clinically high-risk neuroblastoma patients. This article was reviewed by Subharup Guha and Isabel Nepomuceno.

Viscoelastic behavior and life-time predictions

NASA Technical Reports Server (NTRS)

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

1985-01-01

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

A Markovian Entropy Measure for the Analysis of Calcium Activity Time Series

PubMed Central

Rahman, Atiqur; Odorizzi, Laura; LeFew, Michael C.; Golino, Caroline A.; Kemper, Peter; Saha, Margaret S.

2016-01-01

Methods to analyze the dynamics of calcium activity often rely on visually distinguishable features in time series data such as spikes, waves, or oscillations. However, systems such as the developing nervous system display a complex, irregular type of calcium activity which makes the use of such methods less appropriate. Instead, for such systems there exists a class of methods (including information theoretic, power spectral, and fractal analysis approaches) which use more fundamental properties of the time series to analyze the observed calcium dynamics. We present a new analysis method in this class, the Markovian Entropy measure, which is an easily implementable calcium time series analysis method which represents the observed calcium activity as a realization of a Markov Process and describes its dynamics in terms of the level of predictability underlying the transitions between the states of the process. We applied our and other commonly used calcium analysis methods on a dataset from Xenopus laevis neural progenitors which displays irregular calcium activity and a dataset from murine synaptic neurons which displays activity time series that are well-described by visually-distinguishable features. We find that the Markovian Entropy measure is able to distinguish between biologically distinct populations in both datasets, and that it can separate biologically distinct populations to a greater extent than other methods in the dataset exhibiting irregular calcium activity. These results support the benefit of using the Markovian Entropy measure to analyze calcium dynamics, particularly for studies using time series data which do not exhibit easily distinguishable features. PMID:27977764

A Markovian Entropy Measure for the Analysis of Calcium Activity Time Series.

PubMed

Marken, John P; Halleran, Andrew D; Rahman, Atiqur; Odorizzi, Laura; LeFew, Michael C; Golino, Caroline A; Kemper, Peter; Saha, Margaret S

2016-01-01

Methods to analyze the dynamics of calcium activity often rely on visually distinguishable features in time series data such as spikes, waves, or oscillations. However, systems such as the developing nervous system display a complex, irregular type of calcium activity which makes the use of such methods less appropriate. Instead, for such systems there exists a class of methods (including information theoretic, power spectral, and fractal analysis approaches) which use more fundamental properties of the time series to analyze the observed calcium dynamics. We present a new analysis method in this class, the Markovian Entropy measure, which is an easily implementable calcium time series analysis method which represents the observed calcium activity as a realization of a Markov Process and describes its dynamics in terms of the level of predictability underlying the transitions between the states of the process. We applied our and other commonly used calcium analysis methods on a dataset from Xenopus laevis neural progenitors which displays irregular calcium activity and a dataset from murine synaptic neurons which displays activity time series that are well-described by visually-distinguishable features. We find that the Markovian Entropy measure is able to distinguish between biologically distinct populations in both datasets, and that it can separate biologically distinct populations to a greater extent than other methods in the dataset exhibiting irregular calcium activity. These results support the benefit of using the Markovian Entropy measure to analyze calcium dynamics, particularly for studies using time series data which do not exhibit easily distinguishable features.

Verification of a Remaining Flying Time Prediction System for Small Electric Aircraft

NASA Technical Reports Server (NTRS)

Hogge, Edward F.; Bole, Brian M.; Vazquez, Sixto L.; Celaya, Jose R.; Strom, Thomas H.; Hill, Boyd L.; Smalling, Kyle M.; Quach, Cuong C.

2015-01-01

This paper addresses the problem of building trust in online predictions of a battery powered aircraft's remaining available flying time. A set of ground tests is described that make use of a small unmanned aerial vehicle to verify the performance of remaining flying time predictions. The algorithm verification procedure described here uses a fully functional vehicle that is restrained to a platform for repeated run-to-functional-failure experiments. The vehicle under test is commanded to follow a predefined propeller RPM profile in order to create battery demand profiles similar to those expected in flight. The fully integrated aircraft is repeatedly operated until the charge stored in powertrain batteries falls below a specified lower-limit. The time at which the lower-limit on battery charge is crossed is then used to measure the accuracy of remaining flying time predictions. Accuracy requirements are considered in this paper for an alarm that warns operators when remaining flying time is estimated to fall below a specified threshold.

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.

Toward the Real-Time Tsunami Parameters Prediction

NASA Astrophysics Data System (ADS)

Lavrentyev, Mikhail; Romanenko, Alexey; Marchuk, Andrey

2013-04-01

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

Connectionist Architectures for Time Series Prediction of Dynamical Systems

NASA Astrophysics Data System (ADS)

Weigend, Andreas Sebastian

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

Brain activity in valuation regions while thinking about the future predicts individual discount rates.

PubMed

Cooper, Nicole; Kable, Joseph W; Kim, B Kyu; Zauberman, Gal

2013-08-07

People vary widely in how much they discount delayed rewards, yet little is known about the sources of these differences. Here we demonstrate that neural activity in ventromedial prefrontal cortex (VMPFC) and ventral striatum (VS) when human subjects are asked to merely think about the future--specifically, to judge the subjective length of future time intervals--predicts delay discounting. High discounters showed lower activity for longer time delays, while low discounters showed the opposite pattern. Our results demonstrate that the correlation between VMPFC and VS activity and discounting occurs even in the absence of choices about future rewards, and does not depend on a person explicitly evaluating future outcomes or judging their self-relevance. This suggests a link between discounting and basic processes involved in thinking about the future, such as temporal perception. Our results also suggest that reducing impatience requires not suppression of VMPFC and VS activity altogether, but rather modulation of how these regions respond to the present versus the future.

Brain Activity in Valuation Regions while Thinking about the Future Predicts Individual Discount Rates

PubMed Central

Cooper, Nicole; Kim, B. Kyu; Zauberman, Gal

2013-01-01

People vary widely in how much they discount delayed rewards, yet little is known about the sources of these differences. Here we demonstrate that neural activity in ventromedial prefrontal cortex (VMPFC) and ventral striatum (VS) when human subjects are asked to merely think about the future—specifically, to judge the subjective length of future time intervals—predicts delay discounting. High discounters showed lower activity for longer time delays, while low discounters showed the opposite pattern. Our results demonstrate that the correlation between VMPFC and VS activity and discounting occurs even in the absence of choices about future rewards, and does not depend on a person explicitly evaluating future outcomes or judging their self-relevance. This suggests a link between discounting and basic processes involved in thinking about the future, such as temporal perception. Our results also suggest that reducing impatience requires not suppression of VMPFC and VS activity altogether, but rather modulation of how these regions respond to the present versus the future. PMID:23926268

Automated time activity classification based on global positioning system (GPS) tracking data

PubMed Central

2011-01-01

Background Air pollution epidemiological studies are increasingly using global positioning system (GPS) to collect time-location data because they offer continuous tracking, high temporal resolution, and minimum reporting burden for participants. However, substantial uncertainties in the processing and classifying of raw GPS data create challenges for reliably characterizing time activity patterns. We developed and evaluated models to classify people's major time activity patterns from continuous GPS tracking data. Methods We developed and evaluated two automated models to classify major time activity patterns (i.e., indoor, outdoor static, outdoor walking, and in-vehicle travel) based on GPS time activity data collected under free living conditions for 47 participants (N = 131 person-days) from the Harbor Communities Time Location Study (HCTLS) in 2008 and supplemental GPS data collected from three UC-Irvine research staff (N = 21 person-days) in 2010. Time activity patterns used for model development were manually classified by research staff using information from participant GPS recordings, activity logs, and follow-up interviews. We evaluated two models: (a) a rule-based model that developed user-defined rules based on time, speed, and spatial location, and (b) a random forest decision tree model. Results Indoor, outdoor static, outdoor walking and in-vehicle travel activities accounted for 82.7%, 6.1%, 3.2% and 7.2% of manually-classified time activities in the HCTLS dataset, respectively. The rule-based model classified indoor and in-vehicle travel periods reasonably well (Indoor: sensitivity > 91%, specificity > 80%, and precision > 96%; in-vehicle travel: sensitivity > 71%, specificity > 99%, and precision > 88%), but the performance was moderate for outdoor static and outdoor walking predictions. No striking differences in performance were observed between the rule-based and the random forest models. The random forest model was fast and easy to execute

Automated time activity classification based on global positioning system (GPS) tracking data.

PubMed

Wu, Jun; Jiang, Chengsheng; Houston, Douglas; Baker, Dean; Delfino, Ralph

2011-11-14

Air pollution epidemiological studies are increasingly using global positioning system (GPS) to collect time-location data because they offer continuous tracking, high temporal resolution, and minimum reporting burden for participants. However, substantial uncertainties in the processing and classifying of raw GPS data create challenges for reliably characterizing time activity patterns. We developed and evaluated models to classify people's major time activity patterns from continuous GPS tracking data. We developed and evaluated two automated models to classify major time activity patterns (i.e., indoor, outdoor static, outdoor walking, and in-vehicle travel) based on GPS time activity data collected under free living conditions for 47 participants (N = 131 person-days) from the Harbor Communities Time Location Study (HCTLS) in 2008 and supplemental GPS data collected from three UC-Irvine research staff (N = 21 person-days) in 2010. Time activity patterns used for model development were manually classified by research staff using information from participant GPS recordings, activity logs, and follow-up interviews. We evaluated two models: (a) a rule-based model that developed user-defined rules based on time, speed, and spatial location, and (b) a random forest decision tree model. Indoor, outdoor static, outdoor walking and in-vehicle travel activities accounted for 82.7%, 6.1%, 3.2% and 7.2% of manually-classified time activities in the HCTLS dataset, respectively. The rule-based model classified indoor and in-vehicle travel periods reasonably well (Indoor: sensitivity > 91%, specificity > 80%, and precision > 96%; in-vehicle travel: sensitivity > 71%, specificity > 99%, and precision > 88%), but the performance was moderate for outdoor static and outdoor walking predictions. No striking differences in performance were observed between the rule-based and the random forest models. The random forest model was fast and easy to execute, but was likely less robust

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.

Pubertal development, spare time activities, and adolescent delinquency: testing the contextual amplification hypothesis.

PubMed

Kretschmer, Tina; Oliver, Bonamy R; Maughan, Barbara

2014-08-01

Extensive evidence supports associations between early pubertal timing and adolescent externalizing behavior, but how and under which conditions they are linked is not fully understood. In addition, pubertal development is also characterized by variations in the relative speed at which individuals mature, but studies linking pubertal 'tempo' and outcomes are scarce. This study examined the mediating and moderating roles of spare time activities in associations between pubertal development and later delinquency, using data from a large (4,327 girls, 4,250 boys) longitudinal UK cohort (Avon Longitudinal Study of Parents and Children). Self-reports of Tanner stage were available from ages 9 to 14, spare time activities at age 12 and delinquency at age 15. Pubertal development was examined using latent growth models. Spare time activities were categorized using factor analyses, yielding four types (hanging out at home, hanging out outside, consumerist behavior, and sports/games), which were examined as mediators and moderators. Earlier and faster maturation predicted delinquency in boys and girls. Spare time activities partially mediated these links such that early maturing girls more often engaged in hanging out outside, which placed them at greater risk for delinquency. In addition, compared to their later and slower maturing counterparts, boys who matured earlier and faster were less likely to engage in sports/games, a spare time activity type that is linked to lower delinquency risk. No moderation effects were found. The findings extend previous research on outcomes of early maturation and show how spare time activities act as proxies between pubertal development and delinquency.

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.

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…

Gross-alpha and gross-beta activities in airborne particulate samples. Analysis and prediction models.

PubMed

Dueñas, C; Fernández, M C; Carretero, J; Liger, E; Cañete, S

2001-04-01

Measurements of gross-alpha and gross-beta activities were made every week during the years 1992-1997 for airborne particulate samples collected using air filters at a clear site. The data are sufficiently numerous to allow the examination of variations in time and by these measurements to establish several features that should be important in understanding any trends of atmospheric radioactivity. Two models were used to predict the gross-alpha and gross-beta activities. A good agreement between the results of these models and the measurements was highlighted.

Accurate prediction of energy expenditure using a shoe-based activity monitor.

PubMed

Sazonova, Nadezhda; Browning, Raymond C; Sazonov, Edward

2011-07-01

The aim of this study was to develop and validate a method for predicting energy expenditure (EE) using a footwear-based system with integrated accelerometer and pressure sensors. We developed a footwear-based device with an embedded accelerometer and insole pressure sensors for the prediction of EE. The data from the device can be used to perform accurate recognition of major postures and activities and to estimate EE using the acceleration, pressure, and posture/activity classification information in a branched algorithm without the need for individual calibration. We measured EE via indirect calorimetry as 16 adults (body mass index=19-39 kg·m) performed various low- to moderate-intensity activities and compared measured versus predicted EE using several models based on the acceleration and pressure signals. Inclusion of pressure data resulted in better accuracy of EE prediction during static postures such as sitting and standing. The activity-based branched model that included predictors from accelerometer and pressure sensors (BACC-PS) achieved the lowest error (e.g., root mean squared error (RMSE)=0.69 METs) compared with the accelerometer-only-based branched model BACC (RMSE=0.77 METs) and nonbranched model (RMSE=0.94-0.99 METs). Comparison of EE prediction models using data from both legs versus models using data from a single leg indicates that only one shoe needs to be equipped with sensors. These results suggest that foot acceleration combined with insole pressure measurement, when used in an activity-specific branched model, can accurately estimate the EE associated with common daily postures and activities. The accuracy and unobtrusiveness of a footwear-based device may make it an effective physical activity monitoring tool.

Uncertainty estimation of predictions of peptides' chromatographic retention times in shotgun proteomics.

PubMed

Maboudi Afkham, Heydar; Qiu, Xuanbin; The, Matthew; Käll, Lukas

2017-02-15

Liquid chromatography is frequently used as a means to reduce the complexity of peptide-mixtures in shotgun proteomics. For such systems, the time when a peptide is released from a chromatography column and registered in the mass spectrometer is referred to as the peptide's retention time . Using heuristics or machine learning techniques, previous studies have demonstrated that it is possible to predict the retention time of a peptide from its amino acid sequence. In this paper, we are applying Gaussian Process Regression to the feature representation of a previously described predictor E lude . Using this framework, we demonstrate that it is possible to estimate the uncertainty of the prediction made by the model. Here we show how this uncertainty relates to the actual error of the prediction. In our experiments, we observe a strong correlation between the estimated uncertainty provided by Gaussian Process Regression and the actual prediction error. This relation provides us with new means for assessment of the predictions. We demonstrate how a subset of the peptides can be selected with lower prediction error compared to the whole set. We also demonstrate how such predicted standard deviations can be used for designing adaptive windowing strategies. lukas.kall@scilifelab.se. Our software and the data used in our experiments is publicly available and can be downloaded from https://github.com/statisticalbiotechnology/GPTime . © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Cortical activity patterns predict robust speech discrimination ability in noise

PubMed Central

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

2012-01-01

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

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.

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.

An improved grey model for the prediction of real-time GPS satellite clock bias

NASA Astrophysics Data System (ADS)

Zheng, Z. Y.; Chen, Y. Q.; Lu, X. S.

2008-07-01

In real-time GPS precise point positioning (PPP), real-time and reliable satellite clock bias (SCB) prediction is a key to implement real-time GPS PPP. It is difficult to hold the nuisance and inenarrable performance of space-borne GPS satellite atomic clock because of its high-frequency, sensitivity and impressionable, it accords with the property of grey model (GM) theory, i. e. we can look on the variable process of SCB as grey system. Firstly, based on limits of quadratic polynomial (QP) and traditional GM to predict SCB, a modified GM (1,1) is put forward to predict GPS SCB in this paper; and then, taking GPS SCB data for example, we analyzed clock bias prediction with different sample interval, the relationship between GM exponent and prediction accuracy, precision comparison of GM to QP, and concluded the general rule of different type SCB and GM exponent; finally, to test the reliability and validation of the modified GM what we put forward, taking IGS clock bias ephemeris product as reference, we analyzed the prediction precision with the modified GM, It is showed that the modified GM is reliable and validation to predict GPS SCB and can offer high precise SCB prediction for real-time GPS PPP.

Towards an Online Seizure Advisory System-An Adaptive Seizure Prediction Framework Using Active Learning Heuristics.

PubMed

Karuppiah Ramachandran, Vignesh Raja; Alblas, Huibert J; Le, Duc V; Meratnia, Nirvana

2018-05-24

In the last decade, seizure prediction systems have gained a lot of attention because of their enormous potential to largely improve the quality-of-life of the epileptic patients. The accuracy of the prediction algorithms to detect seizure in real-world applications is largely limited because the brain signals are inherently uncertain and affected by various factors, such as environment, age, drug intake, etc., in addition to the internal artefacts that occur during the process of recording the brain signals. To deal with such ambiguity, researchers transitionally use active learning, which selects the ambiguous data to be annotated by an expert and updates the classification model dynamically. However, selecting the particular data from a pool of large ambiguous datasets to be labelled by an expert is still a challenging problem. In this paper, we propose an active learning-based prediction framework that aims to improve the accuracy of the prediction with a minimum number of labelled data. The core technique of our framework is employing the Bernoulli-Gaussian Mixture model (BGMM) to determine the feature samples that have the most ambiguity to be annotated by an expert. By doing so, our approach facilitates expert intervention as well as increasing medical reliability. We evaluate seven different classifiers in terms of the classification time and memory required. An active learning framework built on top of the best performing classifier is evaluated in terms of required annotation effort to achieve a high level of prediction accuracy. The results show that our approach can achieve the same accuracy as a Support Vector Machine (SVM) classifier using only 20 % of the labelled data and also improve the prediction accuracy even under the noisy condition.

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.

Association of parents' and children's physical activity and sedentary time in Year 4 (8-9) and change between Year 1 (5-6) and Year 4: a longitudinal study.

PubMed

Jago, Russell; Solomon-Moore, Emma; Macdonald-Wallis, Corrie; Thompson, Janice L; Lawlor, Deborah A; Sebire, Simon J

2017-08-17

Parents could be important influences on child physical activity and parents are often encouraged to be more active with their child. This paper examined the association between parent and child physical activity and sedentary time in a UK cohort of children assessed when the children were in Year 1 (5-6 years old) and in Year 4 (8-9 years old). One thousand two hundred twenty three children and parents provided data in Year 4 and of these 685 participated in Year 1. Children and parents wore an accelerometer for five days including a weekend. Mean minutes of sedentary time and moderate-to-vigorous intensity physical activity (MVPA) were derived. Multiple imputation was used to impute all missing data and create complete datasets. Linear regression models examined whether parent MVPA and sedentary time at Year 4 and at Year 1 predicted child MVPA and sedentary time at Year 4. Change in parent MVPA and sedentary time was used to predict change in child MVPA and sedentary time between Year 1 and Year 4. Imputed data showed that at Year 4, female parent sedentary time was associated with child sedentary time (0.13, 95% CI = 0.00 to 0.27 mins/day), with a similar association for male parents (0.15, 95% CI = -0.02 to 0.32 mins/day). Female parent and child MVPA at Year 4 were associated (0.16, 95% CI = 0.08 to 0.23 mins/day) with a smaller association for male parents (0.08, 95% CI = -0.01 to 0.17 mins/day). There was little evidence that either male or female parent MVPA at Year 1 predicted child MVPA at Year 4 with similar associations for sedentary time. There was little evidence that change in parent MVPA or sedentary time predicted change in child MVPA or sedentary time respectively. Parents who were more physically active when their child was 8-9 years old had a child who was more active, but the magnitude of association was generally small. There was little evidence that parental activity from three years earlier predicted child activity at age 8-9, or

Prediction of retention times in comprehensive two-dimensional gas chromatography using thermodynamic models.

PubMed

McGinitie, Teague M; Harynuk, James J

2012-09-14

A method was developed to accurately predict both the primary and secondary retention times for a series of alkanes, ketones and alcohols in a flow-modulated GC×GC system. This was accomplished through the use of a three-parameter thermodynamic model where ΔH, ΔS, and ΔC(p) for an analyte's interaction with the stationary phases in both dimensions are known. Coupling this thermodynamic model with a time summation calculation it was possible to accurately predict both (1)t(r) and (2)t(r) for all analytes. The model was able to predict retention times regardless of the temperature ramp used, with an average error of only 0.64% for (1)t(r) and an average error of only 2.22% for (2)t(r). The model shows promise for the accurate prediction of retention times in GC×GC for a wide range of compounds and is able to utilize data collected from 1D experiments. Copyright © 2012 Elsevier B.V. All rights reserved.

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.

Activity in the human brain predicting differential heart rate responses to emotional facial expressions.

PubMed

Critchley, Hugo D; Rotshtein, Pia; Nagai, Yoko; O'Doherty, John; Mathias, Christopher J; Dolan, Raymond J

2005-02-01

The James-Lange theory of emotion proposes that automatically generated bodily reactions not only color subjective emotional experience of stimuli, but also necessitate a mechanism by which these bodily reactions are differentially generated to reflect stimulus quality. To examine this putative mechanism, we simultaneously measured brain activity and heart rate to identify regions where neural activity predicted the magnitude of heart rate responses to emotional facial expressions. Using a forewarned reaction time task, we showed that orienting heart rate acceleration to emotional face stimuli was modulated as a function of the emotion depicted. The magnitude of evoked heart rate increase, both across the stimulus set and within each emotion category, was predicted by level of activity within a matrix of interconnected brain regions, including amygdala, insula, anterior cingulate, and brainstem. We suggest that these regions provide a substrate for translating visual perception of emotional facial expression into differential cardiac responses and thereby represent an interface for selective generation of visceral reactions that contribute to the embodied component of emotional reaction.

Window-Based Channel Impulse Response Prediction for Time-Varying Ultra-Wideband Channels.

PubMed

Al-Samman, A M; Azmi, M H; Rahman, T A; Khan, I; Hindia, M N; Fattouh, A

2016-01-01

This work proposes channel impulse response (CIR) prediction for time-varying ultra-wideband (UWB) channels by exploiting the fast movement of channel taps within delay bins. Considering the sparsity of UWB channels, we introduce a window-based CIR (WB-CIR) to approximate the high temporal resolutions of UWB channels. A recursive least square (RLS) algorithm is adopted to predict the time evolution of the WB-CIR. For predicting the future WB-CIR tap of window wk, three RLS filter coefficients are computed from the observed WB-CIRs of the left wk-1, the current wk and the right wk+1 windows. The filter coefficient with the lowest RLS error is used to predict the future WB-CIR tap. To evaluate our proposed prediction method, UWB CIRs are collected through measurement campaigns in outdoor environments considering line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. Under similar computational complexity, our proposed method provides an improvement in prediction errors of approximately 80% for LOS and 63% for NLOS scenarios compared with a conventional method.

Window-Based Channel Impulse Response Prediction for Time-Varying Ultra-Wideband Channels

PubMed Central

Al-Samman, A. M.; Azmi, M. H.; Rahman, T. A.; Khan, I.; Hindia, M. N.; Fattouh, A.

2016-01-01

This work proposes channel impulse response (CIR) prediction for time-varying ultra-wideband (UWB) channels by exploiting the fast movement of channel taps within delay bins. Considering the sparsity of UWB channels, we introduce a window-based CIR (WB-CIR) to approximate the high temporal resolutions of UWB channels. A recursive least square (RLS) algorithm is adopted to predict the time evolution of the WB-CIR. For predicting the future WB-CIR tap of window wk, three RLS filter coefficients are computed from the observed WB-CIRs of the left wk−1, the current wk and the right wk+1 windows. The filter coefficient with the lowest RLS error is used to predict the future WB-CIR tap. To evaluate our proposed prediction method, UWB CIRs are collected through measurement campaigns in outdoor environments considering line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. Under similar computational complexity, our proposed method provides an improvement in prediction errors of approximately 80% for LOS and 63% for NLOS scenarios compared with a conventional method. PMID:27992445

Prospective versus predictive control in timing of hitting a falling ball.

PubMed

Katsumata, Hiromu; Russell, Daniel M

2012-02-01

Debate exists as to whether humans use prospective or predictive control to intercept an object falling under gravity (Baurès et al. in Vis Res 47:2982-2991, 2007; Zago et al. in Vis Res 48:1532-1538, 2008). Prospective control involves using continuous information to regulate action. τ, the ratio of the size of the gap to the rate of gap closure, has been proposed as the information used in guiding interceptive actions prospectively (Lee in Ecol Psychol 10:221-250, 1998). This form of control is expected to generate movement modulation, where variability decreases over the course of an action based upon more accurate timing information. In contrast, predictive control assumes that a pre-programmed movement is triggered at an appropriate criterion timing variable. For a falling object it is commonly argued that an internal model of gravitational acceleration is used to predict the motion of the object and determine movement initiation. This form of control predicts fixed duration movements initiated at consistent time-to-contact (TTC), either across conditions (constant criterion operational timing) or within conditions (variable criterion operational timing). The current study sought to test predictive and prospective control hypotheses by disrupting continuous visual information of a falling ball and examining consistency in movement initiation and duration, and evidence for movement modulation. Participants (n = 12) batted a ball dropped from three different heights (1, 1.3 and 1.5 m), under both full-vision and partial occlusion conditions. In the occlusion condition, only the initial ball drop and the final 200 ms of ball flight to the interception point could be observed. The initiation of the swing did not occur at a consistent TTC, τ, or any other timing variable across drop heights, in contrast with previous research. However, movement onset was not impacted by occluding the ball flight for 280-380 ms. This finding indicates that humans did not

The Role of Present Time Perspective in Predicting Early Adolescent Violence.

PubMed

Kruger, Daniel J; Carrothers, Jessica; Franzen, Susan P; Miller, Alison L; Reischl, Thomas M; Stoddard, Sarah A; Zimmerman, Marc A

2018-06-01

This study investigated the role of present and future time perspectives, and their relationships with subjective norms and beliefs regarding violence, in predicting violent behaviors among urban middle school students in the Midwestern United States. Although present time perspective covaried with subjective norms and beliefs, each made a unique prediction of self-reported violent behaviors. Future time perspective was not a significant predictor when accounting for these relationships. In addition, present orientation moderated the relationship between subjective norms and beliefs and rates of violent behaviors; those with higher present orientations exhibited stronger associations. We replicated this pattern of results in data from new participants in a subsequent wave of the study. Interventions that explicitly address issues related to time perspective may be effective in reducing early adolescent violence.

Role of enhanced synoptic activity and its interaction with intra-seasonal oscillations on the lower extended range prediction skill during 2015 monsoon season

NASA Astrophysics Data System (ADS)

Abhilash, S.; Mandal, R.; Dey, A.; Phani, R.; Joseph, S.; Chattopadhyay, R.; De, S.; Agarwal, N. K.; Sahai, A. K.; Devi, S. Sunitha; Rajeevan, M.

2018-01-01

Indian summer monsoon of 2015 was deficient with prominence of short-lived (long-lived) active (break) spells. The real-time extended range forecasts disseminated by Indian Institute of Tropical Meteorology using an indigenous ensemble prediction system (EPS) based on National Center for Environmental Predictions's climate forecast system could broadly predict these intraseasonal fluctuations at shorter time leads (i.e. up to 10 days), but failed to predict at longer leads (15-20 days). Considering the multi-scale nature of Indian Summer Monsoon system, this particular study aims to examine the inability of the EPS in predicting the active/break episodes at longer leads from the perspective of non-linear scale interaction between the synoptic, intraseasonal and seasonal scale. It is found that the 2015 monsoon season was dominated by synoptic scale disturbances that can hinder the prediction on extended range. Further, the interaction between synoptic scale disturbances and low frequency mode was prominent during the season, which might have contributed to the reduced prediction skill at longer leads.

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

PubMed

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

2016-01-01

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

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…

Real-time Upstream Monitoring System: Using ACE Data to Predict the Arrival of Interplanetary Shocks

NASA Astrophysics Data System (ADS)

Donegan, M. M.; Wagstaff, K. L.; Ho, G. C.; Vandegriff, J.

2003-12-01

We have developed an algorithm to predict Earth arrival times for interplanetary (IP) shock events originating at the Sun. Our predictions are generated from real-time data collected by the Electron, Proton, and Alpha Monitor (EPAM) instrument on NASA's Advanced Composition Explorer (ACE) spacecraft. The high intensities of energetic ions that occur prior to and during an IP shock pose a radiation hazard to astronauts as well as to electronics in Earth orbit. The potential to predict such events is based on characteristic signatures in the Energetic Storm Particle (ESP) event ion intensities which are often associated with IP shocks. We have previously reported on the development and implementation of an algorithm to forecast the arrival of ESP events. Historical ion data from ACE/EPAM was used to train an artificial neural network which uses the signature of an approaching event to predict the time remaining until the shock arrives. Tests on the trained network have been encouraging, with an average error of 9.4 hours for predictions made 24 hours in advance, and an reduced average error of 4.9 hours when the shock is 12 hours away. The prediction engine has been integrated into a web-based system that uses real-time ACE/EPAM data provided by the NOAA Space Environment Center (http://sd-www.jhuapl.edu/UPOS/RISP/ index.html.) This system continually processes the latest ACE data, reports whether or not there is an impending shock, and predicts the time remaining until the shock arrival. Our predictions are updated every five minutes and provide significant lead-time, thereby supplying critical information that can be used by mission planners, satellite operations controllers, and scientists. We have continued to refine the prediction capabilities of this system; in addition to forecasting arrival times for shocks, we now provide confidence estimates for those predictions.

Long-Term Prediction of the Arctic Ionospheric TEC Based on Time-Varying Periodograms

PubMed Central

Liu, Jingbin; Chen, Ruizhi; Wang, Zemin; An, Jiachun; Hyyppä, Juha

2014-01-01

Knowledge of the polar ionospheric total electron content (TEC) and its future variations is of scientific and engineering relevance. In this study, a new method is developed to predict Arctic mean TEC on the scale of a solar cycle using previous data covering 14 years. The Arctic TEC is derived from global positioning system measurements using the spherical cap harmonic analysis mapping method. The study indicates that the variability of the Arctic TEC results in highly time-varying periodograms, which are utilized for prediction in the proposed method. The TEC time series is divided into two components of periodic oscillations and the average TEC. The newly developed method of TEC prediction is based on an extrapolation method that requires no input of physical observations of the time interval of prediction, and it is performed in both temporally backward and forward directions by summing the extrapolation of the two components. The backward prediction indicates that the Arctic TEC variability includes a 9 years period for the study duration, in addition to the well-established periods. The long-term prediction has an uncertainty of 4.8–5.6 TECU for different period sets. PMID:25369066

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

Study on real-time elevator brake failure predictive system

NASA Astrophysics Data System (ADS)

Guo, Jun; Fan, Jinwei

2013-10-01

This paper presented a real-time failure predictive system of the elevator brake. Through inspecting the running state of the coil by a high precision long range laser triangulation non-contact measurement sensor, the displacement curve of the coil is gathered without interfering the original system. By analyzing the displacement data using the diagnostic algorithm, the hidden danger of the brake system can be discovered in time and thus avoid the according accident.

Real coded genetic algorithm for fuzzy time series prediction

NASA Astrophysics Data System (ADS)

Jain, Shilpa; Bisht, Dinesh C. S.; Singh, Phool; Mathpal, Prakash C.

2017-10-01

Genetic Algorithm (GA) forms a subset of evolutionary computing, rapidly growing area of Artificial Intelligence (A.I.). Some variants of GA are binary GA, real GA, messy GA, micro GA, saw tooth GA, differential evolution GA. This research article presents a real coded GA for predicting enrollments of University of Alabama. Data of Alabama University is a fuzzy time series. Here, fuzzy logic is used to predict enrollments of Alabama University and genetic algorithm optimizes fuzzy intervals. Results are compared to other eminent author works and found satisfactory, and states that real coded GA are fast and accurate.

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

PubMed

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

2016-06-01

Two electrophysiological components have been extensively investigated as candidate neural correlates of perceptual consciousness: An early, occipitally realized component occurring 130-320 ms after stimulus onset and a late, frontally realized component occurring 320-510 ms after stimulus onset. Recent studies have suggested that the late component may not be uniquely related to perceptual consciousness, but also to sensory expectations, task associations, and selective attention. We conducted a magnetoencephalographic study; using multivariate analysis, we compared classification accuracies when decoding perceptual consciousness from the 2 components using sources from occipital and frontal lobes. We found that occipital sources during the early time range were significantly more accurate in decoding perceptual consciousness than frontal sources during both the early and late time ranges. These results are the first of its kind where the predictive values of the 2 components are quantitatively compared, and they provide further evidence for the primary importance of occipital sources in realizing perceptual consciousness. The results have important consequences for current theories of perceptual consciousness, especially theories emphasizing the role of frontal sources. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Sustained anterior cingulate cortex activation during reward processing predicts response to psychotherapy in major depressive disorder.

PubMed

Carl, Hannah; Walsh, Erin; Eisenlohr-Moul, Tory; Minkel, Jared; Crowther, Andrew; Moore, Tyler; Gibbs, Devin; Petty, Chris; Bizzell, Josh; Dichter, Gabriel S; Smoski, Moria J

2016-10-01

The purpose of the present investigation was to evaluate whether pre-treatment neural activation in response to rewards is a predictor of clinical response to Behavioral Activation Therapy for Depression (BATD), an empirically validated psychotherapy that decreases depressive symptoms by increasing engagement with rewarding stimuli and reducing avoidance behaviors. Participants were 33 outpatients with major depressive disorder (MDD) and 20 matched controls. We examined group differences in activation, and the capacity to sustain activation, across task runs using functional magnetic resonance imaging (fMRI) and the monetary incentive delay (MID) task. Hierarchical linear modeling was used to investigate whether pre-treatment neural responses predicted change in depressive symptoms over the course of BATD treatment. MDD and Control groups differed in sustained activation during reward outcomes in the right nucleus accumbens, such that the MDD group experienced a significant decrease in activation in this region from the first to second task run relative to controls. Pretreatment anhedonia severity and pretreatment task-related reaction times were predictive of response to treatment. Furthermore, sustained activation in the anterior cingulate cortex during reward outcomes predicted response to psychotherapy; patients with greater sustained activation in this region were more responsive to BATD treatment. The current study only included a single treatment condition, thus it unknown whether these predictors of treatment response are specific to BATD or psychotherapy in general. Findings add to the growing body of literature suggesting that the capacity to sustain neural responses to rewards may be a critical endophenotype of MDD. Copyright © 2016 Elsevier B.V. All rights reserved.

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.

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.