Varying Time Windows Research Articles

Revealing Inconsistencies in ROTI Index Using Multi‐GNSS Constellation Measurements: Impact of Sampling Rates and Time Window

AbstractUnderstanding ionospheric irregularities and their dynamics is crucial, with the rate of change of the total electron content index (ROTI) serving as a significant metric for this purpose. However, inconsistencies in ROTI magnitudes have been noted when sampled at one‐second interval across various Global Navigation Satellite System (GNSS) receivers. This study presents a detailed statistical analysis to investigate inconsistencies in ROTI using multi‐GNSS observations in conjunction with four distinct GNSS receiver types. Various factors affecting the ROTI inconsistencies among receivers were examined, including differences in sampling rates (1, 5, 15, and 30 s), and varying time window widths (1, 2, and 5 min). By examination of data obtained from GNSS receivers with zero or short baselines on a global scale, the analysis uncovered substantial variations in multi‐GNSS ROTI values across the four assessed receiver types. The main findings suggest that reducing the sampling rate reduces the inconsistencies in the magnitude of ROTI, particularly at lower sampling rates. This reduction can be attributed to the exclusion of high‐frequency components in the ROTI spectrum. Interestingly, the width of the time window is found to have minimal impact on the ROTI magnitude. The study also shows a direct correlation between a larger magnitude of ROTI and the increased noise in the signals tracked by receivers. These results emphasize the importance of considering sampling rates and GNSS receiver types when utilizing ROTI to investigate ionospheric irregularities.

Development, External Validation, and Biomolecular Corroboration of Interoperable Models for Identifying Critically Ill Children at Risk of Neurologic Morbidity.

Declining mortality in the field of pediatric critical care medicine has shifted practicing clinicians' attention to preserving patients' neurodevelopmental potential as a main objective. Earlier identification of critically ill children at risk for incurring neurologic morbidity would facilitate heightened surveillance that could lead to timelier clinical detection, earlier interventions, and preserved neurodevelopmental trajectory. Develop machine-learning models for identifying acquired neurologic morbidity while hospitalized with critical illness and assess correlation with contemporary serum-based, brain injury-derived biomarkers. Retrospective cohort study. Two large, quaternary children's hospitals. Critical illness. The outcome was neurologic morbidity, defined according to a computable, composite definition at the development site or an order for neurocritical care consultation at the validation site. Models were developed using varying time windows for temporal feature engineering and varying censored time horizons prior to identified neurologic morbidity. Optimal models were selected based on F1 scores, cohort sizes, calibration, and data availability for eventual deployment. A generalizable created at the development site was assessed at an external validation site and optimized with spline recalibration. Correlation was assessed between development site model predictions and measurements of brain biomarkers from a convenience cohort. After exclusions there were 14,222-25,171 encounters from 2010-2022 in the development site cohorts and 6,280-6,373 from 2018-2021 in the validation site cohort. At the development site, an extreme gradient boosted model (XGBoost) with a 12-hour time horizon and 48-hour feature engineering window had an F1-score of 0.54, area under the receiver operating characteristics curve (AUROC) of 0.82, and a number needed to alert (NNA) of 2. A generalizable XGBoost model with a 24-hour time horizon and 48-hour feature engineering window demonstrated an F1-score of 0.37, AUROC of 0.81, AUPRC of 0.51, and NNA of 4 at the validation site. After recalibration at the validation site, the Brier score was 0.04. Serum levels of the brain injury biomarker glial fibrillary acidic protein measurements significantly correlated with model output (r s =0.34; P =0.007). We demonstrate a well-performing ensemble of models for predicting neurologic morbidity in children with biomolecular corroboration. Prospective assessment and refinement of biomarker-coupled risk models in pediatric critical illness is warranted. Question Can interoperable models for predicting neurological deterioration in critically ill children be developed, correlated with serum-based brain-derived biomarkers, and validated at an external site? Findings A development site model demonstrated an area under the receiver operating characteristics curve (AUROC) of 0.82 and a number needed to alert (NNA) of 2. Predictions correlated with levels of glial fibrillary acidic protein in a subset of children. A generalizable model demonstrated an AUROC of 0.81 and NNA of 4 at the validation site. Meaning Well performing prediction models coupled with brain biomarkers may help to identify critically ill children at risk for acquired neurological morbidity.

Flexibility assessment and aggregation of alkaline electrolyzers considering dynamic process constraints for energy management of renewable power-to-hydrogen systems

In the energy management of renewable power-to-hydrogen (ReP2H) systems, quantifying the flexibility of alkaline electrolyzers (AELs) to respond to fast load-tracking commands and maintain energy balance across various timescales is crucial. However, the flexibility of AELs is subject to electrochemical, and dynamic heat and mass transfer constraints, making them very different, both physically and mathematically, from conventional power system flexibility resources such as energy storage (ES) and electrical vehicles (EVs). Additionally, in large hydrogen plants (HPs), aggregating the flexibility of multiple AELs is necessary. Despite extensive flexibility-related research on power and integrated energy systems, work on the electrolyzers is limited. To address these gaps, this paper presents a flexibility assessment method for AELs. Considering comprehensive nonlinear dynamic process constraints, we establish a flexibility metric based on the maximal/minimal energy an AEL can provide within varying time windows. This metric is additive, facilitating the aggregation of multiple AELs within seconds. By comparing it with the energy demanded by regulatory commands, the feasibility of load-tracking control can be assessed without time-domain simulation. In cases of infeasibility, the cause and required compensation can also be interpreted. Case studies validate the proposed method, and key factors impacting the flexibility of AELs are quantitatively analyzed.

Optimal policies for nutrition administration to very low birth weight infants

AbstractVery low birth weight (VLBW) infants (birth weight 1500 grams) are at risk of postnatal growth restriction. Understanding how nutrition is associated with growth and how these associations vary based on infant characteristics and comorbidities is important to reduce postnatal growth restriction. We propose a three‐step analytical framework: (i) We use unsupervised Clustering techniques to identify subgroups within a cohort of VLBW infants based on infant characteristics, diagnoses, and treatments. (ii) For each cluster, we use Multilevel Modeling to explore the associations between calorie or protein intake and growth velocity (GV) for varying time windows. (iii) We build Mixed‐Integer Programming Models to achieve simple rule‐based policies that physicians can use to classify infants into one of the identified subgroups. We use electronic health records from VLBW infants at Lurie Children's Hospital in Chicago, IL, born between 2011 and 2014. We find that clustering separates infants into two clusters, with Cluster 1 having smaller infants with more comorbidities than Cluster 2. Initial clustering on only sex and birth weight provides results similar to clustering on later‐life diagnoses and treatments. Multilevel models with Clustering provide better model fit than models without clustering. For Cluster 1, there is a significant association between GV and protein but not calories. For Cluster 2, both protein and calories are individually associated with growth. We develop accurate and sparse scoring systems to help clinicians identify infants at higher risk of growth restriction and consider nutrition regimens accordingly.

Deep learning-based activity-aware 3D human motion trajectory prediction in construction

Predicting human motion is a critical requirement in various applications, with particular significance in the construction sector. This task presents significant challenges due to the diverse nature of human actions and the complexities of converting 2D image coordinates to real-world space. In response to these challenges, this paper introduces an innovative deep learning-driven approach to forecast human motion trajectories, with a novel emphasis on activity recognition to improve predictive accuracy. The method utilizes a multi-camera system to extract 2D joint locations, which are then fused using a particle filter technique for 3D pose generation. Using 3D data, deep learning models are developed to first recognize the activity class, and then take it as auxiliary information for predicting the motion trajectory. Through a comprehensive experiment, we evaluated the proposed methodology. While the main innovation of the proposed approach lies in the incorporation of deep learning-based activity recognition into the trajectory prediction system, the experiment results revealed the activity-aware system’s capability to enhance prediction performance by a minimum of 6.4% and up to 16.6% in short-term forecasts, in compare with a conventional approach. Additionally, we analyzed the effects of varying time windows and joint selections on predictive outcomes across diverse scenarios and discussed the implications of these findings. By enhancing the prediction of human motion, this approach holds promise in improving workspace safety while encouraging effective interactions within complex environments.

Predicting Driver Takeover Decisions in Conditionally Automated Vehicles with a Gaze-based Deep Learning Model

This study proposes a novel methodology for modeling driver takeover behavior in conditionally automated vehicles (AVs) when exiting a freeway using deep learning (DL) network architectures. While previous research has focused on modeling takeover time in emergency scenarios, which require quick responses, these models may not be applicable to scheduled, non-time-critical takeovers. In such situations, drivers may employ varying strategies and take longer to resume control of the vehicle when there is no time pressure. To address this problem, a deep learning architecture based on a convolutional neural network (CNN) was implemented to predict drivers’ takeover behaviors in scheduled takeovers. The model was trained on drivers’ driving data and eye gaze with varying time windows, facilitating an analysis of drivers’ takeover decisions to various takeover request designs. The model achieved good performance metrics, with an F1 Score of 0.993, a recall of 0.996, and a precision of 0.991. The application of these models holds substantial potential for refining the design of the human-machine interface, specifically in calibrating the takeover request (ToR) lead time, thereby promoting safe freeway exiting takeovers in conditionally AVs.

Distributed Intelligence in Autonomous PEM Fuel Cell Control

A combination of perceptive and deliberative processes is necessary to ensure the efficient and autonomous control of proton exchange membrane fuel cells (PEMFCs) under optimal humidification conditions. These processes enable monitoring and control tasks across various application scenarios and operating conditions. Consequently, it becomes crucial to adjust parameter values corresponding to different states of the PEMFC during its operation. In this context, this work presents the design and development of an architecture for the control and management of a PEMFC with a maximum power output of 500 [W] based on intelligent agents operating under optimal conditions (membrane humidification). The proposed architecture integrates perception and action algorithms that leverage sensory and contextual information using heuristic algorithms. It adopts a hierarchical structure with distinct layers, each featuring varying time windows and levels of abstraction. Notably, this architecture demonstrates its effectiveness in achieving the desired energy efficiency objective, as evidenced by successful validation tests conducted with different electrical power values delivered by the fuel cell, encompassing three distinct operating states (dry, normal, and flooded). An exemplary application of this scheme is the dynamic control of the humidification of the polymeric membrane, which further highlights the capabilities of this architecture.

Sensitivity of Electrodermal Activity Features for Driver Arousal Measurement in Cognitive Load: The Application in Automated Driving Systems

Driver’s under-arousal occurred in automated driving systems (ADS) impairs takeover safety. This study aims to determine electrodermal activity (EDA) features’ importance for driver’s arousal quantification. A car-following simulator study was conducted with participants concurrently executing four levels of cognitive tasks, triggering four levels of arousal. Participants’ skin conductance (SC) data were collected and decomposed into tonic (skin conductance level, SCL) and phasic (skin conductance response, SCR) components. Seventeen features extracted from SC, SCL and SCR were compared. As a result, SCR-relevant features showed higher significance and larger effect size than SC and SCL features in response to cognitive load, which suggests the phasic component dominates changes in EDA under varying cognitive load. Moreover, the SCR rate TTP.nSCRs, identified by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.03 ~\mu \text{S}$ </tex-math></inline-formula> thresholds, attained the largest effect size among all features for driver’s arousal measurement. A varying time windows (TW) analysis showed that TTP.nSCRs was the most suggested arousal metric when TW was over 20 s, whereas the sum of SCRs amplitudes TTP.AmpSum was preferred when TW was less than 20 s. For driver’s arousal quantification with multi-features, the top five suggested features were TTP.nSCRs, SC_Rate5, CDA.SCR (or CDA.ISCR), CDA.AmpSum, and TTP.AmpSum. Although male drivers showed higher values of EDA features than female drivers, the sensitivity of the proposed EDA features stands across gender and individuals. This study promotes an improved understanding of EDA changes in human cognitive process. The sensitive EDA features proposed could be used from uni- or multi-modalities in driver state management and takeover-safety prediction for ADS.

Alignment Between Heart Rate Variability From Fitness Trackers and Perceived Stress: Perspectives From a Large-Scale In Situ Longitudinal Study of Information Workers.

BackgroundStress can have adverse effects on health and well-being. Informed by laboratory findings that heart rate variability (HRV) decreases in response to an induced stress response, recent efforts to monitor perceived stress in the wild have focused on HRV measured using wearable devices. However, it is not clear that the well-established association between perceived stress and HRV replicates in naturalistic settings without explicit stress inductions and research-grade sensors.ObjectiveThis study aims to quantify the strength of the associations between HRV and perceived daily stress using wearable devices in real-world settings.MethodsIn the main study, 657 participants wore a fitness tracker and completed 14,695 ecological momentary assessments (EMAs) assessing perceived stress, anxiety, positive affect, and negative affect across 8 weeks. In the follow-up study, approximately a year later, 49.8% (327/657) of the same participants wore the same fitness tracker and completed 1373 EMAs assessing perceived stress at the most stressful time of the day over a 1-week period. We used mixed-effects generalized linear models to predict EMA responses from HRV features calculated over varying time windows from 5 minutes to 24 hours.ResultsAcross all time windows, the models explained an average of 1% (SD 0.5%; marginal R2) of the variance. Models using HRV features computed from an 8 AM to 6 PM time window (namely work hours) outperformed other time windows using HRV features calculated closer to the survey response time but still explained a small amount (2.2%) of the variance. HRV features that were associated with perceived stress were the low frequency to high frequency ratio, very low frequency power, triangular index, and SD of the averages of normal-to-normal intervals. In addition, we found that although HRV was also predictive of other related measures, namely, anxiety, negative affect, and positive affect, it was a significant predictor of stress after controlling for these other constructs. In the follow-up study, calculating HRV when participants reported their most stressful time of the day was less predictive and provided a worse fit (R2=0.022) than the work hours time window (R2=0.032).ConclusionsA significant but small relationship between perceived stress and HRV was found. Thus, although HRV is associated with perceived stress in laboratory settings, the strength of that association diminishes in real-life settings. HRV might be more reflective of perceived stress in the presence of specific and isolated stressors and research-grade sensing. Relying on wearable-derived HRV alone might not be sufficient to detect stress in naturalistic settings and should not be considered a proxy for perceived stress but rather a component of a complex phenomenon.

Stem Radial Growth Is Negatively Related to Tree Defoliation and Damage in Conifers, Northern Italy

Although several manipulative experiments provided evidence for a negative effect of defoliation on tree growth, results from observational studies were less univocal. This may be due to the ability of observed defoliation to reflect the health status of individual trees, to the influence of site condition and to the amplitude of time window used for growth assessment. Here, we investigated the relationship between two tree health indicators (crown defoliation, damage symptoms) and annual (measured by tree-ring width on 69 Norway spruce trees) and periodical (5-year and 10-year diameter increments, 346 trees from five coniferous species) tree growth. Data originated from 14 (seven for tree rings) ICP Forests Level I plots in Trentino, northern Italy. Diameter, defoliation and damage were measured between 1997 and 2011 as part of the annual crown condition survey, while cores for tree-rings were collected on a sub-sample of trees in 2012. We carried out regression modeling combined with model selection in one-step (periodical data) and two-step (annual data) approaches, using moving averages for the annual data with varying time window widths. Our results indicated an overall negative correlation between defoliation and annual or periodical stem diameter growth. The relationship between defoliation and growth changes in relation to the time window considered, and becomes stronger when data are aggregated over longer time windows (&amp;gt;3 years), when also the occurrence of damage symptoms plays a significant role. The effect of the amplitude of the time windows for data aggregation is probably due to the mechanisms behind the defoliation-growth relationship, which may change according to the causal factors involved. In particular, when larger time windows are considered, short-term fluctuations are likely to be smoothed out, and more general patterns may emerge. We concluded that radial growth is significantly negatively related to defoliation, and this supports the use of defoliation as a rapid indicator for forest health and vitality.

Higher Playing Times Accumulated Across Entire Games and Prior to Intense Passages Reduce the Peak Demands Reached by Elite, Junior, Male Basketball Players.

The aim of this study was to analyze the effects of different factors on the external peak demands (PD) encountered by elite, junior, male basketball players in games, including the (1) total playing time during games and (2) playing time accumulated directly prior to each PD episode. Workload variables included the PD for total distance, distance covered in different intensity zones, accelerations >2 m·s-2 (ACC), decelerations <-2 m·s-2 (DEC), and PlayerLoad. PD were calculated across different sample durations for each variable. Linear mixed models were used to identify differences in PD between groups based on playing times. PD for total distance (5-min window), high-speed running (>18 km·h-1) distance (2-min window), and ACC (30-s, 45-s, 1-min, 2-min, and 5-min windows) were significantly (p < .05) higher for players who completed lower total playing times (16.6 ± 2.4 min) than players who completed higher total playing times (25.0 ± 3.4 min). The PD for total distance (30-s, 45-s, 1-min, and 2-min windows), high-speed running distance (30-s and 5-min windows), and PlayerLoad (1-min and 2-min windows) were significantly (p < .05) higher for players who accumulated lower playing times before each PD episode than players who accumulated higher playing times before each PD episode. Players who undertake less playing time overall and prior to each PD episode can reach higher peak external loads aggregated across varied time windows. These findings can inform tactical coaching decisions during games for high external loads to be accomplished during important passages of play.

Pareto distributions in online casino gambling: Sensitivity to timeframe and associations with self exclusion

The Pareto effect (also known as the 80/20 rule) describes a skewed distribution of engagement that is observed for many products. In this study, we investigated Pareto estimates for online casino gambling, and tested their association with voluntary self-exclusion (VSE) as a marker of gambling harm, and examined their sensitivity to varying time windows. We used one year of betting data from the eCasino section of a provincially-run gambling website in British Columbia, Canada. The data contained 30,920 account holders who placed at least one bet on the platform from October 2014 to September 2015. The top 20% most engaged gamblers accounted for 92% (based on total number of bets) and 90% (based on net losses) of eCasino gambling activity over the year. The top 20% of online gamblers displayed higher levels of VSE enrolment than the remaining 80% (total bets: 13% vs 7%; net losses: 16% vs 6%, respectively). Pareto estimates increased with longer time windows, from one month to one year (total bets: 80% to 92%; net losses: 81% to 90%, respectively). This accumulation was driven by the relative loyalty of the most engaged gamblers, coupled with the influx of new and more transient gamblers on a month-to-month basis. These data strengthen links between concentrations of engagement with online products and measures of harm, but also highlight the dynamic nature of these estimates. One year estimates are preferable for estimating the degree of concentration.

Validating viral hepatitis B and C diagnosis codes: a retrospective analysis using Ontario's health administrative data.

We aimed to determine the criterion validity of using diagnosis codes for hepatitis B virus (HBV) and hepatitis C virus (HCV) to identify infections. Using linked laboratory and administrative data in Ontario, Canada, from January 2004 to December 2014, we validated HBV/HCV diagnosis codes against laboratory-confirmed infections. Performance measures (sensitivity, specificity, and positive predictive value) were estimated via cross-validated logistic regression and we explored variations by varying time windows from 1 to 5years before (i.e., prognostic prediction) and after (i.e., diagnostic prediction) the date of laboratory confirmation. Subgroup analyses were performed among immigrants, males, baby boomers, and females to examine the robustness of these measures. A total of 1,599,023 individuals were tested for HBV and 840,924 for HCV, with a resulting 41,714 (2.7%) and 58,563 (7.0%) infections identified, respectively. HBV/HCV diagnosis codes ± 3years of laboratory confirmation showed high specificity (99.9% HBV; 99.8% HCV), moderate positive predictive value (70.3% HBV; 85.8% HCV), and low sensitivity (12.8% HBV; 30.8% HCV). Varying the time window resulted in limited changes to performance measures. Diagnostic models consistently outperformed prognostic models. No major differences were observed among subgroups. HBV/HCV codes should not be the only source used for monitoring the population burden of these infections, due to low sensitivity and moderate positive predictive values. These results underscore the importance of ongoing laboratory and reportable disease surveillance systems for monitoring viral hepatitis in Ontario.

Stimulated emission assisted time-gated detection of a solid-state spin.

The nitrogen vacancy (NV) center in diamond is studied widely for magnetic field and temperature sensing at the nanoscale. Usually, the fluorescence is recorded to estimate the spin state of the NV center. Here we applied a time-gating technique to improve the contrast of the spin-dependent fluorescence. A NIR pulsed laser pumped the stimulated emission of the NV center and depleted the spontaneous emission that was excited by a green laser. We changed the relative delay between the NIR laser and the green laser. Then the spontaneous emission of the NV center in varied time windows was extracted by comparing the fluorescence intensities with and without the NIR laser. The results showed that the spin-dependent fluorescence contrast could be improved by approximately 1.8 times by applying the time gating. The background of the environment was eliminated due to temporal filtering. This work demonstrates that the stimulated emission assisted time-gating technique can be used to improve the performance of an NV center sensor in a noisy environment.

Using Linked Administrative Data to Measure Earnings Mobility of Public Assistance Recipients during the Great Recession

There is a great deal of interest and concern in the trends in income inequality in the United States and how it inequality has changed since the Great Recession. Various reasons for this divide have been offered but are notoriously difficult to evaluate due to data constraints. Public sector programs aimed at alleviating poverty are also difficult to measure because of data issues. In this paper, we estimate the impact of one of the largest federal support programs in the U.S. on income mobility of at risk populations. The Supplemental Nutrition Assistance Program (SNAP) serves low income families by providing significant food benefits.&#x0D; In this paper, we create a dataset linking SNAP (food stamp) administrative records (over 1 million records per year) to Department of Labor earnings records (over 4 million records per year) at the individual level from 2001-2016. Using these uniquely matched administrative records, we can track earnings mobility over time as one measure of the effectiveness of a public assistance program at a point in time (which will inform policy decisions with respect to temporary measures, especially in recessions). We provide results for several time periods, but hone in on the pre-and post Great Recession period as a means to understand the impact of deep economic change on income mobility.&#x0D; As mobility itself is multifaceted concept, we capture a variety of measures/indices and highlight the usefulness and limitations of administrative data for the analysis. Tracking several mobility indices over time with varying time windows we can identify empirical benchmarks applied to administrative to measure the future impacts as well and to tailor government support programs.

A Network Approach to Stroke Systems of Care.

The landscape of stroke systems of care is evolving as patients are increasingly transferred between hospitals for access to higher levels of care. This is driven by time-sensitive disability-reducing interventions such as mechanical thrombectomy. However, coordination and triage of patients for such treatment remain a challenge worldwide, particularly given complex eligibility criteria and varying time windows for treatment. Network analysis is an approach that may be applied to this problem. Hospital networks interlinked by patients moved from facility to facility can be studied using network modeling that respects the interdependent nature of the system. This allows understanding of the central hubs, the change of network structure over time, and the diffusion of innovations. This topical review introduces the basic principles of network science and provides an overview on the applications and potential interventions in stroke systems of care.

Network analysis identifies consensus physiological measures of neurovascular coupling in humans

Intimate communication between neural and vascular structures is required to match neuronal metabolism to blood flow, a process termed neurovascular coupling. The number of laboratories assessing neurovascular coupling in humans is increasing due to clinical interest in disease states, and basic science interest in a non-anesthetized, non-craniotomized, unrestrained, in vivo model. However, there is a lack of knowledge regarding how best to characterize the neurovascular response. To address this knowledge gap, we have amassed a highly powered human neurovascular coupling dataset, and deployed a network-based approach to reveal the most powerful and consistent metrics for quantifying neurovascular coupling. Using dimensionality reduction, community-based clustering, and majority-voting of traditional metrics (e.g. peak response, time to peak) and non-traditional metrics (e.g. varying time windows, pulsatility), we have identified which of the existing metrics predominantly characterize the neurovascular coupling response, are stable within and across participants, and explain the vast majority of the variance within our dataset of over 300 trials. We then harnessed our empirical approach to generate powerful novel metrics of neurovascular coupling, termed iAmplitude, iRate, and iPulsatility, which increase sensitivity when capturing population differences. These metrics may be useful to optimally understand neurovascular coupling in health and disease.

Children's physical activity and sedentary time compared using assessments of accelerometry counts and muscle activity level.

BackgroundThis research compared accelerometry (ACC)-derived and muscle electromyography (EMG)-based estimates of physical activity (PA) and sedentary time in typical PA tasks and during the daily lives of children.MethodsData was included from two exploratory studies. In Study I, 6–7-year-old children (n = 11, 64% girls) were assessed for eight PA tasks (walking, stair negotiation, climbing, crawling, swinging, balancing, trampoline jumping and a game of tag). In Study II, 7–9-year-old children (n = 14, 38% girls) were assessed for six PA tasks (walking, sitting, static squat, single leg hops, jump for height and standing long jump), and daily PA during one day with and one day without structured exercise. Quadriceps and hamstring muscle activity and inactivity using EMG shorts and acceleration by waist-mounted accelerometer were simultaneously measured and classified as sedentary, light, moderate and vigorous activity. Data from ACC was further analyzed using five different published cut-off points and varying time windows (1−60 s) for comparison with EMG.ResultsIn the PA tasks ACC counts and EMG amplitude showed marked differences in swinging, trampoline jumping, crawling, static squat, single leg hops, standing long jump and jump for height, the difference being over 170% when signals were normalized to that during walking. Furthermore, in walking, swinging, trampoline jumping, stair negotiation and crawling ACC classified over 60% of the time as vigorous-intensity activity, while EMG indicated primarily light- and moderate-intensity activities. During both days with and without exercise, ACC resulted in greater proportion of light activity (p < 0.01) and smaller proportion of moderate activity compared to EMG (p < 0.05). The choice of cut-off points and epoch length in ACC analysis influenced the classification of PA level and sedentary time. In the analysis of daily activities the cut-off points by Evenson et al. (2008) with epochs of 7.5 s and 15 s yielded the smallest difference (less than 10% of recording time at each intensity) against EMG-derived PA levels.DiscussionThis research provides novel insight on muscle activity and thereby on neuromuscular loading of major locomotor muscles during normal daily activities of children. While EMG and ACC provided similar estimates of sedentary time in 13 typical PA tasks, duration of light, moderate and vigorous PA varied considerably between the methods especially during walking, stair negotiation, crawling, swinging and trampoline jumping. Evenson et al.’s (2008) cut-off points with ≤15 s epoch provided similar classification of PA than EMG during daily life. Compared to impacts recorded using ACC, EMG can provide understanding on children’s neuromuscular loading during motor tasks that is useful when studying effects of PA interventions on, and development of, motor competence and coordination.

A robust surface-consistent residual phase correction method based on migrated gathers

Conventional residual static corrections determine the residual statics (time shifts) using common mid-point (CMP) gathers to estimate and correct anomalies induced by the near-surface. These time shifts disregard the phase errors existing in the data, which will reduce the resolution of the stacked image. Further errors result when reflection events in CMP gathers do not exhibit hyperbolic moveout. In order to solve these problems, we propose a robust surface-consistent residual phase correction method that simultaneously resolves both time shifts and constant phase rotations based on migrated gathers. The surface-consistent residual statics and phase are obtained from the migrated gathers expressed in terms of shot and receiver locations. We modified the standard technique of estimating the time shift corrections to include a surface-consistent constant phase rotation term. The proposed dual parameter algorithm (time shift and constant phase rotation) proved on a synthetic example that it was superior to conventional residual statics for improving the coherence of trace gathers. The computational effort can be reduced by generating migrated gathers and estimating the dual parameters in a spatially varying time window. We applied the proposed method to both synthetic and real data, and improved results were obtained with both.

Abstract TP328: Cost-Utility of Intravenous Recombinant Tissue Plasminogen Activator in the Treatment of Acute Ischemic Stroke: a Systematic Review

Background: Intravenous recombinant tissue plasminogen activator (IV rtPA) is a recommended thrombolytic for the treatment of acute ischemic stroke that has shown to improve outcomes in various trials, but its cost-utility within varying time windows has not been systematically reviewed. Method: A systematic review was conducted using 2 reviewers and 1 adjudicator. Search strategies were implemented in PUBMED/MEDLINE, EMBASE, Web of Science, and Cochrane Review. Forms and data were managed through RedCap. Studies that met specific inclusion criteria, such as authors citing the time window and analytic horizon they investigated, and specifying the type of model they implemented, were further assessed for cost-utility benefit. Results: Of the 385 results from the implemented search strategy, 18 studies met inclusion criteria, with 94% agreement between reviewers. Eleven studies looked at the time window within 3 hours of stroke symptom onset, 3 looked between 3 to 4.5 hours, 3 looked within 4.5 hours of onset, and 1 looked within 6 hours of onset. All but 1 study with an analytic horizon of 1 year looking at a 0-3 hour time window showed that that IV rtPA was either dominant or cost-effective at a willingness to pay of $50,000/QALY. Conclusion: IV rtPA shows to be cost-effective across a variety of assumptions, both at a national and international level. The analytic horizon and time window do significantly impact the cost-effectiveness of IV rtPA, but even at later time windows, the IV rtPA shows to be cost-effective relative to standard treatment and care.