Regional ionospheric model for improvement of navigation position with EGNOS

NASA Astrophysics Data System (ADS)

Swiatek, Anna; Tomasik, Lukasz; Jaworski, Leszek

The problem of insufficient accuracy of EGNOS correction for the territory of Poland, located at the edge of EGNOS range is well known. The EEI PECS project (EGNOS EUPOS Integration) assumed improving the EGNOS correction by using the GPS observations from Polish ASG-EUPOS stations. A ionospheric delay parameter is a part of EGNOS correction. The comparative analysis of TEC values obtained from EGNOS and regional permanent GNSS stations showed the systematic shift. The TEC from EGNOS correction is underestimated related to computed regional TEC value. The new-‘improved’ corrections computed based on regional model were substituted for the EGNOS correction for suitable message. Dynamic measurements managed using the Mobile GPS Laboratory (MGL), showed the improvement of navigation position with TEC regional model.

A Novel Feed-Forward Modeling System Leads to Sustained Improvements in Attention and Academic Performance.

PubMed

McDermott, Ashley F; Rose, Maya; Norris, Troy; Gordon, Eric

2016-01-28

This study tested a novel feed-forward modeling (FFM) system as a nonpharmacological intervention for the treatment of ADHD children and the training of cognitive skills that improve academic performance. This study implemented a randomized, controlled, parallel design comparing this FFM with a nonpharmacological community care intervention. Improvements were measured on parent- and clinician-rated scales of ADHD symptomatology and on academic performance tests completed by the participant. Participants were followed for 3 months after training. Participants in the FFM training group showed significant improvements in ADHD symptomatology and academic performance, while the control group did not. Improvements from FFM were sustained 3 months later. The FFM appeared to be an effective intervention for the treatment of ADHD and improving academic performance. This FFM training intervention shows promise as a first-line treatment for ADHD while improving academic performance. © The Author(s) 2016.

Assessment of summer rainfall forecast skill in the Intra-Americas in GFDL high and low-resolution models

NASA Astrophysics Data System (ADS)

Krishnamurthy, Lakshmi; Muñoz, Ángel G.; Vecchi, Gabriel A.; Msadek, Rym; Wittenberg, Andrew T.; Stern, Bill; Gudgel, Rich; Zeng, Fanrong

2018-05-01

The Caribbean low-level jet (CLLJ) is an important component of the atmospheric circulation over the Intra-Americas Sea (IAS) which impacts the weather and climate both locally and remotely. It influences the rainfall variability in the Caribbean, Central America, northern South America, the tropical Pacific and the continental Unites States through the transport of moisture. We make use of high-resolution coupled and uncoupled models from the Geophysical Fluid Dynamics Laboratory (GFDL) to investigate the simulation of the CLLJ and its teleconnections and further compare with low-resolution models. The high-resolution coupled model FLOR shows improvements in the simulation of the CLLJ and its teleconnections with rainfall and SST over the IAS compared to the low-resolution coupled model CM2.1. The CLLJ is better represented in uncoupled models (AM2.1 and AM2.5) forced with observed sea-surface temperatures (SSTs), emphasizing the role of SSTs in the simulation of the CLLJ. Further, we determine the forecast skill for observed rainfall using both high- and low-resolution predictions of rainfall and SSTs for the July-August-September season. We determine the role of statistical correction of model biases, coupling and horizontal resolution on the forecast skill. Statistical correction dramatically improves area-averaged forecast skill. But the analysis of spatial distribution in skill indicates that the improvement in skill after statistical correction is region dependent. Forecast skill is sensitive to coupling in parts of the Caribbean, Central and northern South America, and it is mostly insensitive over North America. Comparison of forecast skill between high and low-resolution coupled models does not show any dramatic difference. However, uncoupled models show improvement in the area-averaged skill in the high-resolution atmospheric model compared to lower resolution model. Understanding and improving the forecast skill over the IAS has important implications for highly vulnerable nations in the region.

Quality Improvement on the Acute Inpatient Psychiatry Unit Using the Model for Improvement

PubMed Central

Singh, Kuldeep; Sanderson, Joshua; Galarneau, David; Keister, Thomas; Hickman, Dean

2013-01-01

Background A need exists for constant evaluation and modification of processes within healthcare systems to achieve quality improvement. One common approach is the Model for Improvement that can be used to clearly define aims, measures, and changes that are then implemented through a plan-do-study-act (PDSA) cycle. This approach is a commonly used method for improving quality in a wide range of fields. The Model for Improvement allows for a systematic process that can be revised at set time intervals to achieve a desired result. Methods We used the Model for Improvement in an acute psychiatry unit (APU) to improve the screening incidence of abnormal involuntary movements in eligible patients—those starting or continuing on standing neuroleptics—with the Abnormal Involuntary Movement Scale (AIMS). Results After 8 weeks of using the Model for Improvement, both of the participating inpatient services in the APU showed substantial overall improvement in screening for abnormal involuntary movements using the AIMS. Conclusion Crucial aspects of a successful quality improvement initiative based on the Model for Improvement are well-defined goals, process measures, and structured PDSA cycles. Success also requires communication, organization, and participation of the entire team. PMID:24052768

Quality improvement on the acute inpatient psychiatry unit using the model for improvement.

PubMed

Singh, Kuldeep; Sanderson, Joshua; Galarneau, David; Keister, Thomas; Hickman, Dean

2013-01-01

A need exists for constant evaluation and modification of processes within healthcare systems to achieve quality improvement. One common approach is the Model for Improvement that can be used to clearly define aims, measures, and changes that are then implemented through a plan-do-study-act (PDSA) cycle. This approach is a commonly used method for improving quality in a wide range of fields. The Model for Improvement allows for a systematic process that can be revised at set time intervals to achieve a desired result. We used the Model for Improvement in an acute psychiatry unit (APU) to improve the screening incidence of abnormal involuntary movements in eligible patients-those starting or continuing on standing neuroleptics-with the Abnormal Involuntary Movement Scale (AIMS). After 8 weeks of using the Model for Improvement, both of the participating inpatient services in the APU showed substantial overall improvement in screening for abnormal involuntary movements using the AIMS. Crucial aspects of a successful quality improvement initiative based on the Model for Improvement are well-defined goals, process measures, and structured PDSA cycles. Success also requires communication, organization, and participation of the entire team.

Psychosocial work environment and myocardial infarction: improving risk estimation by combining two complementary job stress models in the SHEEP Study

PubMed Central

Peter, R; Siegrist, J; Hallqvist, J; Reuterwall, C; Theorell, T

2002-01-01

Objectives: Associations between two alternative formulations of job stress derived from the effort-reward imbalance and the job strain model and first non-fatal acute myocardial infarction were studied. Whereas the job strain model concentrates on situational (extrinsic) characteristics the effort-reward imbalance model analyses distinct person (intrinsic) characteristics in addition to situational ones. In view of these conceptual differences the hypothesis was tested that combining information from the two models improves the risk estimation of acute myocardial infarction. Methods: 951 male and female myocardial infarction cases and 1147 referents aged 45–64 years of The Stockholm Heart Epidemiology (SHEEP) case-control study underwent a clinical examination. Information on job stress and health adverse behaviours was derived from standardised questionnaires. Results: Multivariate analysis showed moderately increased odds ratios for either model. Yet, with respect to the effort-reward imbalance model gender specific effects were found: in men the extrinsic component contributed to risk estimation, whereas this was the case with the intrinsic component in women. Controlling each job stress model for the other in order to test the independent effect of either approach did not show systematically increased odds ratios. An improved estimation of acute myocardial infarction risk resulted from combining information from the two models by defining groups characterised by simultaneous exposure to effort-reward imbalance and job strain (men: odds ratio 2.02 (95% confidence intervals (CI) 1.34 to 3.07); women odds ratio 2.19 (95% CI 1.11 to 4.28)). Conclusions: Findings show an improved risk estimation of acute myocardial infarction by combining information from the two job stress models under study. Moreover, gender specific effects of the two components of the effort-reward imbalance model were observed. PMID:11896138

An Intervention Model of Constructive Conflict Resolution and Cooperative Learning.

ERIC Educational Resources Information Center

Zhang, Quanwu

1994-01-01

Tests an intervention model of constructive conflict resolution (CCR) and cooperative learning in three urban high schools. Findings show that improvements in CCR increased social support and decreased victimization for the students. These changes improved student's attitudes, self-esteem, interpersonal relations, and academic achievement. (GLR)

Global and Regional 3D Tomography for Improved Seismic Event Location and Uncertainty in Explosion Monitoring

NASA Astrophysics Data System (ADS)

Downey, N.; Begnaud, M. L.; Hipp, J. R.; Ballard, S.; Young, C. S.; Encarnacao, A. V.

2017-12-01

The SALSA3D global 3D velocity model of the Earth was developed to improve the accuracy and precision of seismic travel time predictions for a wide suite of regional and teleseismic phases. Recently, the global SALSA3D model was updated to include additional body wave phases including mantle phases, core phases, reflections off the core-mantle boundary and underside reflections off the surface of the Earth. We show that this update improves travel time predictions and leads directly to significant improvements in the accuracy and precision of seismic event locations as compared to locations computed using standard 1D velocity models like ak135, or 2½D models like RSTT. A key feature of our inversions is that path-specific model uncertainty of travel time predictions are calculated using the full 3D model covariance matrix computed during tomography, which results in more realistic uncertainty ellipses that directly reflect tomographic data coverage. Application of this method can also be done at a regional scale: we present a velocity model with uncertainty obtained using data obtained from the University of Utah Seismograph Stations. These results show a reduction in travel-time residuals for re-located events compared with those obtained using previously published models.

Improving surgeon utilization in an orthopedic department using simulation modeling

PubMed Central

Simwita, Yusta W; Helgheim, Berit I

2016-01-01

Purpose Worldwide more than two billion people lack appropriate access to surgical services due to mismatch between existing human resource and patient demands. Improving utilization of existing workforce capacity can reduce the existing gap between surgical demand and available workforce capacity. In this paper, the authors use discrete event simulation to explore the care process at an orthopedic department. Our main focus is improving utilization of surgeons while minimizing patient wait time. Methods The authors collaborated with orthopedic department personnel to map the current operations of orthopedic care process in order to identify factors that influence poor surgeons utilization and high patient waiting time. The authors used an observational approach to collect data. The developed model was validated by comparing the simulation output with the actual patient data that were collected from the studied orthopedic care process. The authors developed a proposal scenario to show how to improve surgeon utilization. Results The simulation results showed that if ancillary services could be performed before the start of clinic examination services, the orthopedic care process could be highly improved. That is, improved surgeon utilization and reduced patient waiting time. Simulation results demonstrate that with improved surgeon utilizations, up to 55% increase of future demand can be accommodated without patients reaching current waiting time at this clinic, thus, improving patient access to health care services. Conclusion This study shows how simulation modeling can be used to improve health care processes. This study was limited to a single care process; however the findings can be applied to improve other orthopedic care process with similar operational characteristics. PMID:29355193

Mid- and long-term runoff predictions by an improved phase-space reconstruction model.

PubMed

Hong, Mei; Wang, Dong; Wang, Yuankun; Zeng, Xiankui; Ge, Shanshan; Yan, Hengqian; Singh, Vijay P

2016-07-01

In recent years, the phase-space reconstruction method has usually been used for mid- and long-term runoff predictions. However, the traditional phase-space reconstruction method is still needs to be improved. Using the genetic algorithm to improve the phase-space reconstruction method, a new nonlinear model of monthly runoff is constructed. The new model does not rely heavily on embedding dimensions. Recognizing that the rainfall-runoff process is complex, affected by a number of factors, more variables (e.g. temperature and rainfall) are incorporated in the model. In order to detect the possible presence of chaos in the runoff dynamics, chaotic characteristics of the model are also analyzed, which shows the model can represent the nonlinear and chaotic characteristics of the runoff. The model is tested for its forecasting performance in four types of experiments using data from six hydrological stations on the Yellow River and the Yangtze River. Results show that the medium-and long-term runoff is satisfactorily forecasted at the hydrological stations. Not only is the forecasting trend accurate, but also the mean absolute percentage error is no more than 15%. Moreover, the forecast results of wet years and dry years are both good, which means that the improved model can overcome the traditional ''wet years and dry years predictability barrier,'' to some extent. The model forecasts for different regions are all good, showing the universality of the approach. Compared with selected conceptual and empirical methods, the model exhibits greater reliability and stability in the long-term runoff prediction. Our study provides a new thinking for research on the association between the monthly runoff and other hydrological factors, and also provides a new method for the prediction of the monthly runoff. Copyright © 2015 Elsevier Inc. All rights reserved.

Lunar Reconnaissance Orbiter Orbit Determination Accuracy Analysis

NASA Technical Reports Server (NTRS)

Slojkowski, Steven E.

2014-01-01

Results from operational OD produced by the NASA Goddard Flight Dynamics Facility for the LRO nominal and extended mission are presented. During the LRO nominal mission, when LRO flew in a low circular orbit, orbit determination requirements were met nearly 100% of the time. When the extended mission began, LRO returned to a more elliptical frozen orbit where gravity and other modeling errors caused numerous violations of mission accuracy requirements. Prediction accuracy is particularly challenged during periods when LRO is in full-Sun. A series of improvements to LRO orbit determination are presented, including implementation of new lunar gravity models, improved spacecraft solar radiation pressure modeling using a dynamic multi-plate area model, a shorter orbit determination arc length, and a constrained plane method for estimation. The analysis presented in this paper shows that updated lunar gravity models improved accuracy in the frozen orbit, and a multiplate dynamic area model improves prediction accuracy during full-Sun orbit periods. Implementation of a 36-hour tracking data arc and plane constraints during edge-on orbit geometry also provide benefits. A comparison of the operational solutions to precision orbit determination solutions shows agreement on a 100- to 250-meter level in definitive accuracy.

Modelling and characterization of primary settlers in view of whole plant and resource recovery modelling.

PubMed

Bachis, Giulia; Maruéjouls, Thibaud; Tik, Sovanna; Amerlinck, Youri; Melcer, Henryk; Nopens, Ingmar; Lessard, Paul; Vanrolleghem, Peter A

2015-01-01

Characterization and modelling of primary settlers have been neglected pretty much to date. However, whole plant and resource recovery modelling requires primary settler model development, as current models lack detail in describing the dynamics and the diversity of the removal process for different particulate fractions. This paper focuses on the improved modelling and experimental characterization of primary settlers. First, a new modelling concept based on particle settling velocity distribution is proposed which is then applied for the development of an improved primary settler model as well as for its characterization under addition of chemicals (chemically enhanced primary treatment, CEPT). This model is compared to two existing simple primary settler models (Otterpohl and Freund; Lessard and Beck), showing to be better than the first one and statistically comparable to the second one, but with easier calibration thanks to the ease with which wastewater characteristics can be translated into model parameters. Second, the changes in the activated sludge model (ASM)-based chemical oxygen demand fractionation between inlet and outlet induced by primary settling is investigated, showing that typical wastewater fractions are modified by primary treatment. As they clearly impact the downstream processes, both model improvements demonstrate the need for more detailed primary settler models in view of whole plant modelling.

The Wind Forecast Improvement Project (WFIP). A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations -- the Northern Study Area

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

Finley, Cathy

2014-04-30

This report contains the results from research aimed at improving short-range (0-6 hour) hub-height wind forecasts in the NOAA weather forecast models through additional data assimilation and model physics improvements for use in wind energy forecasting. Additional meteorological observing platforms including wind profilers, sodars, and surface stations were deployed for this study by NOAA and DOE, and additional meteorological data at or near wind turbine hub height were provided by South Dakota State University and WindLogics/NextEra Energy Resources over a large geographical area in the U.S. Northern Plains for assimilation into NOAA research weather forecast models. The resulting improvements inmore » wind energy forecasts based on the research weather forecast models (with the additional data assimilation and model physics improvements) were examined in many different ways and compared with wind energy forecasts based on the current operational weather forecast models to quantify the forecast improvements important to power grid system operators and wind plant owners/operators participating in energy markets. Two operational weather forecast models (OP_RUC, OP_RAP) and two research weather forecast models (ESRL_RAP, HRRR) were used as the base wind forecasts for generating several different wind power forecasts for the NextEra Energy wind plants in the study area. Power forecasts were generated from the wind forecasts in a variety of ways, from very simple to quite sophisticated, as they might be used by a wide range of both general users and commercial wind energy forecast vendors. The error characteristics of each of these types of forecasts were examined and quantified using bulk error statistics for both the local wind plant and the system aggregate forecasts. The wind power forecast accuracy was also evaluated separately for high-impact wind energy ramp events. The overall bulk error statistics calculated over the first six hours of the forecasts at both the individual wind plant and at the system-wide aggregate level over the one year study period showed that the research weather model-based power forecasts (all types) had lower overall error rates than the current operational weather model-based power forecasts, both at the individual wind plant level and at the system aggregate level. The bulk error statistics of the various model-based power forecasts were also calculated by season and model runtime/forecast hour as power system operations are more sensitive to wind energy forecast errors during certain times of year and certain times of day. The results showed that there were significant differences in seasonal forecast errors between the various model-based power forecasts. The results from the analysis of the various wind power forecast errors by model runtime and forecast hour showed that the forecast errors were largest during the times of day that have increased significance to power system operators (the overnight hours and the morning/evening boundary layer transition periods), but the research weather model-based power forecasts showed improvement over the operational weather model-based power forecasts at these times.« less

Improving Organizational Effectiveness through Transformational Leadership.

ERIC Educational Resources Information Center

Bass, Bernard M., Ed.; Avolio, Bruce J., Ed.

This book shows how transformational leadership is expected to contribute to an organization's efforts to improve its operations and the best use of its human resources. The concepts of transactional and transformational leadership are introduced within the framework of a full-range model of leadership. This model is applied to research,…

Prediction model of dissolved oxygen in ponds based on ELM neural network

NASA Astrophysics Data System (ADS)

Li, Xinfei; Ai, Jiaoyan; Lin, Chunhuan; Guan, Haibin

2018-02-01

Dissolved oxygen in ponds is affected by many factors, and its distribution is unbalanced. In this paper, in order to improve the imbalance of dissolved oxygen distribution more effectively, the dissolved oxygen prediction model of Extreme Learning Machine (ELM) intelligent algorithm is established, based on the method of improving dissolved oxygen distribution by artificial push flow. Select the Lake Jing of Guangxi University as the experimental area. Using the model to predict the dissolved oxygen concentration of different voltage pumps, the results show that the ELM prediction accuracy is higher than the BP algorithm, and its mean square error is MSEELM=0.0394, the correlation coefficient RELM=0.9823. The prediction results of the 24V voltage pump push flow show that the discrete prediction curve can approximate the measured values well. The model can provide the basis for the artificial improvement of the dissolved oxygen distribution decision.

The Role of Multimodel Combination in Improving Streamflow Prediction

NASA Astrophysics Data System (ADS)

Arumugam, S.; Li, W.

2008-12-01

Model errors are the inevitable part in any prediction exercise. One approach that is currently gaining attention to reduce model errors is by optimally combining multiple models to develop improved predictions. The rationale behind this approach primarily lies on the premise that optimal weights could be derived for each model so that the developed multimodel predictions will result in improved predictability. In this study, we present a new approach to combine multiple hydrological models by evaluating their predictability contingent on the predictor state. We combine two hydrological models, 'abcd' model and Variable Infiltration Capacity (VIC) model, with each model's parameter being estimated by two different objective functions to develop multimodel streamflow predictions. The performance of multimodel predictions is compared with individual model predictions using correlation, root mean square error and Nash-Sutcliffe coefficient. To quantify precisely under what conditions the multimodel predictions result in improved predictions, we evaluate the proposed algorithm by testing it against streamflow generated from a known model ('abcd' model or VIC model) with errors being homoscedastic or heteroscedastic. Results from the study show that streamflow simulated from individual models performed better than multimodels under almost no model error. Under increased model error, the multimodel consistently performed better than the single model prediction in terms of all performance measures. The study also evaluates the proposed algorithm for streamflow predictions in two humid river basins from NC as well as in two arid basins from Arizona. Through detailed validation in these four sites, the study shows that multimodel approach better predicts the observed streamflow in comparison to the single model predictions.

[The improvement of mixed human serum-induced anaphylactic reaction death model in guinea pigs].

PubMed

Chen, Jiong-Yuan; Lai, Yue; Li, Dang-Ri; Yue, Xia; Wang, Hui-Jun

2012-12-01

To increase the death rate of fatal anaphylaxis in guinea pigs and the detectahie level of the tryptase of mast cell in hlood serum. Seventy-four guinea pigs were randomly divided into five groups: original model group, original model control group, improved model group, improved model control group, improved model with non-anaphylaxis group. Using mixed human serum as the allergen, the way of injection, sensitization and induction were improved. ELISA was used to detect the serum mast cell tryptase and total IgE in guinea pigs of each group. The death rate of fatal anaphylaxis in original model group was 54.2% with the different degree of hemopericardium. The severe pericardial tamponade appeared in 9 guinea pigs in original model group and original model control group. The death rate of fatal anaphylaxis in improved model group was 75% without pericardial tamponade. The concentration of the serum total IgE showed no statistically difference hetween original model group and original model control group (P > 0.05), hut the serum mast cell tryptase level was higher in the original model group than that in the original model control group (P > 0.05). The concentration of the serum total IgE and the serum mast cell tryptase level were significantly higher in improved model group than that in the improved model control group (P < 0.05). The death rate of the improved model significantly increases, which can provide effective animal model for the study of serum total IgE and mast cell tryptase.

Superior Prognostic Value of Cumulative Intracranial Tumor Volume Relative to Largest Intracranial Tumor Volume for Stereotactic Radiosurgery-Treated Brain Metastasis Patients.

PubMed

Hirshman, Brian R; Wilson, Bayard; Ali, Mir Amaan; Proudfoot, James A; Koiso, Takao; Nagano, Osamu; Carter, Bob S; Serizawa, Toru; Yamamoto, Masaaki; Chen, Clark C

2018-04-01

Two intracranial tumor volume variables have been shown to prognosticate survival of stereotactic-radiosurgery-treated brain metastasis patients: the largest intracranial tumor volume (LITV) and the cumulative intracranial tumor volume (CITV). To determine whether the prognostic value of the Scored Index for Radiosurgery (SIR) model can be improved by replacing one of its components-LITV-with CITV. We compared LITV and CITV in terms of their survival prognostication using a series of multivariable models that included known components of the SIR: age, Karnofsky Performance Score, status of extracranial disease, and the number of brain metastases. Models were compared using established statistical measures, including the net reclassification improvement (NRI > 0) and integrated discrimination improvement (IDI). The analysis was performed in 2 independent cohorts, each consisting of ∼3000 patients. In both cohorts, CITV was shown to be independently predictive of patient survival. Replacement of LITV with CITV in the SIR model improved the model's ability to predict 1-yr survival. In the first cohort, the CITV model showed an NRI > 0 improvement of 0.2574 (95% confidence interval [CI] 0.1890-0.3257) and IDI of 0.0088 (95% CI 0.0057-0.0119) relative to the LITV model. In the second cohort, the CITV model showed a NRI > 0 of 0.2604 (95% CI 0.1796-0.3411) and IDI of 0.0051 (95% CI 0.0029-0.0073) relative to the LITV model. After accounting for covariates within the SIR model, CITV offers superior prognostic value relative to LITV for stereotactic radiosurgery-treated brain metastasis patients.

The Incorporation and Initialization of Cloud Water/ice in AN Operational Forecast Model

NASA Astrophysics Data System (ADS)

Zhao, Qingyun

Quantitative precipitation forecasts have been one of the weakest aspects of numerical weather prediction models. Theoretical studies show that the errors in precipitation calculation can arise from three sources: errors in the large-scale forecasts of primary variables, errors in the crude treatment of condensation/evaporation and precipitation processes, and errors in the model initial conditions. A new precipitation parameterization scheme has been developed to investigate the forecast value of improved precipitation physics via the introduction of cloud water and cloud ice into a numerical prediction model. The main feature of this scheme is the explicit calculation of cloud water and cloud ice in both the convective and stratiform precipitation parameterization. This scheme has been applied to the eta model at the National Meteorological Center. Four extensive tests have been performed. The statistical results showed a significant improvement in the model precipitation forecasts. Diagnostic studies suggest that the inclusion of cloud ice is important in transferring water vapor to precipitation and in the enhancement of latent heat release; the latter subsequently affects the vertical motion field significantly. Since three-dimensional cloud data is absent from the analysis/assimilation system for most numerical models, a method has been proposed to incorporate observed precipitation and nephanalysis data into the data assimilation system to obtain the initial cloud field for the eta model. In this scheme, the initial moisture and vertical motion fields are also improved at the same time as cloud initialization. The physical initialization is performed in a dynamical initialization framework that uses the Newtonian dynamical relaxation method to nudge the model's wind and mass fields toward analyses during a 12-hour data assimilation period. Results from a case study showed that a realistic cloud field was produced by this method at the end of the data assimilation period. Precipitation forecasts have been significantly improved as a result of the improved initial cloud, moisture and vertical motion fields.

Improving spatio-temporal model estimation of satellite-derived PM2.5 concentrations: Implications for public health

NASA Astrophysics Data System (ADS)

Barik, M. G.; Al-Hamdan, M. Z.; Crosson, W. L.; Yang, C. A.; Coffield, S. R.

2017-12-01

Satellite-derived environmental data, available in a range of spatio-temporal scales, are contributing to the growing use of health impact assessments of air pollution in the public health sector. Models developed using correlation of Moderate Resolution Imaging Spectrometer (MODIS) Aerosol Optical Depth (AOD) with ground measurements of fine particulate matter less than 2.5 microns (PM2.5) are widely applied to measure PM2.5 spatial and temporal variability. In the public health sector, associations of PM2.5 with respiratory and cardiovascular diseases are often investigated to quantify air quality impacts on these health concerns. In order to improve predictability of PM2.5 estimation using correlation models, we have included meteorological variables, higher-resolution AOD products and instantaneous PM2.5 observations into statistical estimation models. Our results showed that incorporation of high-resolution (1-km) Multi-Angle Implementation of Atmospheric Correction (MAIAC)-generated MODIS AOD, meteorological variables and instantaneous PM2.5 observations improved model performance in various parts of California (CA), USA, where single variable AOD-based models showed relatively weak performance. In this study, we further asked whether these improved models actually would be more successful for exploring associations of public health outcomes with estimated PM2.5. To answer this question, we geospatially investigated model-estimated PM2.5's relationship with respiratory and cardiovascular diseases such as asthma, high blood pressure, coronary heart disease, heart attack and stroke in CA using health data from the Centers for Disease Control and Prevention (CDC)'s Wide-ranging Online Data for Epidemiologic Research (WONDER) and the Behavioral Risk Factor Surveillance System (BRFSS). PM2.5 estimation from these improved models have the potential to improve our understanding of associations between public health concerns and air quality.

Improving emissions inventories in Mexico through systematic analysis of model performance along C-130 and DC-8 flight tracks during MILAGRO

NASA Astrophysics Data System (ADS)

Mena-Carrasco, M.; Carmichael, G. R.; Campbell, J. E.; Tang, Y.; Chai, T.

2007-05-01

During the MILAGRO campaign in March 2006 the University of Iowa provided regional air quality forecasting for scientific flight planning for the C-130 and DC-8. Model performance showed positive bias of ozone prediction (~15ppbv), associated to overpredictions in precursor concentrations (~2.15 ppbv NOy and ~1ppmv ARO1). Model bias showed a distinct geographical pattern in which the higher values were in and near Mexico City. Newer runs in which NOx and VOC emissions were decreased improved ozone prediction, decreasing bias and increasing model correlation, at the same time reducing regional bias over Mexico. This work will evaluate model performance using the newly published Mexico National Emissions Inventory, and the introduction of data assimilation to recover emissions scaling factors to optimize model performance. Finally the results of sensitivity runs showing the regional impact of Mexico City emissions on ozone concentrations will be shown, along with the influence of Mexico City aerosol concentrations on regional photochemistry.

Effect of geometric size on mechanical properties of dielectric elastomers based on an improved visco-hyperelastic film model

NASA Astrophysics Data System (ADS)

Chang, Mengzhou; Wang, Zhenqing; Tong, Liyong; Liang, Wenyan

2017-03-01

Dielectric polymers show complex mechanical behaviors with different boundary conditions, geometry size and pre-stress. A viscoelastic model suitable for inhomogeneous deformation is presented integrating the Kelvin-Voigt model in a new form in this work. For different types of uniaxial tensile test loading along the length direction of sample, single-step-relaxation tests, loading-unloading tests and tensile-creep-relaxation tests the improved model provides a quite favorable comparison with the experiment results. Moreover, The mechanical properties of test sample with several length-width ratios under different boundary conditions are also invested. The influences of the different boundary conditions are calculated with a stress applied on the boundary point and the result show that the fixed boundary will increase the stress compare with homogeneous deformation. In modeling the effect of pre-stress in the shear test, three pre-stressed mode are discussed. The model validation on the general mechanical behavior shows excellent predictive capability.

MR Vascular Fingerprinting in Stroke and Brain Tumors Models

NASA Astrophysics Data System (ADS)

Lemasson, B.; Pannetier, N.; Coquery, N.; Boisserand, Ligia S. B.; Collomb, Nora; Schuff, N.; Moseley, M.; Zaharchuk, G.; Barbier, E. L.; Christen, T.

2016-11-01

In this study, we evaluated an MRI fingerprinting approach (MRvF) designed to provide high-resolution parametric maps of the microvascular architecture (i.e., blood volume fraction, vessel diameter) and function (blood oxygenation) simultaneously. The method was tested in rats (n = 115), divided in 3 models: brain tumors (9 L, C6, F98), permanent stroke, and a control group of healthy animals. We showed that fingerprinting can robustly distinguish between healthy and pathological brain tissues with different behaviors in tumor and stroke models. In particular, fingerprinting revealed that C6 and F98 glioma models have similar signatures while 9 L present a distinct evolution. We also showed that it is possible to improve the results of MRvF and obtain supplemental information by changing the numerical representation of the vascular network. Finally, good agreement was found between MRvF and conventional MR approaches in healthy tissues and in the C6, F98, and permanent stroke models. For the 9 L glioma model, fingerprinting showed blood oxygenation measurements that contradict results obtained with a quantitative BOLD approach. In conclusion, MR vascular fingerprinting seems to be an efficient technique to study microvascular properties in vivo. Multiple technical improvements are feasible and might improve diagnosis and management of brain diseases.

MR Vascular Fingerprinting in Stroke and Brain Tumors Models

PubMed Central

Lemasson, B.; Pannetier, N.; Coquery, N.; Boisserand, Ligia S. B.; Collomb, Nora; Schuff, N.; Moseley, M.; Zaharchuk, G.; Barbier, E. L.; Christen, T.

2016-01-01

In this study, we evaluated an MRI fingerprinting approach (MRvF) designed to provide high-resolution parametric maps of the microvascular architecture (i.e., blood volume fraction, vessel diameter) and function (blood oxygenation) simultaneously. The method was tested in rats (n = 115), divided in 3 models: brain tumors (9 L, C6, F98), permanent stroke, and a control group of healthy animals. We showed that fingerprinting can robustly distinguish between healthy and pathological brain tissues with different behaviors in tumor and stroke models. In particular, fingerprinting revealed that C6 and F98 glioma models have similar signatures while 9 L present a distinct evolution. We also showed that it is possible to improve the results of MRvF and obtain supplemental information by changing the numerical representation of the vascular network. Finally, good agreement was found between MRvF and conventional MR approaches in healthy tissues and in the C6, F98, and permanent stroke models. For the 9 L glioma model, fingerprinting showed blood oxygenation measurements that contradict results obtained with a quantitative BOLD approach. In conclusion, MR vascular fingerprinting seems to be an efficient technique to study microvascular properties in vivo. Multiple technical improvements are feasible and might improve diagnosis and management of brain diseases. PMID:27883015

Diagnosis of the GLAS climate model's stationary planetary waves using a linearized steady state model

NASA Technical Reports Server (NTRS)

Youngblut, C.

1984-01-01

Orography and geographically fixed heat sources which force a zonally asymmetric motion field are examined. An extensive space-time spectral analysis of the GLAS climate model (D130) response and observations are compared. An updated version of the model (D150) showed a remarkable improvement in the simulation of the standing waves. The main differences in the model code are an improved boundary layer flux computation and a more realistic specification of the global boundary conditions.

Full velocity difference car-following model considering desired inter-vehicle distance

NASA Astrophysics Data System (ADS)

Xin, Tong; Yi, Liu; Rongjun, Cheng; Hongxia, Ge

Based on the full velocity difference car-following model, an improved car-following model is put forward by considering the driver’s desired inter-vehicle distance. The stability conditions are obtained by applying the control method. The results of theoretical analysis are used to demonstrate the advantages of our model. Numerical simulations are used to show that traffic congestion can be improved as the desired inter-vehicle distance is considered in the full velocity difference car-following model.

Ensemble formulation of surface fluxes and improvement in evapotranspiration and cloud parameterizations in a GCM. [General Circulation Model

NASA Technical Reports Server (NTRS)

Sud, Y. C.; Smith, W. E.

1984-01-01

The influence of some modifications to the parameters of the current general circulation model (GCM) is investigated. The aim of the modifications was to eliminate strong occasional bursts of oscillations in planetary boundary layer (PBL) fluxes. Smoothly varying bulk aerodynamic friction and heat transport coefficients were found by ensemble averaging of the PBL fluxes in the current GCM. A comparison was performed of the simulations of the modified model and the unmodified model. The comparison showed that the surface fluxes and cloudiness in the modified model simulations were much more accurate. The planetary albedo in the model was also realistic. Weaknesses persisted in the models positioning of the Inter-tropical convergence zone (ICTZ) and in the temperature estimates for polar regions. A second simulation of the model following reparametrization of the cloud data showed improved results and these are described in detail.

An Improved K-Epsilon Model for Near-Wall Turbulence and Comparison with Direct Numerical Simulation

NASA Technical Reports Server (NTRS)

Shih, T. H.

1990-01-01

An improved k-epsilon model for low Reynolds number turbulence near a wall is presented. The near-wall asymptotic behavior of the eddy viscosity and the pressure transport term in the turbulent kinetic energy equation is analyzed. Based on this analysis, a modified eddy viscosity model, having correct near-wall behavior, is suggested, and a model for the pressure transport term in the k-equation is proposed. In addition, a modeled dissipation rate equation is reformulated. Fully developed channel flows were used for model testing. The calculations using various k-epsilon models are compared with direct numerical simulations. The results show that the present k-epsilon model performs well in predicting the behavior of near-wall turbulence. Significant improvement over previous k-epsilon models is obtained.

Voluntary wheel running improves recovery from a moderate spinal cord injury.

PubMed

Engesser-Cesar, Christie; Anderson, Aileen J; Basso, D Michele; Edgerton, V R; Cotman, Carl W

2005-01-01

Recently, locomotor training has been shown to improve overground locomotion in patients with spinal cord injury (SCI). This has triggered renewed interest in the role of exercise in rehabilitation after SCI. However, there are no mouse models for voluntary exercise and recovery of function following SCI. Here, we report voluntary wheel running improves recovery from a SCI in mice. C57Bl/10 female mice received a 60-kdyne T9 contusion injury with an IH impactor after 3 weeks of voluntary wheel running or 3 weeks of standard single housing conditions. Following a 7-day recovery period, running mice were returned to their running wheels. Weekly open-field behavior measured locomotor recovery using the Basso, Beattie and Bresnahan (BBB) locomotor rating scale and the Basso Mouse Scale (BMS) locomotor rating scale, a scale recently developed specifically for mice. Initial experiments using standard rung wheels show that wheel running impaired recovery, but subsequent experiments using a modified flat-surface wheel show improved recovery with exercise. By 14 days post SCI, the modified flat-surface running group had significantly higher BBB and BMS scores than the sedentary group. A repeated measures ANOVA shows locomotor recovery of modified flat-surface running mice was significantly improved compared to sedentary animals (p < 0.05). Locomotor assessment using a ladder beam task also shows a significant improvement in the modified flat-surface runners (p < 0.05). Finally, fibronectin staining shows no significant difference in lesion size between the two groups. These data represent the first mouse model showing voluntary exercise improves recovery after SCI.

Phase Behavior Modeling of Asphaltene Precipitation for Heavy Crudes: A Promising Tool Along with Experimental Data

NASA Astrophysics Data System (ADS)

Tavakkoli, M.; Kharrat, R.; Masihi, M.; Ghazanfari, M. H.; Fadaei, S.

2012-12-01

Thermodynamic modeling is known as a promising tool for phase behavior modeling of asphaltene precipitation under different conditions such as pressure depletion and CO2 injection. In this work, a thermodynamic approach is used for modeling the phase behavior of asphaltene precipitation. The precipitated asphaltene phase is represented by an improved solid model, while the oil and gas phases are modeled with an equation of state. The PR-EOS was used to perform flash calculations. Then, the onset point and the amount of precipitated asphaltene were predicted. A computer code based on an improved solid model has been developed and used for predicting asphaltene precipitation data for one of Iranian heavy crudes, under pressure depletion and CO2 injection conditions. A significant improvement has been observed in predicting the asphaltene precipitation data under gas injection conditions. Especially for the maximum value of asphaltene precipitation and for the trend of the curve after the peak point, good agreement was observed. For gas injection conditions, comparison of the thermodynamic micellization model and the improved solid model showed that the thermodynamic micellization model cannot predict the maximum of precipitation as well as the improved solid model. The non-isothermal improved solid model has been used for predicting asphaltene precipitation data under pressure depletion conditions. The pressure depletion tests were done at different levels of temperature and pressure, and the parameters of a non-isothermal model were tuned using three onset pressures at three different temperatures for the considered crude. The results showed that the model is highly sensitive to the amount of solid molar volume along with the interaction coefficient parameter between the asphaltene component and light hydrocarbon components. Using a non-isothermal improved solid model, the asphaltene phase envelope was developed. It has been revealed that at high temperatures, an increase in the temperature results in a lower amount of asphaltene precipitation and also it causes the convergence of lower and upper boundaries of the asphaltene phase envelope. This work illustrates successful application of a non-isothermal improved solid model for developing the asphaltene phase envelope of heavy crude which can be helpful for monitoring and controlling of asphaltene precipitation through the wellbore and surface facilities during heavy oil production.

An Improved BeiDou-2 Satellite-Induced Code Bias Estimation Method.

PubMed

Fu, Jingyang; Li, Guangyun; Wang, Li

2018-04-27

Different from GPS, GLONASS, GALILEO and BeiDou-3, it is confirmed that the code multipath bias (CMB), which originate from the satellite end and can be over 1 m, are commonly found in the code observations of BeiDou-2 (BDS) IGSO and MEO satellites. In order to mitigate their adverse effects on absolute precise applications which use the code measurements, we propose in this paper an improved correction model to estimate the CMB. Different from the traditional model which considering the correction values are orbit-type dependent (estimating two sets of values for IGSO and MEO, respectively) and modeling the CMB as a piecewise linear function with a elevation node separation of 10°, we estimate the corrections for each BDS IGSO + MEO satellite on one hand, and a denser elevation node separation of 5° is used to model the CMB variations on the other hand. Currently, the institutions such as IGS-MGEX operate over 120 stations which providing the daily BDS observations. These large amounts of data provide adequate support to refine the CMB estimation satellite by satellite in our improved model. One month BDS observations from MGEX are used for assessing the performance of the improved CMB model by means of precise point positioning (PPP). Experimental results show that for the satellites on the same orbit type, obvious differences can be found in the CMB at the same node and frequency. Results show that the new correction model can improve the wide-lane (WL) ambiguity usage rate for WL fractional cycle bias estimation, shorten the WL and narrow-lane (NL) time to first fix (TTFF) in PPP ambiguity resolution (AR) as well as improve the PPP positioning accuracy. With our improved correction model, the usage of WL ambiguity is increased from 94.1% to 96.0%, the WL and NL TTFF of PPP AR is shorten from 10.6 to 9.3 min, 67.9 to 63.3 min, respectively, compared with the traditional correction model. In addition, both the traditional and improved CMB model have a better performance in these aspects compared with the model which does not account for the CMB correction.

An Improved BeiDou-2 Satellite-Induced Code Bias Estimation Method

PubMed Central

Fu, Jingyang; Li, Guangyun; Wang, Li

2018-01-01

Different from GPS, GLONASS, GALILEO and BeiDou-3, it is confirmed that the code multipath bias (CMB), which originate from the satellite end and can be over 1 m, are commonly found in the code observations of BeiDou-2 (BDS) IGSO and MEO satellites. In order to mitigate their adverse effects on absolute precise applications which use the code measurements, we propose in this paper an improved correction model to estimate the CMB. Different from the traditional model which considering the correction values are orbit-type dependent (estimating two sets of values for IGSO and MEO, respectively) and modeling the CMB as a piecewise linear function with a elevation node separation of 10°, we estimate the corrections for each BDS IGSO + MEO satellite on one hand, and a denser elevation node separation of 5° is used to model the CMB variations on the other hand. Currently, the institutions such as IGS-MGEX operate over 120 stations which providing the daily BDS observations. These large amounts of data provide adequate support to refine the CMB estimation satellite by satellite in our improved model. One month BDS observations from MGEX are used for assessing the performance of the improved CMB model by means of precise point positioning (PPP). Experimental results show that for the satellites on the same orbit type, obvious differences can be found in the CMB at the same node and frequency. Results show that the new correction model can improve the wide-lane (WL) ambiguity usage rate for WL fractional cycle bias estimation, shorten the WL and narrow-lane (NL) time to first fix (TTFF) in PPP ambiguity resolution (AR) as well as improve the PPP positioning accuracy. With our improved correction model, the usage of WL ambiguity is increased from 94.1% to 96.0%, the WL and NL TTFF of PPP AR is shorten from 10.6 to 9.3 min, 67.9 to 63.3 min, respectively, compared with the traditional correction model. In addition, both the traditional and improved CMB model have a better performance in these aspects compared with the model which does not account for the CMB correction. PMID:29702559

A Whole School Approach: Collaborative Development of School Health Policies, Processes, and Practices

ERIC Educational Resources Information Center

Hunt, Pete; Barrios, Lisa; Telljohann, Susan K.; Mazyck, Donna

2015-01-01

Background: The Whole School, Whole Community, Whole Child (WSCC) model shows the interrelationship between health and learning and the potential for improving educational outcomes by improving health outcomes. However, current descriptions do not explain how to implement the model. Methods: The existing literature, including scientific articles,…

Realism of Indian Summer Monsoon Simulation in a Quarter Degree Global Climate Model

NASA Astrophysics Data System (ADS)

Salunke, P.; Mishra, S. K.; Sahany, S.; Gupta, K.

2017-12-01

This study assesses the fidelity of Indian Summer Monsoon (ISM) simulations using a global model at an ultra-high horizontal resolution (UHR) of 0.25°. The model used was the atmospheric component of the Community Earth System Model version 1.2.0 (CESM 1.2.0) developed at the National Center for Atmospheric Research (NCAR). Precipitation and temperature over the Indian region were analyzed for a wide range of space and time scales to evaluate the fidelity of the model under UHR, with special emphasis on the ISM simulations during the period of June-through-September (JJAS). Comparing the UHR simulations with observed data from the India Meteorological Department (IMD) over the Indian land, it was found that 0.25° resolution significantly improved spatial rainfall patterns over many regions, including the Western Ghats and the South-Eastern peninsula as compared to the standard model resolution. Convective and large-scale rainfall components were analyzed using the European Centre for Medium Range Weather Forecast (ECMWF) Re-Analysis (ERA)-Interim (ERA-I) data and it was found that at 0.25° resolution, there was an overall increase in the large-scale component and an associated decrease in the convective component of rainfall as compared to the standard model resolution. Analysis of the diurnal cycle of rainfall suggests a significant improvement in the phase characteristics simulated by the UHR model as compared to the standard model resolution. Analysis of the annual cycle of rainfall, however, failed to show any significant improvement in the UHR model as compared to the standard version. Surface temperature analysis showed small improvements in the UHR model simulations as compared to the standard version. Thus, one may conclude that there are some significant improvements in the ISM simulations using a 0.25° global model, although there is still plenty of scope for further improvement in certain aspects of the annual cycle of rainfall.

Prediction of stock markets by the evolutionary mix-game model

NASA Astrophysics Data System (ADS)

Chen, Fang; Gou, Chengling; Guo, Xiaoqian; Gao, Jieping

2008-06-01

This paper presents the efforts of using the evolutionary mix-game model, which is a modified form of the agent-based mix-game model, to predict financial time series. Here, we have carried out three methods to improve the original mix-game model by adding the abilities of strategy evolution to agents, and then applying the new model referred to as the evolutionary mix-game model to forecast the Shanghai Stock Exchange Composite Index. The results show that these modifications can improve the accuracy of prediction greatly when proper parameters are chosen.

Impact of high resolution land surface initialization in Indian summer monsoon simulation using a regional climate model

NASA Astrophysics Data System (ADS)

Unnikrishnan, C. K.; Rajeevan, M.; Rao, S. Vijaya Bhaskara

2016-06-01

The direct impact of high resolution land surface initialization on the forecast bias in a regional climate model in recent years over Indian summer monsoon region is investigated. Two sets of regional climate model simulations are performed, one with a coarse resolution land surface initial conditions and second one used a high resolution land surface data for initial condition. The results show that all monsoon years respond differently to the high resolution land surface initialization. The drought monsoon year 2009 and extended break periods were more sensitive to the high resolution land surface initialization. These results suggest that the drought monsoon year predictions can be improved with high resolution land surface initialization. Result also shows that there are differences in the response to the land surface initialization within the monsoon season. Case studies of heat wave and a monsoon depression simulation show that, the model biases were also improved with high resolution land surface initialization. These results show the need for a better land surface initialization strategy in high resolution regional models for monsoon forecasting.

Machine Learning Based Multi-Physical-Model Blending for Enhancing Renewable Energy Forecast -- Improvement via Situation Dependent Error Correction

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

Lu, Siyuan; Hwang, Youngdeok; Khabibrakhmanov, Ildar

With increasing penetration of solar and wind energy to the total energy supply mix, the pressing need for accurate energy forecasting has become well-recognized. Here we report the development of a machine-learning based model blending approach for statistically combining multiple meteorological models for improving the accuracy of solar/wind power forecast. Importantly, we demonstrate that in addition to parameters to be predicted (such as solar irradiance and power), including additional atmospheric state parameters which collectively define weather situations as machine learning input provides further enhanced accuracy for the blended result. Functional analysis of variance shows that the error of individual modelmore » has substantial dependence on the weather situation. The machine-learning approach effectively reduces such situation dependent error thus produces more accurate results compared to conventional multi-model ensemble approaches based on simplistic equally or unequally weighted model averaging. Validation over an extended period of time results show over 30% improvement in solar irradiance/power forecast accuracy compared to forecasts based on the best individual model.« less

Tailoring Mathematical Models to Stem-Cell Derived Cardiomyocyte Lines Can Improve Predictions of Drug-Induced Changes to Their Electrophysiology.

PubMed

Lei, Chon Lok; Wang, Ken; Clerx, Michael; Johnstone, Ross H; Hortigon-Vinagre, Maria P; Zamora, Victor; Allan, Andrew; Smith, Godfrey L; Gavaghan, David J; Mirams, Gary R; Polonchuk, Liudmila

2017-01-01

Human induced pluripotent stem cell derived cardiomyocytes (iPSC-CMs) have applications in disease modeling, cell therapy, drug screening and personalized medicine. Computational models can be used to interpret experimental findings in iPSC-CMs, provide mechanistic insights, and translate these findings to adult cardiomyocyte (CM) electrophysiology. However, different cell lines display different expression of ion channels, pumps and receptors, and show differences in electrophysiology. In this exploratory study, we use a mathematical model based on iPSC-CMs from Cellular Dynamic International (CDI, iCell), and compare its predictions to novel experimental recordings made with the Axiogenesis Cor.4U line. We show that tailoring this model to the specific cell line, even using limited data and a relatively simple approach, leads to improved predictions of baseline behavior and response to drugs. This demonstrates the need and the feasibility to tailor models to individual cell lines, although a more refined approach will be needed to characterize individual currents, address differences in ion current kinetics, and further improve these results.

Simulation and fabrication of thin film bulk acoustic wave resonator

NASA Astrophysics Data System (ADS)

Xixi, Han; Yi, Ou; Zhigang, Li; Wen, Ou; Dapeng, Chen; Tianchun, Ye

2016-07-01

In this paper, we present the simulation and fabrication of a thin film bulk acoustic resonator (FBAR). In order to improve the accuracy of simulation, an improved Mason model was introduced to design the resonator by taking the coupling effect between electrode and substrate into consideration. The resonators were fabricated by the eight inch CMOS process, and the measurements show that the improved Mason model is more accurate than a simple Mason model. The Q s (Q at series resonance), Q p (Q at parallel resonance), Q max and k t 2 of the FBAR were measured to be 695, 814, 1049, and 7.01% respectively, showing better performance than previous reports. Project supported by the National Natural Science Foundation of China (Nos. 61274119, 61306141, 61335008) and the Natural Science Foundation of Jiangsu Province (No. BK20131099).

The Wind Forecast Improvement Project (WFIP): A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations. The Southern Study Area, Final Report

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

Freedman, Jeffrey M.; Manobianco, John; Schroeder, John

This Final Report presents a comprehensive description, findings, and conclusions for the Wind Forecast Improvement Project (WFIP) -- Southern Study Area (SSA) work led by AWS Truepower (AWST). This multi-year effort, sponsored by the Department of Energy (DOE) and National Oceanographic and Atmospheric Administration (NOAA), focused on improving short-term (15-minute - 6 hour) wind power production forecasts through the deployment of an enhanced observation network of surface and remote sensing instrumentation and the use of a state-of-the-art forecast modeling system. Key findings from the SSA modeling and forecast effort include: 1. The AWST WFIP modeling system produced an overall 10more » - 20% improvement in wind power production forecasts over the existing Baseline system, especially during the first three forecast hours; 2. Improvements in ramp forecast skill, particularly for larger up and down ramps; 3. The AWST WFIP data denial experiments showed mixed results in the forecasts incorporating the experimental network instrumentation; however, ramp forecasts showed significant benefit from the additional observations, indicating that the enhanced observations were key to the model systems’ ability to capture phenomena responsible for producing large short-term excursions in power production; 4. The OU CAPS ARPS simulations showed that the additional WFIP instrument data had a small impact on their 3-km forecasts that lasted for the first 5-6 hours, and increasing the vertical model resolution in the boundary layer had a greater impact, also in the first 5 hours; and 5. The TTU simulations were inconclusive as to which assimilation scheme (3DVAR versus EnKF) provided better forecasts, and the additional observations resulted in some improvement to the forecasts in the first 1 - 3 hours.« less

Application of Hellison's Teaching Personal and Social Responsibility Model in physical education to improve self-efficacy for adolescents at risk of dropping-out of school.

PubMed

Escartí, Amparo; Gutiérrez, Melchor; Pascual, Carmina; Marín, Diana

2010-11-01

This study evaluated improvement in self-efficacy and personal and social responsibility among at-risk of dropping-out of school adolescents participating in a program in which Hellison's Teaching Personal and Social Responsibility Model was applied in physical education classes during the course of an academic year. Thirty at-risk adolescents aged 13-14 years old (23 boys, 7 girls) were assigned to an intervention group (12 boys and 3 girls) or a comparison group (11 boys, 4 girls), the latter of which did not participate in the program. Quantitative results showed a significant improvement in the students' self-efficacy for enlisting social resources and in self-efficacy for self-regulated learning. Qualitative results showed an improvement in responsibility behaviors of participants in the intervention group. This suggests that the model could be effective for improving psychological and social development in at-risk adolescents, and that physical education classes may be an appropriate arena for working with these young people.

Comparison of Kasai Autocorrelation and Maximum Likelihood Estimators for Doppler Optical Coherence Tomography

PubMed Central

Chan, Aaron C.; Srinivasan, Vivek J.

2013-01-01

In optical coherence tomography (OCT) and ultrasound, unbiased Doppler frequency estimators with low variance are desirable for blood velocity estimation. Hardware improvements in OCT mean that ever higher acquisition rates are possible, which should also, in principle, improve estimation performance. Paradoxically, however, the widely used Kasai autocorrelation estimator’s performance worsens with increasing acquisition rate. We propose that parametric estimators based on accurate models of noise statistics can offer better performance. We derive a maximum likelihood estimator (MLE) based on a simple additive white Gaussian noise model, and show that it can outperform the Kasai autocorrelation estimator. In addition, we also derive the Cramer Rao lower bound (CRLB), and show that the variance of the MLE approaches the CRLB for moderate data lengths and noise levels. We note that the MLE performance improves with longer acquisition time, and remains constant or improves with higher acquisition rates. These qualities may make it a preferred technique as OCT imaging speed continues to improve. Finally, our work motivates the development of more general parametric estimators based on statistical models of decorrelation noise. PMID:23446044

Brief report: theatre as therapy for children with autism spectrum disorder.

PubMed

Corbett, Blythe A; Gunther, Joan R; Comins, Dan; Price, Jenifer; Ryan, Niles; Simon, David; Schupp, Clayton W; Rios, Taylor

2011-04-01

The pilot investigation evaluated a theatrical intervention program, Social Emotional NeuroScience Endocrinology (SENSE) Theatre, designed to improve socioemotional functioning and reduce stress in children with autism spectrum disorder (ASD). Eight children with ASD were paired with typically developing peers that served as expert models. Neuropsychological, biological (cortisol and oxytocin), and behavioral measures were assessed in a pretest-posttest design. The intervention was embedded in a full musical theatrical production. Participants showed some improvement in face identification and theory of mind skills. The intervention shows potential promise in improving the socioemotional functioning in children with ASD through the utilization of peers, video and behavioral modeling, and a community-based theatrical setting.

Insights on multivariate updates of physical and biogeochemical ocean variables using an Ensemble Kalman Filter and an idealized model of upwelling

NASA Astrophysics Data System (ADS)

Yu, Liuqian; Fennel, Katja; Bertino, Laurent; Gharamti, Mohamad El; Thompson, Keith R.

2018-06-01

Effective data assimilation methods for incorporating observations into marine biogeochemical models are required to improve hindcasts, nowcasts and forecasts of the ocean's biogeochemical state. Recent assimilation efforts have shown that updating model physics alone can degrade biogeochemical fields while only updating biogeochemical variables may not improve a model's predictive skill when the physical fields are inaccurate. Here we systematically investigate whether multivariate updates of physical and biogeochemical model states are superior to only updating either physical or biogeochemical variables. We conducted a series of twin experiments in an idealized ocean channel that experiences wind-driven upwelling. The forecast model was forced with biased wind stress and perturbed biogeochemical model parameters compared to the model run representing the "truth". Taking advantage of the multivariate nature of the deterministic Ensemble Kalman Filter (DEnKF), we assimilated different combinations of synthetic physical (sea surface height, sea surface temperature and temperature profiles) and biogeochemical (surface chlorophyll and nitrate profiles) observations. We show that when biogeochemical and physical properties are highly correlated (e.g., thermocline and nutricline), multivariate updates of both are essential for improving model skill and can be accomplished by assimilating either physical (e.g., temperature profiles) or biogeochemical (e.g., nutrient profiles) observations. In our idealized domain, the improvement is largely due to a better representation of nutrient upwelling, which results in a more accurate nutrient input into the euphotic zone. In contrast, assimilating surface chlorophyll improves the model state only slightly, because surface chlorophyll contains little information about the vertical density structure. We also show that a degradation of the correlation between observed subsurface temperature and nutrient fields, which has been an issue in several previous assimilation studies, can be reduced by multivariate updates of physical and biogeochemical fields.

Improving a Spectral Bin Microphysical Scheme Using TRMM Satellite Observations

NASA Technical Reports Server (NTRS)

Li, Xiaowen; Tao, Wei-Kuo; Matsui, Toshihisa; Liu, Chuntao; Masunaga, Hirohiko

2010-01-01

Comparisons between cloud model simulations and observations are crucial in validating model performance and improving physical processes represented in the mod Tel.hese modeled physical processes are idealized representations and almost always have large rooms for improvements. In this study, we use data from two different sensors onboard TRMM (Tropical Rainfall Measurement Mission) satellite to improve the microphysical scheme in the Goddard Cumulus Ensemble (GCE) model. TRMM observed mature-stage squall lines during late spring, early summer in central US over a 9-year period are compiled and compared with a case simulation by GCE model. A unique aspect of the GCE model is that it has a state-of-the-art spectral bin microphysical scheme, which uses 33 different bins to represent particle size distribution of each of the seven hydrometeor species. A forward radiative transfer model calculates TRMM Precipitation Radar (PR) reflectivity and TRMM Microwave Imager (TMI) 85 GHz brightness temperatures from simulated particle size distributions. Comparisons between model outputs and observations reveal that the model overestimates sizes of snow/aggregates in the stratiform region of the squall line. After adjusting temperature-dependent collection coefficients among ice-phase particles, PR comparisons become good while TMI comparisons worsen. Further investigations show that the partitioning between graupel (a high-density form of aggregate), and snow (a low-density form of aggregate) needs to be adjusted in order to have good comparisons in both PR reflectivity and TMI brightness temperature. This study shows that long-term satellite observations, especially those with multiple sensors, can be very useful in constraining model microphysics. It is also the first study in validating and improving a sophisticated spectral bin microphysical scheme according to long-term satellite observations.

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

Hong, Mei; Wang, Dong, E-mail: wangdong@nju.edu.cn; Wang, Yuankun

In recent years, the phase-space reconstruction method has usually been used for mid- and long-term runoff predictions. However, the traditional phase-space reconstruction method is still needs to be improved. Using the genetic algorithm to improve the phase-space reconstruction method, a new nonlinear model of monthly runoff is constructed. The new model does not rely heavily on embedding dimensions. Recognizing that the rainfall–runoff process is complex, affected by a number of factors, more variables (e.g. temperature and rainfall) are incorporated in the model. In order to detect the possible presence of chaos in the runoff dynamics, chaotic characteristics of the modelmore » are also analyzed, which shows the model can represent the nonlinear and chaotic characteristics of the runoff. The model is tested for its forecasting performance in four types of experiments using data from six hydrological stations on the Yellow River and the Yangtze River. Results show that the medium-and long-term runoff is satisfactorily forecasted at the hydrological stations. Not only is the forecasting trend accurate, but also the mean absolute percentage error is no more than 15%. Moreover, the forecast results of wet years and dry years are both good, which means that the improved model can overcome the traditional ‘‘wet years and dry years predictability barrier,’’ to some extent. The model forecasts for different regions are all good, showing the universality of the approach. Compared with selected conceptual and empirical methods, the model exhibits greater reliability and stability in the long-term runoff prediction. Our study provides a new thinking for research on the association between the monthly runoff and other hydrological factors, and also provides a new method for the prediction of the monthly runoff. - Highlights: • The improved phase-space reconstruction model of monthly runoff is established. • Two variables (temperature and rainfall) are incorporated in the model. • Chaotic characteristics of the model are also analyzed. • The forecast results of the mid and long-term runoff in six stations are accurate.« less

Automation of energy demand forecasting

NASA Astrophysics Data System (ADS)

Siddique, Sanzad

Automation of energy demand forecasting saves time and effort by searching automatically for an appropriate model in a candidate model space without manual intervention. This thesis introduces a search-based approach that improves the performance of the model searching process for econometrics models. Further improvements in the accuracy of the energy demand forecasting are achieved by integrating nonlinear transformations within the models. This thesis introduces machine learning techniques that are capable of modeling such nonlinearity. Algorithms for learning domain knowledge from time series data using the machine learning methods are also presented. The novel search based approach and the machine learning models are tested with synthetic data as well as with natural gas and electricity demand signals. Experimental results show that the model searching technique is capable of finding an appropriate forecasting model. Further experimental results demonstrate an improved forecasting accuracy achieved by using the novel machine learning techniques introduced in this thesis. This thesis presents an analysis of how the machine learning techniques learn domain knowledge. The learned domain knowledge is used to improve the forecast accuracy.

Analysis of vehicle's safety envelope under car-following model

NASA Astrophysics Data System (ADS)

Tang, Tie-Qiao; Zhang, Jian; Chen, Liang; Shang, Hua-Yan

2017-05-01

In this paper, we propose an improved car-following model to explore the impacts of vehicle's two safety distances (i.e., the front safety distance and back safety distance) on the traffic safety during the starting process. The numerical results show that our model is prominently safer than the FVD (full velocity difference) model, i.e., our model is better than the FVD model from the perspective of the traffic safety, which shows that each driver should consider his two safety distances during his driving process.

Applying ILAMB to data from several generations of the Community Land Model to assess the relative contribution of model improvements and forcing uncertainty to model-data agreement

NASA Astrophysics Data System (ADS)

Lawrence, D. M.; Fisher, R.; Koven, C.; Oleson, K. W.; Swenson, S. C.; Hoffman, F. M.; Randerson, J. T.; Collier, N.; Mu, M.

2017-12-01

The International Land Model Benchmarking (ILAMB) project is a model-data intercomparison and integration project designed to assess and help improve land models. The current package includes assessment of more than 25 land variables across more than 60 global, regional, and site-level (e.g., FLUXNET) datasets. ILAMB employs a broad range of metrics including RMSE, mean error, spatial distributions, interannual variability, and functional relationships. Here, we apply ILAMB for the purpose of assessment of several generations of the Community Land Model (CLM4, CLM4.5, and CLM5). Encouragingly, CLM5, which is the result of model development over the last several years by more than 50 researchers from 15 different institutions, shows broad improvements across many ILAMB metrics including LAI, GPP, vegetation carbon stocks, and the historical net ecosystem carbon balance among others. We will also show that considerable uncertainty arises from the historical climate forcing data used (GSWP3v1 and CRUNCEPv7). ILAMB score variations due to forcing data can be as large for many variables as that due to model structural differences. Strengths and weaknesses and persistent biases across model generations will also be presented.

How the national healthcare quality and disparities reports can catalyze quality improvement.

PubMed

McNeill, Dwight; Kelley, Ed

2005-03-01

The purpose of the National Reports on Healthcare Quality and Disparities is to enhance awareness of quality and health care disparities, track progress, understand variations, and catalyze improvements in health care. The objective of this paper is to propose a model that will facilitate a user's progression from knowledge to action and to show how the reports, its data warehouse, associated products, and Agency for Healthcare Research and Quality resources are integrated and focused on a comprehensive campaign to improve health care quality. The design of the paper is to present a conceptual model and to show how implementation strategies for the reports fit the model. The authors propose a quality improvement supply chain model to help elucidate the links of the process, corresponding developmental stages that potential users need to master and progress through, and "just-in-time" supply chain inputs at each of the corresponding stages, and populate the model with examples. The traditional ways of disseminating knowledge derived from science through reports and conferences are inadequate to the humbling need for vast improvements in the US health care system. Our model suggests the need for a wide variety of information, packaged in a diverse ways, and delivered just in time and on demand. It encourages the alignment of decision makers and researchers, along with information intermediaries and innovation brokers, to make the information production cycle more efficient and effective. Future iterations of the reports will improve relevance, meaning, and distribution of information to facilitate its uptake by potential users.

Improving Rice Modeling Success Rate with Ternary Non-structural Fertilizer Response Model.

PubMed

Li, Juan; Zhang, Mingqing; Chen, Fang; Yao, Baoquan

2018-06-13

Fertilizer response modelling is an important technical approach to realize metrological fertilization on rice. With the goal of solving the problems of a low success rate of a ternary quadratic polynomial model (TPFM) and to expand the model's applicability, this paper established a ternary non-structural fertilizer response model (TNFM) based on the experimental results from N, P and K fertilized rice fields. Our research results showed that the TNFM significantly improved the modelling success rate by addressing problems arising from setting the bias and multicollinearity in a TPFM. The results from 88 rice field trials in China indicated that the proportion of typical TNFMs that satisfy the general fertilizer response law of plant nutrition was 40.9%, while the analogous proportion of TPFMs was only 26.1%. The recommended fertilization showed a significant positive linear correlation between the two models, and the parameters N 0 , P 0 and K 0 that estimated the value of soil supplying nutrient equivalents can be used as better indicators of yield potential in plots where no N or P or K fertilizer was applied. The theoretical analysis showed that the new model has a higher fitting accuracy and a wider application range.

Improving emissions inventories in North America through systematic analysis of model performance during ICARTT and MILAGRO

NASA Astrophysics Data System (ADS)

Mena, Marcelo Andres

During 2004 and 2006 the University of Iowa provided air quality forecast support for flight planning of the ICARTT and MILAGRO field campaigns. A method for improvement of model performance in comparison to observations is showed. The method allows identifying sources of model error from boundary conditions and emissions inventories. Simultaneous analysis of horizontal interpolation of model error and error covariance showed that error in ozone modeling is highly correlated to the error of its precursors, and that there is geographical correlation also. During ICARTT ozone modeling error was improved by updating from the National Emissions Inventory from 1999 and 2001, and furthermore by updating large point source emissions from continuous monitoring data. Further improvements were achieved by reducing area emissions of NOx y 60% for states in the Southeast United States. Ozone error was highly correlated to NOy error during this campaign. Also ozone production in the United States was most sensitive to NOx emissions. During MILAGRO model performance in terms of correlation coefficients was higher, but model error in ozone modeling was high due overestimation of NOx and VOC emissions in Mexico City during forecasting. Large model improvements were shown by decreasing NOx emissions in Mexico City by 50% and VOC by 60%. Recurring ozone error is spatially correlated to CO and NOy error. Sensitivity studies show that Mexico City aerosol can reduce regional photolysis rates by 40% and ozone formation by 5-10%. Mexico City emissions can enhance NOy and O3 concentrations over the Gulf of Mexico in up to 10-20%. Mexico City emissions can convert regional ozone production regimes from VOC to NOx limited. A method of interpolation of observations along flight tracks is shown, which can be used to infer on the direction of outflow plumes. The use of ratios such as O3/NOy and NOx/NOy can be used to provide information on chemical characteristics of the plume, such as age, and ozone production regime. Interpolated MTBE observations can be used as a tracer of urban mobile source emissions. Finally procedures for estimating and gridding emissions inventories in Brazil and Mexico are presented.

Does box model training improve surgical dexterity and economy of movement during virtual reality laparoscopy? A randomised trial.

PubMed

Clevin, Lotte; Grantcharov, Teodor P

2008-01-01

Laparoscopic box model trainers have been used in training curricula for a long time, however data on their impact on skills acquisition is still limited. Our aim was to validate a low cost box model trainer as a tool for the training of skills relevant to laparoscopic surgery. Randomised, controlled trial (Canadian Task Force Classification I). University Hospital. Sixteen gynaecologic residents with limited laparoscopic experience were randomised to a group that received a structured box model training curriculum, and a control group. Performance before and after the training was assessed in a virtual reality laparoscopic trainer (LapSim and was based on objective parameters, registered by the computer system (time, error, and economy of motion scores). Group A showed significantly greater improvement in all performance parameters compared with the control group: economy of movement (p=0.001), time (p=0.001) and tissue damage (p=0.036), confirming the positive impact of box-trainer curriculum on laparoscopic skills acquisition. Structured laparoscopic skill training on a low cost box model trainer improves performance as assessed using the VR system. Trainees who used the box model trainer showed significant improvement compared to the control group. Box model trainers are valid tools for laparoscopic skills training and should be implemented in the comprehensive training curricula in gynaecology.

The Local Ensemble Transform Kalman Filter with the Weather Research and Forecasting Model: Experiments with Real Observations

NASA Astrophysics Data System (ADS)

Miyoshi, Takemasa; Kunii, Masaru

2012-03-01

The local ensemble transform Kalman filter (LETKF) is implemented with the Weather Research and Forecasting (WRF) model, and real observations are assimilated to assess the newly-developed WRF-LETKF system. The WRF model is a widely-used mesoscale numerical weather prediction model, and the LETKF is an ensemble Kalman filter (EnKF) algorithm particularly efficient in parallel computer architecture. This study aims to provide the basis of future research on mesoscale data assimilation using the WRF-LETKF system, an additional testbed to the existing EnKF systems with the WRF model used in the previous studies. The particular LETKF system adopted in this study is based on the system initially developed in 2004 and has been continuously improved through theoretical studies and wide applications to many kinds of dynamical models including realistic geophysical models. Most recent and important improvements include an adaptive covariance inflation scheme which considers the spatial and temporal inhomogeneity of inflation parameters. Experiments show that the LETKF successfully assimilates real observations and that adaptive inflation is advantageous. Additional experiments with various ensemble sizes show that using more ensemble members improves the analyses consistently.

Quarkonium production in an improved color evaporation model

DOE PAGES

Ma, Yan-Qing; Vogt, Ramona

2016-12-27

In this paper, we propose an improved version of the color evaporation model to describe heavy quarkonium production. In contrast to the traditional color evaporation model, we impose the constraint that the invariant mass of the intermediate heavy quark-antiquark pair be larger than the mass of produced quarkonium. We also introduce a momentum shift between the heavy quark-antiquark pair and the quarkonium. Finally, numerical calculations show that our model can describe the charmonium yields as well as the ratio of ψ' over J/ψ better than the traditional color evaporation model.

FOG Random Drift Signal Denoising Based on the Improved AR Model and Modified Sage-Husa Adaptive Kalman Filter.

PubMed

Sun, Jin; Xu, Xiaosu; Liu, Yiting; Zhang, Tao; Li, Yao

2016-07-12

In order to reduce the influence of fiber optic gyroscope (FOG) random drift error on inertial navigation systems, an improved auto regressive (AR) model is put forward in this paper. First, based on real-time observations at each restart of the gyroscope, the model of FOG random drift can be established online. In the improved AR model, the FOG measured signal is employed instead of the zero mean signals. Then, the modified Sage-Husa adaptive Kalman filter (SHAKF) is introduced, which can directly carry out real-time filtering on the FOG signals. Finally, static and dynamic experiments are done to verify the effectiveness. The filtering results are analyzed with Allan variance. The analysis results show that the improved AR model has high fitting accuracy and strong adaptability, and the minimum fitting accuracy of single noise is 93.2%. Based on the improved AR(3) model, the denoising method of SHAKF is more effective than traditional methods, and its effect is better than 30%. The random drift error of FOG is reduced effectively, and the precision of the FOG is improved.

Cultural competence in mental health care: a review of model evaluations

PubMed Central

Bhui, Kamaldeep; Warfa, Nasir; Edonya, Patricia; McKenzie, Kwame; Bhugra, Dinesh

2007-01-01

Background Cultural competency is now a core requirement for mental health professionals working with culturally diverse patient groups. Cultural competency training may improve the quality of mental health care for ethnic groups. Methods A systematic review that included evaluated models of professional education or service delivery. Results Of 109 potential papers, only 9 included an evaluation of the model to improve the cultural competency practice and service delivery. All 9 studies were located in North America. Cultural competency included modification of clinical practice and organizational performance. Few studies published their teaching and learning methods. Only three studies used quantitative outcomes. One of these showed a change in attitudes and skills of staff following training. The cultural consultation model showed evidence of significant satisfaction by clinicians using the service. No studies investigated service user experiences and outcomes. Conclusion There is limited evidence on the effectiveness of cultural competency training and service delivery. Further work is required to evaluate improvement in service users' experiences and outcomes. PMID:17266765

Improving Synoptic and Intra-Seasonnal Variability in CFS via a Better Representation of Organized Convection

NASA Astrophysics Data System (ADS)

Khouider, B.; Goswami, B. B.; Majda, A.; Krishna, R. P. M. M.; Mukhopadhyay, P.

2016-12-01

Improvements in the capability of climate models to realistically capture the synoptic and intra-seasonnal variability, associated with tropical rainfall, are conditioned by improvement in the representation of the subgrid variability due to organized convection and the underlying two-way interactions through multiple scales and thus breaking with the quasi-equilibrium bottleneck. By design, the stochastic multi-cloud model (SMCM) mimics the life cycle of organized tropical convective systems and the interactions of the associated cloud types with each other and with large scales, as it is observed. It is based a lattice particle interaction model for predefined microscopic (subgrid) sites that make random transitions from one cloud type to another conditional to the large scale state. In return the SMCM provides the cloud type area fractions on the form of a Markov chain model which can be run in parallel with the climate model without any significant computational overhead. The SMCM was previously successfully tested in both reduced complexity tropical models and an aquaplanet global atmospheric model. Here, we report for the first time the results of its implementation in the fully coupled NCEP climate model (CFSv2) through the used of prescribed vertical profiles of heating and drying obtained from observations. While many known biases in CFSv2 have been slightly improved there are no noticeable degradation in the simulated mean climatology. Nonetheless, comparison with observations show that the improvements in terms of synoptic and intra-seasonnal variability are spectacular, despite the fact that CFSv2 is one of the best models in this regard. In particular, while CFSv2 exaggerates the intra-seasonnal variance at the expense of the synoptic contribution, the CFS-SMCM shows a good balance between the two as in the observations.

Modeling and analysis of Galfenol cantilever vibration energy harvester with nonlinear magnetic force

NASA Astrophysics Data System (ADS)

Cao, Shuying; Sun, Shuaishuai; Zheng, Jiaju; Wang, Bowen; Wan, Lili; Pan, Ruzheng; Zhao, Ran; Zhang, Changgeng

2018-05-01

Galfenol traditional cantilever energy harvesters (TCEHs) have bigger electrical output only at resonance and exhibit nonlinear mechanical-magnetic-electric coupled (NMMEC) behaviors. To increase low-frequency broadband performances of a TCEH, an improved CEH (ICEH) with magnetic repulsive force is studied. Based on the magnetic dipole model, the nonlinear model of material, the Faraday law and the dynamic principle, a lumped parameter NMMEC model of the devices is established. Comparisons between the calculated and measured results show that the proposed model can provide reasonable data trends of TCEH under acceleration, bias field and different loads. Simulated results show that ICEH exhibits low-frequency resonant, hard spring and bistable behaviors, thus can harvest more low-frequency broadband vibration energy than TCEH, and can elicit snap-through and generate higher voltage even under weak noise. The proposed structure and model are useful for improving performances of the devices.

The use of self-modeling to improve the swimming performance of spina bifida children.

PubMed Central

Dowrick, P W; Dove, C

1980-01-01

The use of edited videotape replay (which showed only "positive" behaviors) to improve the water skills of three spina bifida children, aged 5 to 10 years was examined. A multiple baseline across subjects design was used, and behavioral changes were observed to occur in close association with intervention. One child was given successive reapplications of videotaped self-modeling with continuing improvements. It appears that a useful practical technique has been developed. PMID:6988381

Curcumin Monoglucoside Shows Improved Bioavailability and Mitigates Rotenone Induced Neurotoxicity in Cell and Drosophila Models of Parkinson's Disease.

PubMed

Pandareesh, M D; Shrivash, M K; Naveen Kumar, H N; Misra, K; Srinivas Bharath, M M

2016-11-01

Curcumin (CUR), a dietary polyphenol has diverse pharmacologic effects, but is limited by poor bioavailability. This is probably due to decreased solubility, cellular uptake and stability. In order to enhance its solubility and bioavailability, we synthesized the CUR bioconjugate curcumin monoglucoside (CMG) and tested its bioavailability, neuroprotective and anti-apoptotic propensity against rotenone (ROT) induced toxicity in N27 dopaminergic neuronal cells and Drosophila models. Our results elucidate that CMG showed improved bioavailability than CUR in N27 cells. Pre-treatment with CMG protected against ROT neurotoxicity and exerted antioxidant effects by replenishing cellular glutathione levels and significantly decreasing reactive species. CMG pre-treatment also restored mitochondrial complex I and IV activities inhibited by ROT. ROT-induced nuclear damage was also restored by CMG as confirmed by comet assay. CMG induced anti-apoptotic effects was substantiated by decreased phosporylation of JNK3 and c-jun, which in turn decreased the cleavage of pro-caspase 3. Q-PCR analysis of redox genes showed up-regulation of NOS2 and down-regulation of NQO1 upon ROT exposure and this was attenuated by CMG pre-treatment. Studies in the Drosophila ROT model revealed that, CMG administration showed better survival rate and locomotor activity, improved antioxidant activity and dopamine content than ROT treated group and was comparable with the CUR group. Based on these data, we surmise that CMG has improved bioavailability and offered neuroprotection comparable with CUR, against ROT-induced toxicity both in dopaminergic neuronal cell line and Drosophila models, with therapeutic implications for PD.

Improving of local ozone forecasting by integrated models.

PubMed

Gradišar, Dejan; Grašič, Boštjan; Božnar, Marija Zlata; Mlakar, Primož; Kocijan, Juš

2016-09-01

This paper discuss the problem of forecasting the maximum ozone concentrations in urban microlocations, where reliable alerting of the local population when thresholds have been surpassed is necessary. To improve the forecast, the methodology of integrated models is proposed. The model is based on multilayer perceptron neural networks that use as inputs all available information from QualeAria air-quality model, WRF numerical weather prediction model and onsite measurements of meteorology and air pollution. While air-quality and meteorological models cover large geographical 3-dimensional space, their local resolution is often not satisfactory. On the other hand, empirical methods have the advantage of good local forecasts. In this paper, integrated models are used for improved 1-day-ahead forecasting of the maximum hourly value of ozone within each day for representative locations in Slovenia. The WRF meteorological model is used for forecasting meteorological variables and the QualeAria air-quality model for gas concentrations. Their predictions, together with measurements from ground stations, are used as inputs to a neural network. The model validation results show that integrated models noticeably improve ozone forecasts and provide better alert systems.

Improving School Effectiveness: The Dynamics of Implementation.

ERIC Educational Resources Information Center

Gaynor, Alan K.; Clauset, Karl H., Jr.

Six causal-influence diagrams introduce a model showing the difficulties of implementing school improvement policies in such areas as teacher expectations and student behavior. The first diagram deals with basic dynamics of improvement. Cybernetics of activities to correct student achievement are revealed in a second diagram, which portrays…

OCEANFILMS-2: Representing coadsorption of saccharides in marine films and potential impacts on modeled marine aerosol chemistry

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

Burrows, Susannah M.; Gobrogge, Eric; Fu, Li

Here we show that the addition of chemical interactions of soluble polysaccharides with a surfactant monolayer improves agreement of modeled sea spray chemistry with observed marine aerosol chemistry. In particular, the fraction of hydroxyl functional groups in modeled sea spray organic matter is increased, improving agreement with FTIR observations of marine aerosol composition. The overall organic fraction of submicron sea spray also increases, allowing organic mass fractions in the range 0.5 – 0.7 for submicron sea spray particles over highly active phytoplankton blooms. We show results from Sum Frequency Generation (SFG) experiments that support the modeling approach, by demonstrating thatmore » soluble polysaccharides can strongly adsorb to a lipid monolayer via columbic interactions under appropriate conditions.« less

Decadal climate predictions improved by ocean ensemble dispersion filtering

NASA Astrophysics Data System (ADS)

Kadow, C.; Illing, S.; Kröner, I.; Ulbrich, U.; Cubasch, U.

2017-06-01

Decadal predictions by Earth system models aim to capture the state and phase of the climate several years in advance. Atmosphere-ocean interaction plays an important role for such climate forecasts. While short-term weather forecasts represent an initial value problem and long-term climate projections represent a boundary condition problem, the decadal climate prediction falls in-between these two time scales. In recent years, more precise initialization techniques of coupled Earth system models and increased ensemble sizes have improved decadal predictions. However, climate models in general start losing the initialized signal and its predictive skill from one forecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improved by a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. We found that this procedure, called ensemble dispersion filter, results in more accurate results than the standard decadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions show an increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with larger ensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from ocean ensemble dispersion filtering toward the ensemble mean.