WorldWideScience

Sample records for ocean-atmospheric prediction studies

  1. The Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS)

    National Research Council Canada - National Science Library

    Hodur, Richard M; Hong, Xiaodong; Doyle, James D; Pullen, Julie; Cummings, James; Martin, Paul; Rennick, Mary Alice

    2002-01-01

    ... of the Couple Ocean/Atmosphere Mesoscale Prediction System (COAMPS). The goal of this modeling project is to gain predictive skill in simulating the ocean and atmosphere at high resolution on time-scales of hours to several days...

  2. South African seasonal rainfall prediction performance by a coupled ocean-atmosphere model

    CSIR Research Space (South Africa)

    Landman, WA

    2010-12-01

    Full Text Available Evidence is presented that coupled ocean-atmosphere models can already outscore computationally less expensive atmospheric models. However, if the atmospheric models are forced with highly skillful SST predictions, they may still be a very strong...

  3. Initial conditions and ENSO prediction using a coupled ocean-atmosphere model

    Science.gov (United States)

    Larow, T. E.; Krishnamurti, T. N.

    1998-01-01

    A coupled ocean-atmosphere initialization scheme using Newtonian relaxation has been developed for the Florida State University coupled ocean-atmosphere global general circulation model. The initialization scheme is used to initialize the coupled model for seasonal forecasting the boreal summers of 1987 and 1988. The atmosphere model is a modified version of the Florida State University global spectral model, resolution T-42. The ocean general circulation model consists of a slightly modified version of the Hamburg's climate group model described in Latif (1987) and Latif et al. (1993). The coupling is synchronous with information exchanged every two model hours. Using ECMWF atmospheric daily analysis and observed monthly mean SSTs, two, 1-year, time-dependent, Newtonian relaxation were performed using the coupled model prior to conducting the seasonal forecasts. The coupled initializations were conducted from 1 June 1986 to 1 June 1987 and from 1 June 1987 to 1 June 1988. Newtonian relaxation was applied to the prognostic atmospheric vorticity, divergence, temperature and dew point depression equations. In the ocean model the relaxation was applied to the surface temperature. Two, 10-member ensemble integrations were conducted to examine the impact of the coupled initialization on the seasonal forecasts. The initial conditions used for the ensembles are the ocean's final state after the initialization and the atmospheric initial conditions are ECMWF analysis. Examination of the SST root mean square error and anomaly correlations between observed and forecasted SSTs in the Niño-3 and Niño-4 regions for the 2 seasonal forecasts, show closer agreement between the initialized forecast than two, 10-member non-initialized ensemble forecasts. The main conclusion here is that a single forecast with the coupled initialization outperforms, in SST anomaly prediction, against each of the control forecasts (members of the ensemble) which do not include such an initialization

  4. Thermodynamic ocean-atmosphere Coupling and the Predictability of Nordeste rainfall

    Science.gov (United States)

    Chang, P.; Saravanan, R.; Giannini, A.

    2003-04-01

    The interannual variability of rainfall in the northeastern region of Brazil, or Nordeste, is known to be very strongly correlated with sea surface temperature (SST) variability, of Atlantic and Pacific origin. For this reason the potential predictability of Nordeste rainfall is high. The current generation of state-of-the-art atmospheric models can replicate the observed rainfall variability with high skill when forced with the observed record of SST variability. The correlation between observed and modeled indices of Nordeste rainfall, in the AMIP-style integrations with two such models (NSIPP and CCM3) analyzed here, is of the order of 0.8, i.e. the models explain about 2/3 of the observed variability. Assuming that thermodynamic, ocean-atmosphere heat exchange plays the dominant role in tropical Atlantic SST variability on the seasonal to interannual time scale, we analyze its role in Nordeste rainfall predictability using an atmospheric general circulation model coupled to a slab ocean model. Predictability experiments initialized with observed December SST show that thermodynamic coupling plays a significant role in enhancing the persistence of SST anomalies, both in the tropical Pacific and in the tropical Atlantic. We show that thermodynamic coupling is sufficient to provide fairly accurate forecasts of tropical Atlantic SST in the boreal spring that are significantly better than the persistence forecasts. The consequences for the prediction of Nordeste rainfall are analyzed.

  5. AMS Observations over Coastal California from the Biological and Oceanic Atmospheric Study (BOAS)

    Science.gov (United States)

    Bates, K. H.; Coggon, M. M.; Hodas, N.; Negron, A.; Ortega, A. M.; Crosbie, E.; Sorooshian, A.; Nenes, A.; Flagan, R. C.; Seinfeld, J.

    2015-12-01

    In July 2015, fifteen research flights were conducted on a US Navy Twin Otter aircraft as part of the Biological and Oceanic Atmospheric Study (BOAS) campaign. The flights took place near the California coast at Monterey, to investigate the effects of sea surface temperature and algal blooms on oceanic particulate emissions, the diurnal mixing of urban pollution with other airmasses, and the impacts of biological aerosols on the California atmosphere. The aircraft's payload included an aerosol mass spectrometer (AMS), a differential mobility analyzer, a cloud condensation nuclei counter, a counterflow virtual impactor, a cloudwater collector, and two instruments designed to detect biological aerosols - a wideband integrated biological spectrometer and a SpinCon II - as well as a number of meteorology and aerosol probes, two condensation particle counters, and instruments to measure gas-phase CO, CO2, O3, and NOx. Here, we describe in depth the objectives and outcomes of BOAS and report preliminary results, primarily from the AMS. We detail the spatial characteristics and meteorological variability of speciated aerosol components over a strong and persistent bloom of Pseudo-Nitzschia, the harmful algae that cause 'red tide', and report newly identified AMS markers for biological particles. Finally, we compare these results with data collected during BOAS over urban, forested, and agricultural environments, and describe the mixing observed between oceanic and terrestrial airmasses.

  6. South African mid-summer seasonal rainfall prediction performance by a coupled ocean-atmosphere model

    CSIR Research Space (South Africa)

    Landman, WA

    2011-01-01

    Full Text Available . 2000; Goddard and Mason, 2002). Such a so-called two-tiered procedure to predict the outcome of the rainfall season has been employed in South Africa for a number of years already (e.g., Landman et al., 2001). The advent of fully coupled ocean...

  7. Ocean-Atmosphere Coupling Processes Affecting Predictability in the Climate System

    Science.gov (United States)

    Miller, A. J.; Subramanian, A. C.; Seo, H.; Eliashiv, J. D.

    2017-12-01

    Predictions of the ocean and atmosphere are often sensitive to coupling at the air-sea interface in ways that depend on the temporal and spatial scales of the target fields. We will discuss several aspects of these types of coupled interactions including oceanic and atmospheric forecast applications. For oceanic mesoscale eddies, the coupling can influence the energetics of the oceanic flow itself. For Madden-Julian Oscillation onset, the coupling timestep should resolve the diurnal cycle to properly raise time-mean SST and latent heat flux prior to deep convection. For Atmospheric River events, the evolving SST field can alter the trajectory and intensity of precipitation anomalies along the California coast. Improvements in predictions will also rely on identifying and alleviating sources of biases in the climate states of the coupled system. Surprisingly, forecast skill can also be improved by enhancing stochastic variability in the atmospheric component of coupled models as found in a multiscale ensemble modeling approach.

  8. On Verifying Currents and Other Features in the Hawaiian Islands Region Using Fully Coupled Ocean/Atmosphere Mesoscale Prediction System Compared to Global Ocean Model and Ocean Observations

    Science.gov (United States)

    Jessen, P. G.; Chen, S.

    2014-12-01

    This poster introduces and evaluates features concerning the Hawaii, USA region using the U.S. Navy's fully Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS-OS™) coupled to the Navy Coastal Ocean Model (NCOM). It also outlines some challenges in verifying ocean currents in the open ocean. The system is evaluated using in situ ocean data and initial forcing fields from the operational global Hybrid Coordinate Ocean Model (HYCOM). Verification shows difficulties in modelling downstream currents off the Hawaiian islands (Hawaii's wake). Comparing HYCOM to NCOM current fields show some displacement of small features such as eddies. Generally, there is fair agreement from HYCOM to NCOM in salinity and temperature fields. There is good agreement in SSH fields.

  9. Ocean-atmosphere interactions during cyclone Nargis

    Digital Repository Service at National Institute of Oceanography (India)

    Mc; Foltz, G.R.; Lee, T.; Murty, V.S.N.; Ravichandran, M.; Vecchi, G.A.; Vialard, J.; Wiggert, J.D.; Yu, L.

    =UTF-8 Author version: EOS: Trans. Am. Geophys. Union: 90(7); 2009; 53-60; doi:10.1029/2009EO070001 Ocean-Atmosphere Interactions During Cyclone Nargis M. J. McPhaden (1) , G. R. Foltz (2) , T. Lee (3) , V. S. N. Murty (4) , M... Moored Array for African-Asian-Australian Monsoon Analysis and Prediction; McPhaden et al, 2008) designed to complement a constellation of earth observing satellites for key environmental parameters such as winds, sea surface temperature (SST), and sea...

  10. Land-Ocean-Atmospheric Coupling Associated with Earthquakes

    Science.gov (United States)

    Prasad, A. K.; Singh, R. P.; Kumar, S.; Cervone, G.; Kafatos, M.; Zlotnicki, J.

    2007-12-01

    Earthquakes are well known to occur along the plate boundaries and also on the stable shield. The recent studies have shown existence of strong coupling between land-ocean-atmospheric parameters associated with the earthquakes. We have carried out detailed analysis of multi sensor data (optical and microwave remote) to show existence of strong coupling between land-ocean-atmospheric parameters associated with the earthquakes with focal depth up to 30 km and magnitude greater than 5.5. Complimentary nature of various land, ocean and atmospheric parameters will be demonstrated in getting an early warning information about an impending earthquake.

  11. Northern hemisphere storm tracks during the last glacial maximum in the PMIP2 ocean-atmosphere coupled models: energetic study, seasonal cycle, precipitation

    Energy Technology Data Exchange (ETDEWEB)

    Laine, A.; Kageyama, M.; Ramstein, G.; Peterschmitt, J.Y. [LSCE/IPSL, UMR CEA-CNRS-UVSQ 1572, CE Saclay, Gif-sur-Yvette Cedex (France); Salas-Melia, D.; Voldoire, A.; Riviere, G.; Planton, S.; Tyteca, S. [CNRM-GAME, URA CNRS-Meteo-France 1357, Toulouse Cedex 01 (France)

    2009-04-15

    Mid-latitude eddies are an important component of the climatic system due to their role in transporting heat, moisture and momentum from the tropics to the poles, and also for the precipitation associated with their fronts, especially in winter. We study northern hemisphere storm-tracks at the Last Glacial Maximum (LGM) and their influence on precipitation using ocean-atmosphere general circulation model (OAGCM) simulations from the second phase of the Paleoclimate Modelling Intercomparison Project (PMIP2). The difference with PMIP1 results in terms of sea-surface temperature forcing, fundamental for storm-track dynamics, is large, especially in the eastern North Atlantic where sea-ice extends less to the south in OAGCMs compared to atmospheric-only GCMs. Our analyses of the physics of the eddies are based on the equations of eddy energetics. All models simulate a consistent southeastward shift of the North Pacific storm-track in winter, related to a similar displacement of the jet stream, partly forced by the eddies themselves. Precipitation anomalies are consistent with storm-track changes, with a southeastward displacement of the North Pacific precipitation pattern. The common features of North Atlantic changes in the LGM simulations consist of a thinning of the storm-track in its western part and an amplification of synoptic activity to the southeast, in the region between the Azores Islands and the Iberian Peninsula, which reflects on precipitation. This southeastward extension is related to a similar displacement of the jet, partly forced by the eddies. In the western North Atlantic, the synoptic activity anomalies are at first order related to baroclinic generation term anomalies, but the mean-flow baroclinicity increase due to the presence of the Laurentide ice-sheet is partly balanced by a loss of eddy efficiency to convert energy from the mean flow. Moisture availability in this region is greatly reduced due to more advection of dry polar air by

  12. Ocean-Atmosphere Interactions Modulate Irrigation's Climate Impacts

    Science.gov (United States)

    Krakauer, Nir Y.; Puma, Michael J.; Cook, Benjamin I.; Gentine, Pierre; Nazarenko, Larissa

    2016-01-01

    Numerous studies have focused on the local and regional climate effects of irrigated agriculture and other land cover and land use change (LCLUC) phenomena, but there are few studies on the role of ocean- atmosphere interaction in modulating irrigation climate impacts. Here, we compare simulations with and without interactive sea surface temperatures of the equilibrium effect on climate of contemporary (year 2000) irrigation geographic extent and intensity. We find that ocean-atmosphere interaction does impact the magnitude of global-mean and spatially varying climate impacts, greatly increasing their global reach. Local climate effects in the irrigated regions remain broadly similar, while non-local effects, particularly over the oceans, tend to be larger. The interaction amplifies irrigation-driven standing wave patterns in the tropics and mid-latitudes in our simulations, approximately doubling the global-mean amplitude of surface temperature changes due to irrigation. The fractions of global area experiencing significant annual-mean surface air temperature and precipitation change also approximately double with ocean-atmosphere interaction. Subject to confirmation with other models, these findings imply that LCLUC is an important contributor to climate change even in remote areas such as the Southern Ocean, and that attribution studies should include interactive oceans and need to consider LCLUC, including irrigation, as a truly global forcing that affects climate and the water cycle over ocean as well as land areas.

  13. Uncertainty in the ocean-atmosphere feedbacks associated with ENSO in the reanalysis products

    Energy Technology Data Exchange (ETDEWEB)

    Kumar, Arun; Hu, Zeng-Zhen [NCEP/NWS/NOAA, Climate Prediction Center, Camp Springs, MD (United States)

    2012-08-15

    The evolution of El Nino-Southern Oscillation (ENSO) variability can be characterized by various ocean-atmosphere feedbacks, for example, the influence of ENSO related sea surface temperature (SST) variability on the low-level wind and surface heat fluxes in the equatorial tropical Pacific, which in turn affects the evolution of the SST. An analysis of these feedbacks requires physically consistent observational data sets. Availability of various reanalysis data sets produced during the last 15 years provides such an opportunity. A consolidated estimate of ocean surface fluxes based on multiple reanalyses also helps understand biases in ENSO predictions and simulations from climate models. In this paper, the intensity and the spatial structure of ocean-atmosphere feedback terms (precipitation, surface wind stress, and ocean surface heat flux) associated with ENSO are evaluated for six different reanalysis products. The analysis provides an estimate for the feedback terms that could be used for model validation studies. The analysis includes the robustness of the estimate across different reanalyses. Results show that one of the ''coupled'' reanalysis among the six investigated is closer to the ensemble mean of the results, suggesting that the coupled data assimilation may have the potential to better capture the overall atmosphere-ocean feedback processes associated with ENSO than the uncoupled ones. (orig.)

  14. Comprehensive Ocean - Atmosphere Data Set (COADS) LMRF Arctic Subset

    Data.gov (United States)

    National Aeronautics and Space Administration — The Comprehensive Ocean - Atmosphere Data Set (COADS) LMRF Arctic subset contains marine surface weather reports for the region north of 65 degrees N from ships,...

  15. Processes analysis of ocean-atmosphere interaction in Colombian marine areas

    International Nuclear Information System (INIS)

    Melo, Jeimmy; Pabon Caicedo, Jose Daniel

    2002-01-01

    This document shows the importance to understanding the processes of interaction ocean-atmosphere by means of the knowledge of the behavior of the physical and biological processes in the Colombian marine areas. For such aim, it was studied the production of the pigment concentration (chlorophyll-a) by means the state of the sea surface temperature and the atmospheric dynamics for year 2001

  16. Flexible global ocean-atmosphere-land system model. A modeling tool for the climate change research community

    International Nuclear Information System (INIS)

    Zhou, Tianjun; Yu, Yongqiang; Liu, Yimin; Wang, Bin

    2014-01-01

    First book available on systematic evaluations of the performance of the global climate model FGOALS. Covers the whole field, ranging from the development to the applications of this climate system model. Provide an outlook for the future development of the FGOALS model system. Offers brief introduction about how to run FGOALS. Coupled climate system models are of central importance for climate studies. A new model known as FGOALS (the Flexible Global Ocean-Atmosphere-Land System model), has been developed by the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences (LASG/IAP, CAS), a first-tier national geophysical laboratory. It serves as a powerful tool, both for deepening our understanding of fundamental mechanisms of the climate system and for making decadal prediction and scenario projections of future climate change. ''Flexible Global Ocean-Atmosphere-Land System Model: A Modeling Tool for the Climate Change Research Community'' is the first book to offer systematic evaluations of this model's performance. It is comprehensive in scope, covering both developmental and application-oriented aspects of this climate system model. It also provides an outlook of future development of FGOALS and offers an overview of how to employ the model. It represents a valuable reference work for researchers and professionals working within the related areas of climate variability and change.

  17. Flexible global ocean-atmosphere-land system model. A modeling tool for the climate change research community

    Energy Technology Data Exchange (ETDEWEB)

    Zhou, Tianjun; Yu, Yongqiang; Liu, Yimin; Wang, Bin (eds.) [Chinese Academy of Sciences, Beijing, (China). Inst. of Atmospheric Physics

    2014-04-01

    First book available on systematic evaluations of the performance of the global climate model FGOALS. Covers the whole field, ranging from the development to the applications of this climate system model. Provide an outlook for the future development of the FGOALS model system. Offers brief introduction about how to run FGOALS. Coupled climate system models are of central importance for climate studies. A new model known as FGOALS (the Flexible Global Ocean-Atmosphere-Land System model), has been developed by the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences (LASG/IAP, CAS), a first-tier national geophysical laboratory. It serves as a powerful tool, both for deepening our understanding of fundamental mechanisms of the climate system and for making decadal prediction and scenario projections of future climate change. ''Flexible Global Ocean-Atmosphere-Land System Model: A Modeling Tool for the Climate Change Research Community'' is the first book to offer systematic evaluations of this model's performance. It is comprehensive in scope, covering both developmental and application-oriented aspects of this climate system model. It also provides an outlook of future development of FGOALS and offers an overview of how to employ the model. It represents a valuable reference work for researchers and professionals working within the related areas of climate variability and change.

  18. Longitudinal biases in the Seychelles Dome simulated by 35 ocean-atmosphere coupled general circulation models

    Science.gov (United States)

    Nagura, Motoki; Sasaki, Wataru; Tozuka, Tomoki; Luo, Jing-Jia; Behera, Swadhin K.; Yamagata, Toshio

    2013-02-01

    Seychelles Dome refers to the shallow climatological thermocline in the southwestern Indian Ocean, where ocean wave dynamics efficiently affect sea surface temperature, allowing sea surface temperature anomalies to be predicted up to 1-2 years in advance. Accurate reproduction of the dome by ocean-atmosphere coupled general circulation models (CGCMs) is essential for successful seasonal predictions in the Indian Ocean. This study examines the Seychelles Dome as simulated by 35 CGCMs, including models used in phase five of the Coupled Model Intercomparison Project (CMIP5). Among the 35 CGCMs, 14 models erroneously produce an upwelling dome in the eastern half of the basin whereas the observed Seychelles Dome is located in the southwestern tropical Indian Ocean. The annual mean Ekman pumping velocity in these models is found to be almost zero in the southern off-equatorial region. This result is inconsistent with observations, in which Ekman upwelling acts as the main cause of the Seychelles Dome. In the models reproducing an eastward-displaced dome, easterly biases are prominent along the equator in boreal summer and fall, which result in shallow thermocline biases along the Java and Sumatra coasts via Kelvin wave dynamics and a spurious upwelling dome in the region. Compared to the CMIP3 models, the CMIP5 models are even worse in simulating the dome longitudes.

  19. Coupled Regional Ocean-Atmosphere Modeling of the Mount Pinatubo Impact on the Red Sea

    Science.gov (United States)

    Stenchikov, G. L.; Osipov, S.

    2017-12-01

    The 1991 eruption of Mount Pinatubo had dramatic effects on the regional climate in the Middle East. Though acknowledged, these effects have not been thoroughly studied. To fill this gap and to advance understanding of the mechanisms that control variability in the Middle East's regional climate, we simulated the impact of the 1991 Pinatubo eruption using a regional coupled ocean-atmosphere modeling system set for the Middle East and North Africa (MENA) domain. We used the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) framework, which couples the Weather Research and Forecasting Model (WRF) model with the Regional Oceanic Modeling System (ROMS). We modified the WRF model to account for the radiative effect of volcanic aerosols. Our coupled ocean-atmosphere simulations verified by available observations revealed strong perturbations in the energy balance of the Red Sea, which drove thermal and circulation responses. Our modeling approach allowed us to separate changes in the atmospheric circulation caused by the impact of the volcano from direct regional radiative cooling from volcanic aerosols. The atmospheric circulation effect was significantly stronger than the direct volcanic aerosols effect. We found that the Red Sea response to the Pinatubo eruption was stronger and qualitatively different from that of the global ocean system. Our results suggest that major volcanic eruptions significantly affect the climate in the Middle East and the Red Sea and should be carefully taken into account in assessments of long-term climate variability and warming trends in MENA and the Red Sea.

  20. Ocean-atmosphere coupled climate model development at SAWS: description and diagnosis

    CSIR Research Space (South Africa)

    Beraki, A

    2011-09-01

    Full Text Available This paper introduces the South African Weather Service's coupled ocean-atmosphere model. The paper also demonstrates the advances made in configuring an operational coupled ocean-atmosphere model in South Africa for seasonal forecast production...

  1. Ocean-Atmosphere Interaction in Climate Changes

    Science.gov (United States)

    Liu, W. Timothy

    1999-01-01

    The diagram, which attests the El Nino teleconnection observed by the NASA Scatterometer (NSCAT) in 1997, is an example of the results of our research in air-sea interaction - the core component of our three-part contribution to the Climate Variability Program. We have established an interplay among scientific research, which turns spacebased data into knowledge, a push in instrument technology, which improves observations of climate variability, and an information system, which produces and disseminates new data to support our scientific research. Timothy Liu led the proposal for advanced technology, in response to the NASA Post-2002 Request for Information. The sensor was identified as a possible mission for continuous ocean surface wind measurement at higher spatial resolution, and with the unique capability to measure ocean surface salinity. He is participating in the Instrument Incubator Program to improve the antenna technology, and is initiating a study to integrate the concept on Japanese missions. He and his collaborators have set up a system to produce and disseminate high level (gridded) ocean surface wind/stress data from NSCAT and European missions. The data system is being expanded to produce real-time gridded ocean surface winds from Quikscat, and precipitation and evaporation from the Tropical Rain Measuring Mission. It will form the basis for a spacebased data analysis system which will include momentum, heat and water fluxes. The study on 1997 El Nino teleconnection illustrates our interdisciplinary and multisensor approach to study climate variability. The diagram shows that the collapse of trade wind and the westerly wind anomalies in the central equatorial Pacific led to the equatorial ocean warming. The equatorial wind anomalies are connected to the anomalous cyclonic wind pattern in the northeast Pacific. The anomalous warming along the west coast of the United States is the result of the movement of the pre-existing warm sea surface

  2. Accelerated Prediction of the Polar Ice and Global Ocean (APPIGO)

    Science.gov (United States)

    2014-09-30

    APPIGO) Eric Chassignet Center for Ocean-Atmosphere Prediction Studies (COAPS) Florida State University PO Box 3062840 Tallahassee, FL 32306...PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Florida Atlantic University,Center for Ocean-Atmosphere Prediction Studies (COAPS),PO Box 3062840...Cavalieri, D. J., C. I. Parkinson , P. Gloersen, and H. J. Zwally. 1997. Arctic and Antarctic Sea Ice Concentrations from Multichannel Passive-Microwave

  3. Skill Assessment of a Spectral Ocean-Atmosphere Radiative Model

    Science.gov (United States)

    Gregg, Watson, W.; Casey, Nancy W.

    2009-01-01

    Ocean phytoplankton, detrital material, and water absorb and scatter light spectrally. The Ocean- Atmosphere Spectral Irradiance Model (OASIM) is intended to provide surface irradiance over the oceans with sufficient spectral resolution to support ocean ecology, biogeochemistry, and heat exchange investigations, and of sufficient duration to support inter-annual and decadal investigations. OASIM total surface irradiance (integrated 200 nm to 4 microns) was compared to in situ data and three publicly available global data products at monthly 1-degree resolution. OASIM spectrally-integrated surface irradiance had root mean square (RMS) difference= 20.1 W/sq m (about 11%), bias=1.6 W/sq m (about 0.8%), regression slope= 1.01 and correlation coefficient= 0.89, when compared to 2322 in situ observations. OASIM had the lowest bias of any of the global data products evaluated (ISCCP-FD, NCEP, and ISLSCP 11), and the best slope (nearest to unity). It had the second best RMS, and the third best correlation coefficient. OASIM total surface irradiance compared well with ISCCP-FD (RMS= 20.7 W/sq m; bias=-11.4 W/sq m, r=0.98) and ISLSCP II (RMS =25.2 W/sq m; bias= -13.8 W/sq m; r=0.97), but less well with NCEP (RMS =43.0 W/sq m ;bias=-22.6 W/sq m; x=0.91). Comparisons of OASIM photosynthetically available radiation (PAR) with PAR derived from SeaWiFS showed low bias (-1.8 mol photons /sq m/d, or about 5%), RMS (4.25 mol photons /sq m/d ' or about 12%), near unity slope (1.03) and high correlation coefficient (0.97). Coupled with previous estimates of clear sky spectral irradiance in OASIM (6.6% RMS at 1 nm resolution), these results suggest that OASIM provides reasonable estimates of surface broadband and spectral irradiance in the oceans, and can support studies on ocean ecosystems, carbon cycling, and heat exchange.

  4. Observations of Recent Arctic Sea Ice Volume Loss and Its Impact on Ocean-Atmosphere Energy Exchange and Ice Production

    Science.gov (United States)

    Kurtz, N. T.; Markus, T.; Farrell, S. L.; Worthen, D. L.; Boisvert, L. N.

    2011-01-01

    Using recently developed techniques we estimate snow and sea ice thickness distributions for the Arctic basin through the combination of freeboard data from the Ice, Cloud, and land Elevation Satellite (ICESat) and a snow depth model. These data are used with meteorological data and a thermodynamic sea ice model to calculate ocean-atmosphere heat exchange and ice volume production during the 2003-2008 fall and winter seasons. The calculated heat fluxes and ice growth rates are in agreement with previous observations over multiyear ice. In this study, we calculate heat fluxes and ice growth rates for the full distribution of ice thicknesses covering the Arctic basin and determine the impact of ice thickness change on the calculated values. Thinning of the sea ice is observed which greatly increases the 2005-2007 fall period ocean-atmosphere heat fluxes compared to those observed in 2003. Although there was also a decline in sea ice thickness for the winter periods, the winter time heat flux was found to be less impacted by the observed changes in ice thickness. A large increase in the net Arctic ocean-atmosphere heat output is also observed in the fall periods due to changes in the areal coverage of sea ice. The anomalously low sea ice coverage in 2007 led to a net ocean-atmosphere heat output approximately 3 times greater than was observed in previous years and suggests that sea ice losses are now playing a role in increasing surface air temperatures in the Arctic.

  5. Some sensitivities of a coupled ocean-atmosphere GCM

    International Nuclear Information System (INIS)

    Stockdale, T.; Latif, M.; Burgers, G.; Wolff, J.O.

    1994-01-01

    A coupled ocean-atmosphere GCM is being developed for use in seasonal forecasting. As part of the development work, a number of experiments have been made to explore some of the sensitivities of the coupled model system. The overall heat balance of the tropics is found to be very sensitive to convective cloud cover. Adjusting the cloud parameterization to produce stable behaviour of the coupled model also leads to better agreement between model radiative fluxes and satellite data. A further sensitivity is seen to changes in low-level marine stratus, which is under-represented in the initial model experiments. An increase in this cloud in the coupled model produces a small improvement in both the global mean state and the phase of the east Pacific annual cycle. The computational expense of investigating such small changes is emphasized. An indication of model sensitivity to surface albedo is also presented. The sensitivity of the coupled GCM to initial conditions is investigated. The model is very sensitive, with tiny perturbations able to determine El Nino or non-El Nino conditions just six months later. This large sensitivity may be related to the relatively weak amplitude of the model ENSO cycle. (orig.)

  6. Forecasting of rainfall using ocean-atmospheric indices with a fuzzy neural technique

    Science.gov (United States)

    Srivastava, Gaurav; Panda, Sudhindra N.; Mondal, Pratap; Liu, Junguo

    2010-12-01

    SummaryForecasting of rainfall is imperative for rainfed agriculture of arid and semi-arid regions of the world where agriculture consumes nearly 80% of the total water demand. Fuzzy-Ranking Algorithm (FRA) is used to identify the significant input variables for rainfall forecast. A case study is carried out to forecast monthly rainfall in India with several ocean-atmospheric predictor variables. Three different scenarios of ocean-atmospheric predictor variables are used as a set of possible input variables for rainfall forecasting model: (1) two climate indices, i.e. Southern Oscillation Index (SOI) and Pacific Decadal Oscillation Index (PDOI); (2) Sea Surface Temperature anomalies (SSTa) in the 5° × 5° grid points in Indian Ocean; and (3) both the climate indices and SSTa. To generate a set of possible input variables for these scenarios, we use climatic indices and the SSTa data with different lags between 1 and 12 months. Nonlinear relationship between identified inputs and rainfall is captured with an Artificial Neural Network (ANN) technique. A new approach based on fuzzy c-mean clustering is proposed for dividing data into representative subsets for training, testing, and validation. The results show that this proposed approach overcomes the difficulty in determining optimal numbers of clusters associated with the data division technique of self-organized map. The ANN model developed with both the climate indices and SSTa shows the best performance for the forecast of the monthly August rainfall in India. Similar approach can be applied to forecast rainfall of any period at selected climatic regions of the world where significant relationship exists between the rainfall and climate indices.

  7. GFDL CM2.1 Global Coupled Ocean-Atmosphere Model Water ...

    Indian Academy of Sciences (India)

    First page Back Continue Last page Overview Graphics. GFDL CM2.1 Global Coupled Ocean-Atmosphere Model Water Hosing Experiment with 1 Sv equivalent of Freshening Control Expt: 100 yrs After Hosing: 300 yrs.

  8. International Comprehensive Ocean Atmosphere Data Set (ICOADS) in Near-Real Time (NRT)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The International Comprehensive Ocean-Atmosphere Data Set (ICOADS) Near-Real-Time (NRT) product is an extension of the official ICOADS dataset with preliminary...

  9. International Comprehensive Ocean-Atmosphere Data Set (ICOADS) with Enhanced Trimming, Release 3

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset contains the latest official release of International Comprehensive Ocean-Atmosphere Data Set (ICOADS) with Enhanced Trimming, provided in a common...

  10. International Comprehensive Ocean-Atmosphere Data Set (ICOADS) Release 3.0 - Monthly Summary Groups (MSG)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset, the International Comprehensive Ocean-Atmosphere Data Set (ICOADS), is the most widely-used freely available collection of surface marine observations,...

  11. International Comprehensive Ocean Atmosphere Data Set (ICOADS) And NCEI Global Marine Observations

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — International Comprehensive Ocean Atmosphere Data Set (ICOADS) consists of digital data set DSI-1173, archived at the National Center for Environmental Information...

  12. Coupled ocean-atmosphere surface variability and its climate impacts in the tropical Atlantic region

    Science.gov (United States)

    Fontaine, B.; Janicot, Serge; Roucou, P.

    This study examines time evolution and statistical relationships involving the two leading ocean-atmosphere coupled modes of variability in the tropical Atlantic and some climate anomalies over the tropical 120°W-60°W region using selected historical files (75-y near global SSTs and precipitation over land), more recent observed data (30-y SST and pseudo wind stress in the tropical Atlantic) and reanalyses from the US National Centers for Environmental Prediction (NCEP/NCAR) reanalysis System on the period 1968-1997: surface air temperature, sea level pressure, moist static energy content at 850 hPa, precipitable water and precipitation. The first coupled mode detected through singular value decomposition of the SST and pseudo wind-stress data over the tropical Atlantic (30°N-20°S) expresses a modulation in the thermal transequatorial gradient of SST anomalies conducted by one month leading wind-stress anomalies mainly in the tropical north Atlantic during northern winter and fall. It features a slight dipole structure in the meridional plane. Its time variability is dominated by a quasi-decadal signal well observed in the last 20-30 ys and, when projected over longer-term SST data, in the 1920s and 1930s but with shorter periods. The second coupled mode is more confined to the south-equatorial tropical Atlantic in the northern summer and explains considerably less wind-stress/SST cross-covariance. Its time series features an interannual variability dominated by shorter frequencies with increased variance in the 1960s and 1970s before 1977. Correlations between these modes and the ENSO-like Nino3 index lead to decreasing amplitude of thermal anomalies in the tropical Atlantic during warm episodes in the Pacific. This could explain the nonstationarity of meridional anomaly gradients on seasonal and interannual time scales. Overall the relationships between the oceanic component of the coupled modes and the climate anomaly patterns denote thermodynamical

  13. Identification of Holocene millennial-scale forcing in the North Atlantic area: Ocean/atmosphere contribution

    Science.gov (United States)

    Debret, M.; Masson-Delmotte, V.; Christophe, C.; de Vernal, A.; Massei, N.; Eynaud, F.; Nicolle, M.; Frank, N.; Mary, Y.; Magny, M.

    2017-12-01

    Millennial (1500-year) cycles were evidenced decades ago from the advance and retreat of glaciers but many subsequent studies failed to demonstrate the unequivocal character of such oscillation from paleoclimate time series. Hence, the identification of a persistent 1500 year periodicity remains controversial both for the last glacial episode and the Holocene. Applying wavelet analysis to Holocene climate records, we have identified synchronous millennial-scale oscillations which permit to establish a North Atlantic millennial variability index (NAV-Index), maximum at 5330 ± 245, 3560 ± 190, 1810 ± 160 cal years BP and minimum at 4430 ± 250, 2640 ± 225 and 970 ± 200 years before present. This NAV-index was compared with the millennial variability of cosmogenic 10Be isotope, a proxy of solar activity. Differences between the two sets of records suggest that an internal mechanism (Ocean/atmosphere) must be at the origin of the North Atlantic millennial scale variability. Our data document an increased coherence and magnitude of the North Atlantic millennial variability since 6000 cal. years BP, with a frequency of 1780 ± 240 years. During the early Holocene, deglacial meltwater fluxes had strong regional impact and the coupling between subpolar gyre migration and Atlantic meridional oceanic circulation observed since afterward seems to be related to the end of the Laurentide and Inuitian ice sheet meltwater discharge. Hence, we may conclude that the evolution of this millennial oscillation in the future will depend upon the Greenland stability or melting.

  14. Medicanes in an ocean-atmosphere coupled regional climate model

    Science.gov (United States)

    Akhtar, N.; Brauch, J.; Dobler, A.; Béranger, K.; Ahrens, B.

    2014-08-01

    So-called medicanes (Mediterranean hurricanes) are meso-scale, marine, and warm-core Mediterranean cyclones that exhibit some similarities to tropical cyclones. The strong cyclonic winds associated with medicanes threaten the highly populated coastal areas around the Mediterranean basin. To reduce the risk of casualties and overall negative impacts, it is important to improve the understanding of medicanes with the use of numerical models. In this study, we employ an atmospheric limited-area model (COSMO-CLM) coupled with a one-dimensional ocean model (1-D NEMO-MED12) to simulate medicanes. The aim of this study is to assess the robustness of the coupled model in simulating these extreme events. For this purpose, 11 historical medicane events are simulated using the atmosphere-only model, COSMO-CLM, and coupled model, with different setups (horizontal atmospheric grid spacings of 0.44, 0.22, and 0.08°; with/without spectral nudging, and an ocean grid spacing of 1/12°). The results show that at high resolution, the coupled model is able to not only simulate most of medicane events but also improve the track length, core temperature, and wind speed of simulated medicanes compared to the atmosphere-only simulations. The results suggest that the coupled model is more proficient for systemic and detailed studies of historical medicane events, and that this model can be an effective tool for future projections.

  15. Key features of the IPSL ocean atmosphere model and its sensitivity to atmospheric resolution

    Energy Technology Data Exchange (ETDEWEB)

    Marti, Olivier; Braconnot, P.; Bellier, J.; Brockmann, P.; Caubel, A.; Noblet, N. de; Friedlingstein, P.; Idelkadi, A.; Kageyama, M. [Unite Mixte CEA-CNRS-UVSQ, IPSL/LSCE, Gif-sur-Yvette Cedex (France); Dufresne, J.L.; Bony, S.; Codron, F.; Fairhead, L.; Grandpeix, J.Y.; Hourdin, F.; Musat, I. [Unite Mixte CNRS-Ecole Polytechnique-ENS-UPCM, IPSL/LMD, Paris Cedex 05 (France); Benshila, R.; Guilyardi, E.; Levy, C.; Madec, G.; Mignot, J.; Talandier, C. [unite mixte CNRS-IRD-UPMC, IPLS/LOCEAN, Paris Cedex 05 (France); Cadule, P.; Denvil, S.; Foujols, M.A. [Institut Pierre Simon Laplace des Sciences de l' Environnement (IPSL), Paris Cedex 05 (France); Fichefet, T.; Goosse, H. [Universite Catholique de Louvain, Institut d' Astronomie et de Geophysique Georges Lemaitre, Louvain-la-Neuve (Belgium); Krinner, G. [Unite mixte CNRS-UJF Grenoble, LGGE, BP96, Saint-Martin-d' Heres (France); Swingedouw, D. [CNRS/CERFACS, Toulouse (France)

    2010-01-15

    This paper presents the major characteristics of the Institut Pierre Simon Laplace (IPSL) coupled ocean-atmosphere general circulation model. The model components and the coupling methodology are described, as well as the main characteristics of the climatology and interannual variability. The model results of the standard version used for IPCC climate projections, and for intercomparison projects like the Paleoclimate Modeling Intercomparison Project (PMIP 2) are compared to those with a higher resolution in the atmosphere. A focus on the North Atlantic and on the tropics is used to address the impact of the atmosphere resolution on processes and feedbacks. In the North Atlantic, the resolution change leads to an improved representation of the storm-tracks and the North Atlantic oscillation. The better representation of the wind structure increases the northward salt transports, the deep-water formation and the Atlantic meridional overturning circulation. In the tropics, the ocean-atmosphere dynamical coupling, or Bjerknes feedback, improves with the resolution. The amplitude of ENSO (El Nino-Southern oscillation) consequently increases, as the damping processes are left unchanged. (orig.)

  16. Impact of resolving the diurnal cycle in an ocean-atmosphere GCM. Pt. 2. A diurnally coupled CGCM

    Energy Technology Data Exchange (ETDEWEB)

    Bernie, D.J. [Met Office Hadley Centre, Exeter (United Kingdom); University of Reading, National Centre for Atmospheric Science-Climate, Department of Meteorology, Reading (United Kingdom); Numeriques, IPSL, Laboratoire d' Oceanographie et du Climat, Experimentation et Approches, Paris (France); Guilyardi, E. [University of Reading, National Centre for Atmospheric Science-Climate, Department of Meteorology, Reading (United Kingdom); Numeriques, IPSL, Laboratoire d' Oceanographie et du Climat, Experimentation et Approches, Paris (France); Madec, G. [Numeriques, IPSL, Laboratoire d' Oceanographie et du Climat, Experimentation et Approches, Paris (France); Slingo, J.M.; Woolnough, S.J.; Cole, J. [University of Reading, National Centre for Atmospheric Science-Climate, Department of Meteorology, Reading (United Kingdom)

    2008-12-15

    Coupled ocean atmosphere general circulation models (GCM) are typically coupled once every 24 h, excluding the diurnal cycle from the upper ocean. Previous studies attempting to examine the role of the diurnal cycle of the upper ocean and particularly of diurnal SST variability have used models unable to resolve the processes of interest. In part 1 of this study a high vertical resolution ocean GCM configuration with modified physics was developed that could resolve the diurnal cycle in the upper ocean. In this study it is coupled every 3 h to atmospheric GCM to examine the sensitivity of the mean climate simulation and aspects of its variability to the inclusion of diurnal ocean-atmosphere coupling. The inclusion of the diurnal cycle leads to a tropics wide increase in mean sea surface temperature (SST), with the strongest signal being across the equatorial Pacific where the warming increases from 0.2 C in the central and western Pacific to over 0.3 C in the eastern equatorial Pacific. Much of this warming is shown to be a direct consequence of the rectification of daily mean SST by the diurnal variability of SST. The warming of the equatorial Pacific leads to a redistribution of precipitation from the Inter tropical convergence zone (ITCZ) toward the equator. In the western Pacific there is an increase in precipitation between Papa new guinea and 170 E of up to 1.2 mm/day, improving the simulation compared to climatology. Pacific sub tropical cells are increased in strength by about 10%, in line with results of part 1 of this study, due to the modification of the exchange of momentum between the equatorially divergent Ekman currents and the geostropic convergence at depth, effectively increasing the dynamical response of the tropical Pacific to zonal wind stresses. During the spring relaxation of the Pacific trade winds, a large diurnal cycle of SST increases the seasonal warming of the equatorial Pacific. When the trade winds then re-intensify, the increase in

  17. Effect of Modulation of ENSO by Decadal and Multidecadal Ocean-Atmospheric Oscillations on Continental US Streamflows

    Science.gov (United States)

    Singh, S.; Abebe, A.; Srivastava, P.; Chaubey, I.

    2017-12-01

    Evaluation of the influences of individual and coupled oceanic-atmospheric oscillations on streamflow at a regional scale in the United States is the focus of this study. The main climatic oscillations considered in this study are: El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO), and North Atlantic Oscillation (NAO). Unimpacted or minimally impacted by water management streamflow data from the Model Parameter Estimation Experiment (MOPEX) were used in this study. Two robust and novel non-parametric tests, namely, the rank based partial least square (PLS) and the Joint Rank Fit (JRFit) procedures were used to identify the individual and coupled effect of oscillations on streamflow across continental U.S. (CONUS), respectively. Moreover, the interactive effects of ENSO with decadal and multidecadal cycles were tested and quantified using the JRFit interaction test. The analysis of ENSO indicated higher streamflows during La Niña phase compared to the El Niño phase in Northwest, Northeast and the lower part of Ohio Valley while the opposite occurs for rest of the climatic regions in US. Two distinct climate regions (Northwest and Southeast) were identified from the PDO analysis where PDO negative phase results in increased streamflow than PDO positive phase. Consistent negative and positive correlated regions around the CONUS were identified for AMO and NAO, respectively. The interaction test of ENSO with decadal and multidecadal oscillations showed that El Niño is modulated by the negative phase of PDO and NAO, and the positive phase of AMO, respectively, in the Upper Midwest. However, La Niña is modulated by the positive phase of AMO and PDO in Ohio Valley and Northeast while in Southeast and the South it is modulated by AMO negative phase. Results of this study will assist water managers to understand the streamflow change patterns across the CONUS at decadal and multi-decadal time scales. The

  18. Mechanism of climate change over South America during the LGM in coupled Ocean- Atmosphere model simulations

    Science.gov (United States)

    Khodri, M.

    2006-12-01

    On a regional perspective the database of proxy information for South America during the Last Glacial Maximum (LGM) shows large and regionally extensive changes of the mean climate and vegetation types over the Amazon basin. In some instances these changes were associated with decrease in the mean precipitation amount (and most probably in moist deep convection) over the Amazonian and South East Brazil monsoon regions and wetter mean conditions in present day drought-prone regions such as Northeast of Brazil (Nordeste). These changes have been interpreted as local responses to shift in the mean position and intensity of the Atlantic ITCZ due to glacial extratropical forcings or to changes in the South American Monsoons. However there are still two issues is the path to further understand the mechanism of climate change over South America during the LGM. The first is incomplete knowledge in both the modeling and observational communities of how the moist deep convection over the Amazonian region respond to glacial boundary condition and how this changes might interact with the meridional shift of rainfall over Nordeste and Atlantic Ocean. The second is our understanding of how ocean-atmosphere changes that do occur in the tropical Pacific region influence the climate of the remainder of the planet and on a regional way over South America. Using PMIP-2 coupled Ocean-Atmosphere simulations for LGM and comparison to paleodata we show that hydrological cycle changes over the Amazon basin might be independent of their Atlantic Ocean counterpart, while teleconnections with Pacific Ocean might have played a significant role in the observed changes over tropical South America.

  19. Coupled Ocean/Atmospheric Mesoscale Prediction System (COAMPS), Version 5.0 (User’s Guide)

    Science.gov (United States)

    2010-03-30

    provides tools for common modeling functions, as well as regridding, data decomposition, and communication on parallel computers. NRL/MR/7320--10...specified gncomDir. If running COAMPS at the DSRC (e.g. BABBAGE, DAVINCI , or EINSTEIN), the global NCOM files will be copied to /scr/[user]/COAMPS/data...the site (DSRC or local) and the platform (BABBAGE. DAVINCI , EINSTEIN, or local machine) on which COAMPS is being run. site=navy_dsrc (for DSRC

  20. Development and Testing of a Coupled Ocean-atmosphere Mesoscale Ensemble Prediction System

    Science.gov (United States)

    2011-06-28

    member 0; see text for a detailed description of the physics parameters) Member abl mixlen Flux w-kf tinc-lcl cld -rad precip Graupel Auto-conv Rain-int...increment has an impact on the convective initiation. 7. The cloud updraft radius used in the K–F parameteri- zation: The radius cld -rad (m) varies

  1. Autumn atmospheric response to the 2007 low Arctic sea ice extent in coupled ocean-atmosphere hindcasts

    Energy Technology Data Exchange (ETDEWEB)

    Orsolini, Yvan J. [Norwegian Institute for Air Research (NILU), PO BOX 100, Kjeller (Norway); Senan, Retish; Benestad, Rasmus E.; Melsom, Arne [Norwegian Meteorological Institute (met. no), Oslo (Norway)

    2012-06-15

    The autumn and early winter atmospheric response to the record-low Arctic sea ice extent at the end of summer 2007 is examined in ensemble hindcasts with prescribed sea ice extent, made with the European Centre for Medium-Range Weather Forecasts state-of-the-art coupled ocean-atmosphere seasonal forecast model. Robust, warm anomalies over the Pacific and Siberian sectors of the Arctic, as high as 10 C at the surface, are found in October and November. A regime change occurs by December, characterized by weaker temperatures anomalies extending through the troposphere. Geopotential anomalies extend from the surface up to the stratosphere, associated to deeper Aleutian and Icelandic Lows. While the upper-level jet is weakened and shifted southward over the continents, it is intensified over both oceanic sectors, especially over the Pacific Ocean. On the American and Eurasian continents, intensified surface Highs are associated with anomalous advection of cold (warm) polar air on their eastern (western) sides, bringing cooler temperatures along the Pacific coast of Asia and Northeastern North America. Transient eddy activity is reduced over Eurasia, intensified over the entrance and exit regions of the Pacific and Atlantic storm tracks, in broad qualitative agreement with the upper-level wind anomalies. Potential predictability calculations indicate a strong influence of sea ice upon surface temperatures over the Arctic in autumn, but also along the Pacific coast of Asia in December. When the observed sea ice extent from 2007 is prescribed throughout the autumn, a higher correlation of surface temperatures with meteorological re-analyses is found at high latitudes from October until mid-November. This further emphasises the relevance of sea ice for seasonal forecasting in the Arctic region, in the autumn. (orig.)

  2. The stability of the thermohaline circulation in a coupled ocean-atmosphere general circulation model

    Energy Technology Data Exchange (ETDEWEB)

    Schiller, A. [Max-Planck-Institut fuer Meteorologie, Hamburg (Germany); Mikolajewicz, U. [Max-Planck-Institut fuer Meteorologie, Hamburg (Germany); Voss, R. [Deutsches Klimarechenzentrum (DKRZ), Hamburg (Germany)

    1996-02-01

    The stability of the Atlantic thermohaline circulation against meltwater input is investigated in a coupled ocean-atmosphere general circulation model. The meltwater input to the Labrador Sea is increased linearly for 250 years to a maximum input of 0.625 Sv and then reduced again to 0 (both instantaneously and slowly decreasing over 250 years). The resulting freshening forces a shutdown of the formation of North Atlantic deepwater and a subsequent reversal of the thermohaline circulation of the Atlantic, filling the deep Atlantic with Antarctic bottom water. The change in the overturning pattern causes a drastic reduction of the Atlantic northward heat transport, resulting in a strong cooling with maximum amplitude over the northern North Atlantic and a southward shift of the sea-ice margin in the Atlantic. Due to the increased meridional temperature gradient, the Atlantic intertropical convergence zone is displaced southward and the westerlies in the northern hemisphere gain strength. We identify four main feedbacks affecting the stability of the thermohaline circulation: the change in the overturning circulation of the Atlantic leads to longer residence times of the surface waters in high northern latitudes, which allows them to accumulate more precipitation and runoff from the continents, which results in an increased stability in the North Atlantic.

  3. Scaling laws for perturbations in the ocean-atmosphere system following large CO2 emissions

    Science.gov (United States)

    Towles, N.; Olson, P.; Gnanadesikan, A.

    2015-07-01

    Scaling relationships are found for perturbations to atmosphere and ocean variables from large transient CO2 emissions. Using the Long-term Ocean-atmosphere-Sediment CArbon cycle Reservoir (LOSCAR) model (Zeebe et al., 2009; Zeebe, 2012b), we calculate perturbations to atmosphere temperature, total carbon, ocean temperature, total ocean carbon, pH, alkalinity, marine-sediment carbon, and carbon-13 isotope anomalies in the ocean and atmosphere resulting from idealized CO2 emission events. The peak perturbations in the atmosphere and ocean variables are then fit to power law functions of the form of γ DαEβ, where D is the event duration, E is its total carbon emission, and γ is a coefficient. Good power law fits are obtained for most system variables for E up to 50 000 PgC and D up to 100 kyr. Although all of the peak perturbations increase with emission rate E/D, we find no evidence of emission-rate-only scaling, α + β = 0. Instead, our scaling yields α + β ≃ 1 for total ocean and atmosphere carbon and 0 < α + β < 1 for most of the other system variables.

  4. Decadal Variations in Eastern Canada's Taiga Wood Biomass Production Forced by Ocean-Atmosphere Interactions.

    Science.gov (United States)

    Boucher, Etienne; Nicault, Antoine; Arseneault, Dominique; Bégin, Yves; Karami, Mehdi Pasha

    2017-05-26

    Across Eastern Canada (EC), taiga forests represent an important carbon reservoir, but the extent to which climate variability affects this ecosystem over decades remains uncertain. Here, we analyze an extensive network of black spruce (Picea mariana Mill.) ring width and wood density measurements and provide new evidence that wood biomass production is influenced by large-scale, internal ocean-atmosphere processes. We show that while black spruce wood biomass production is primarily governed by growing season temperatures, the Atlantic ocean conveys heat from the subtropics and influences the decadal persistence in taiga forests productivity. Indeed, we argue that 20-30 years periodicities in Sea Surface Temperatures (SSTs) as part of the the Atlantic Multi-decadal Oscillation (AMO) directly influence heat transfers to adjacent lands. Winter atmospheric conditions associated with the North Atlantic Oscillation (NAO) might also impact EC's taiga forests, albeit indirectly, through its effect on SSTs and sea ice conditions in surrounding seas. Our work emphasizes that taiga forests would benefit from the combined effects of a warmer atmosphere and stronger ocean-to-land heat transfers, whereas a weakening of these transfers could cancel out, for decades or longer, the positive effects of climate change on Eastern Canada's largest ecosystem.

  5. Secular trends and climate drift in coupled ocean-atmosphere general circulation models

    Science.gov (United States)

    Covey, Curt; Gleckler, Peter J.; Phillips, Thomas J.; Bader, David C.

    2006-02-01

    Coupled ocean-atmosphere general circulation models (coupled GCMs) with interactive sea ice are the primary tool for investigating possible future global warming and numerous other issues in climate science. A long-standing problem with such models is that when different components of the physical climate system are linked together, the simulated climate can drift away from observation unless constrained by ad hoc adjustments to interface fluxes. However, 11 modern coupled GCMs, including three that do not employ flux adjustments, behave much better in this respect than the older generation of models. Surface temperature trends in control run simulations (with external climate forcing such as solar brightness and atmospheric carbon dioxide held constant) are small compared with observed trends, which include 20th century climate change due to both anthropogenic and natural factors. Sea ice changes in the models are dominated by interannual variations. Deep ocean temperature and salinity trends are small enough for model control runs to extend over 1000 simulated years or more, but trends in some regions, most notably the Arctic, differ substantially among the models and may be problematic. Methods used to initialize coupled GCMs can mitigate climate drift but cannot eliminate it. Lengthy "spin-ups" of models, made possible by increasing computer power, are one reason for the improvements this paper documents.

  6. Ocean-atmosphere forcing of centennial hydroclimatic variability in the Pacific Northwest

    Science.gov (United States)

    Steinman, Byron A.; Abbott, Mark B.; Mann, Michael E.; Ortiz, Joseph D.; Feng, Song; Pompeani, David P.; Stansell, Nathan D.; Anderson, Lesleigh; Finney, Bruce P.; Bird, Broxton W.

    2014-01-01

    Reconstructing centennial timescale hydroclimate variability during the late Holocene is critically important for understanding large-scale patterns of drought and their relationship with climate dynamics. We present sediment oxygen isotope records spanning the last two millennia from 10 lakes, as well as climate model simulations, indicating that the Little Ice Age was dry relative to the Medieval Climate Anomaly in much of the Pacific Northwest of North America. This pattern is consistent with observed associations between the El Niño Southern Oscillation (ENSO), the Northern Annular Mode and drought as well as with proxy-based reconstructions of Pacific ocean-atmosphere variations over the past 1000 years. The large amplitude of centennial variability indicated by the lake data suggests that regional hydroclimate is characterized by longer-term shifts in ENSO-like dynamics, and that an improved understanding of the centennial timescale relationship between external forcing and drought conditions is necessary for projecting future hydroclimatic conditions in western North America.

  7. Monte Carlo climate change forecasts with a global coupled ocean-atmosphere model

    International Nuclear Information System (INIS)

    Cubasch, U.; Santer, B.D.; Hegerl, G.; Hoeck, H.; Maier-Reimer, E.; Mikolajwicz, U.; Stoessel, A.; Voss, R.

    1992-01-01

    The Monte Carlo approach, which has increasingly been used during the last decade in the field of extended range weather forecasting, has been applied for climate change experiments. Four integrations with a global coupled ocean-atmosphere model have been started from different initial conditions, but with the same greenhouse gas forcing according to the IPCC scenario A. All experiments have been run for a period of 50 years. The results indicate that the time evolution of the global mean warming depends strongly on the initial state of the climate system. It can vary between 6 and 31 years. The Monte Carlo approach delivers information about both the mean response and the statistical significance of the response. While the individual members of the ensemble show a considerable variation in the climate change pattern of temperature after 50 years, the ensemble mean climate change pattern closely resembles the pattern obtained in a 100 year integration and is, at least over most of the land areas, statistically significant. The ensemble averaged sea-level change due to thermal expansion is significant in the global mean and locally over wide regions of the Pacific. The hydrological cycle is also significantly enhanced in the global mean, but locally the changes in precipitation and soil moisture are masked by the variability of the experiments. (orig.)

  8. Bi-decadal variability excited in the coupled ocean-atmosphere system by strong tropical volcanic eruptions

    Energy Technology Data Exchange (ETDEWEB)

    Zanchettin, D.; Lorenz, S.; Lohmann, K.; Jungclaus, J.H. [Max Planck Institute for Meteorology, Ocean in the Earth System Department, Hamburg (Germany); Timmreck, C. [Max Planck Institute for Meteorology, Atmosphere in the Earth System Department, Hamburg (Germany); Graf, H.-F. [University of Cambridge, Centre for Atmospheric Science, Cambridge (United Kingdom); Rubino, A. [Ca' Foscari University, Department of Environmental Sciences, Venice (Italy); Krueger, K. [Leibniz-Institute of Marine Sciences, IFM-GEOMAR, Kiel (Germany)

    2012-07-15

    Decadal and bi-decadal climate responses to tropical strong volcanic eruptions (SVEs) are inspected in an ensemble simulation covering the last millennium based on the Max Planck Institute - Earth system model. An unprecedentedly large collection of pre-industrial SVEs (up to 45) producing a peak annual-average top-of-atmosphere radiative perturbation larger than -1.5 Wm{sup -2} is investigated by composite analysis. Post-eruption oceanic and atmospheric anomalies coherently describe a fluctuation in the coupled ocean-atmosphere system with an average length of 20-25 years. The study provides a new physically consistent theoretical framework to interpret decadal Northern Hemisphere (NH) regional winter climates variability during the last millennium. The fluctuation particularly involves interactions between the Atlantic meridional overturning circulation and the North Atlantic gyre circulation closely linked to the state of the winter North Atlantic Oscillation. It is characterized by major distinctive details. Among them, the most prominent are: (a) a strong signal amplification in the Arctic region which allows for a sustained strengthened teleconnection between the North Pacific and the North Atlantic during the first post-eruption decade and which entails important implications from oceanic heat transport and from post-eruption sea ice dynamics, and (b) an anomalous surface winter warming emerging over the Scandinavian/Western Russian region around 10-12 years after a major eruption. The simulated long-term climate response to SVEs depends, to some extent, on background conditions. Consequently, ensemble simulations spanning different phases of background multidecadal and longer climate variability are necessary to constrain the range of possible post-eruption decadal evolution of NH regional winter climates. (orig.)

  9. Impact of the configuration of stretching and ocean-atmosphere coupling on tropical cyclone activity in the variable-resolution GCM ARPEGE

    Energy Technology Data Exchange (ETDEWEB)

    Daloz, Anne Sophie; Chauvin, Fabrice [CNRM-GAME, Groupe de Modelisation Grande Echelle et Climat, Toulouse Cedex 1 (France); Roux, Frank [Universite de Toulouse, Laboratoire d' Aerologie, Centre National de la Recherche Scientifique, Toulouse (France)

    2012-11-15

    This study starts by investigating the impact of the configuration of the variable-resolution atmospheric grid on tropical cyclone (TC) activity. The French atmospheric general circulation model ARPEGE, the grid of which is rotated and stretched over the North Atlantic basin, was used with prescribed sea surface temperatures. The study clearly shows that changing the position of the stretching pole strongly modifies the representation of TC activity over the North Atlantic basin. A pole in the centre of the North Atlantic basin provides the best representation of the TC activity for this region. In a second part, the variable-resolution climate model ARPEGE is coupled with the European oceanic global climate model NEMO in order to study the impact of ocean-atmosphere coupling on TC activity over the North Atlantic basin. Two pre-industrial runs, a coupled simulation and a simulation forced by the sea surface temperatures from the coupled one, are compared. The results show that the coupled simulation is more active in the Caribbean Sea and the Gulf of Mexico while the forced simulation is more active over eastern Florida and the eastern Atlantic. The difference in the distribution of TC activity is certainly linked with the location of TC genesis. In the forced simulation, tropical cyclogenesis is closer to the west African coast than in the coupled simulation. Moreover, the difference in TC activity over the eastern Atlantic seems to be related to two different mechanisms: the difference in African easterly wave activity over the west of Africa and the cooling produced, in the coupled simulation, by African easterly waves over the eastern Atlantic. Finally, the last part studies the impact of changing the frequency of ocean-atmosphere coupling on Atlantic TC activity. Increasing the frequency of coupling decreases the density of TC activity over the North Atlantic basin. However, it does not modify the spatial distribution of the TC activity. TC rainfalls are

  10. Longitudinal Biases in the Seychelles Dome Simulated by 34 Ocean-Atmosphere Coupled General Circulation Models

    Science.gov (United States)

    Nagura, M.; Sasaki, W.; Tozuka, T.; Luo, J.; Behera, S. K.; Yamagata, T.

    2012-12-01

    The upwelling dome of the southern tropical Indian Ocean is examined by using simulated results from 34 ocean-atmosphere coupled general circulation models (CGCMs) including those from the phase five of the Coupled Model Intercomparison Project (CMIP5). Among the current set of the 34 CGCMs, 12 models erroneously produce the upwelling dome in the eastern half of the basin while the observed Seychelles Dome is located in the southwestern tropical Indian Ocean (Figure 1). The annual mean Ekman pumping velocity is almost zero in the southern off-equatorial region in these models. This is in contrast with the observations that show Ekman upwelling as the cause of the Seychelles Dome. In the models that produce the dome in the eastern basin, the easterly biases are prominent along the equator in boreal summer and fall that cause shallow thermocline biases along the Java and Sumatra coasts via Kelvin wave dynamics and result in a spurious upwelling dome there. In addition, these models tend to overestimate (underestimate) the magnitude of annual (semiannual) cycle of thermocline depth variability in the dome region, which is another consequence of the easterly wind biases in boreal summer-fall. Compared to the CMIP3 models (Yokoi et al. 2009), the CMIP5 models are even worse in simulating the dome longitudes and magnitudes of annual and semiannual cycles of thermocline depth variability in the dome region. Considering the increasing need to understand regional impacts of climate modes, these results may give serious caveats to interpretation of model results and help in further model developments.; Figure 1: The longitudes of the shallowest annual-mean D20 in 5°S-12°S. The open and filled circles are for the observations and the CGCMs, respectively.

  11. Indian Ocean and Indian summer monsoon: relationships without ENSO in ocean-atmosphere coupled simulations

    Science.gov (United States)

    Crétat, Julien; Terray, Pascal; Masson, Sébastien; Sooraj, K. P.; Roxy, Mathew Koll

    2017-08-01

    The relationship between the Indian Ocean and the Indian summer monsoon (ISM) and their respective influence over the Indo-Western North Pacific (WNP) region are examined in the absence of El Niño Southern Oscillation (ENSO) in two partially decoupled global experiments. ENSO is removed by nudging the tropical Pacific simulated sea surface temperature (SST) toward SST climatology from either observations or a fully coupled control run. The control reasonably captures the observed relationships between ENSO, ISM and the Indian Ocean Dipole (IOD). Despite weaker amplitude, IODs do exist in the absence of ENSO and are triggered by a boreal spring ocean-atmosphere coupled mode over the South-East Indian Ocean similar to that found in the presence of ENSO. These pure IODs significantly affect the tropical Indian Ocean throughout boreal summer, inducing a significant modulation of both the local Walker and Hadley cells. This meridional circulation is masked in the presence of ENSO. However, these pure IODs do not significantly influence the Indian subcontinent rainfall despite overestimated SST variability in the eastern equatorial Indian Ocean compared to observations. On the other hand, they promote a late summer cross-equatorial quadrupole rainfall pattern linking the tropical Indian Ocean with the WNP, inducing important zonal shifts of the Walker circulation despite the absence of ENSO. Surprisingly, the interannual ISM rainfall variability is barely modified and the Indian Ocean does not force the monsoon circulation when ENSO is removed. On the contrary, the monsoon circulation significantly forces the Arabian Sea and Bay of Bengal SSTs, while its connection with the western tropical Indian Ocean is clearly driven by ENSO in our numerical framework. Convection and diabatic heating associated with above-normal ISM induce a strong response over the WNP, even in the absence of ENSO, favoring moisture convergence over India.

  12. Nonlinear dynamics and predictability in the atmospheric sciences

    Science.gov (United States)

    Ghil, M.; Kimoto, M.; Neelin, J. D.

    1991-01-01

    Systematic applications of nonlinear dynamics to studies of the atmosphere and climate are reviewed for the period 1987-1990. Problems discussed include paleoclimatic applications, low-frequency atmospheric variability, and interannual variability of the ocean-atmosphere system. Emphasis is placed on applications of the successive bifurcation approach and the ergodic theory of dynamical systems to understanding and prediction of intraseasonal, interannual, and Quaternary climate changes.

  13. International Comprehensive Ocean-Atmosphere Data Set (ICOADS) Release 3 Final, Individual Reports in the International Maritime Meteorological Archive Format version 1 (IMMA1)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — This dataset, the International Comprehensive Ocean-Atmosphere Data Set (ICOADS), is the most widely-used freely available collection of surface marine observations,...

  14. Impact of a realistic river routing in coupled ocean-atmosphere simulations of the Last Glacial Maximum climate

    Energy Technology Data Exchange (ETDEWEB)

    Alkama, Ramdane [IPSL, Laboratoire des Sciences du Climat et de l' Environnement, Gif-sur-Yvette Cedex (France); Universite Pierre et Marie Curie, Structure et fonctionnement des systemes hydriques continentaux (Sisyphe), Paris (France); Kageyama, M.; Ramstein, G.; Marti, O.; Swingedouw, D. [IPSL, Laboratoire des Sciences du Climat et de l' Environnement, Gif-sur-Yvette Cedex (France); Ribstein, P. [Universite Pierre et Marie Curie, Structure et fonctionnement des systemes hydriques continentaux (Sisyphe), Paris (France)

    2008-06-15

    The presence of large ice sheets over North America and North Europe at the Last Glacial Maximum (LGM) strongly impacted Northern hemisphere river pathways. Despite the fact that such changes may significantly alter the freshwater input to the ocean, modified surface hydrology has never been accounted for in coupled ocean-atmosphere general circulation model simulations of the LGM climate. To reconstruct the LGM river routing, we use the ICE-5G LGM topography. Because of the uncertainties in the extent of the Fennoscandian ice sheet in the Eastern part of the Kara Sea, we consider two more realistic river routing scenarios. The first scenario is characterised by the presence of an ice dammed lake south of the Fennoscandian ice sheet, and corresponds to the ICE-5G topography. This lake is fed by the Ob and Yenisei rivers. In the second scenario, both these rivers flow directly into the Arctic Ocean, which is more consistent with the latest QUEEN ice sheet margin reconstructions. We study the impact of these changes on the LGM climate as simulated by the IPSL{sub C}M4 model and focus on the overturning thermohaline circulation. A comparison with a classical LGM simulation performed using the same model and modern river basins as designed in the PMIP2 exercise leads to the following conclusions: (1) The discharge into the North Atlantic Ocean is increased by 2,000 m{sup 3}/s between 38 and 54 N in both simulations that contain LGM river routing, compared to the classical LGM experiment. (2) The ice dammed lake is shown to have a weak impact, relative to the classical simulation, both in terms of climate and ocean circulation. (3) In contrast, the North Atlantic deep convection and meridional overturning are weaker than during the classical LGM run if the Ob and Yenisei rivers flow directly into the Arctic Ocean. The total discharge into the Arctic Ocean is increased by 31,000 m{sup 3}/s, relative to the classical LGM simulation. Consequentially, northward ocean heat

  15. Climate Prediction Center - Monitoring & Data Index

    Science.gov (United States)

    Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Site Map News Oceanic & Atmospheric Monitoring and Data Monitoring Weather & Climate in Realtime Climate Diagnostics Bulletin Preliminary Climate Diagnostics Bulletin Figures Monthly Atmospheric & Sea Surface

  16. Modes of North Atlantic Decadal Variability in the ECHAM1/LSG Coupled Ocean-Atmosphere General Circulation Model.

    Science.gov (United States)

    Zorita, Eduardo; Frankignoul, Claude

    1997-02-01

    The climate variability in the North Atlantic sector is investigated in a 325-yr integration of the ECHAM1/ LSG coupled ocean-atmosphere general circulation model. At the interannual timescale, the coupled model behaves realistically and sea surface temperature (SST) anomalies arise as a response of the oceanic surface layer to the stochastic forcing by the atmosphere, with the heat exchanges both generating and damping the SST anomalies. In the ocean interior, the temperature spectra are red up to a period of about 20 years, and substantial decadal fluctuations are found in the upper kilometer or so of the water column. Using extended empirical orthogonal function analysis, two distinct quasi-oscillatory modes of ocean-atmosphere variability are identified, with dominant periods of about 20 and 10 years, respectively. The oceanic changes in both modes reflect the direct forcing by the atmosphere through anomalous air-sea fluxes and Ekman pumping, which after some delay affects the intensity of the subtropical and subpolar gyres. The SST is also strongly modulated by the gyre currents. In the thermocline, the temperature and salinity fluctuations are in phase, as if caused by thermocline displacements, and they have no apparent connection with the thermohaline circulation. The 20-yr mode is the most energetic one; it is easily seen in the thermocline and can be found in SST data, but it is not detected in the atmosphere alone. As there is no evidence of positive ocean-atmosphere feedback, the 20-yr mode primarily reflects the passive response of the ocean to atmospheric fluctuations, which may be in part associated with climate anomalies appearing a few years earlier in the North Pacific. The 10-yr mode is more surface trapped in the ocean. Although the mode is most easily seen in the temperature variations of the upper few hundred meters of the ocean, it is also detected in the atmosphere alone and thus appears to be a coupled ocean-atmosphere mode. In both modes

  17. Ocean-atmosphere interaction during Thane cyclone: A numerical study using WRF

    Digital Repository Service at National Institute of Oceanography (India)

    VinodKumar, K.; Soumya, M.; Tkalich, P.; Vethamony, P.

    Cyclone `Thane` developed over the southeast Bay of Bengal (BoB) at 88.5 degree E, 8.5degree N during 25-31 December 2011.Simulations have been carried out using Weather Research and Forecasting (WRF) model to generate fine resolution winds...

  18. LOSCAR: Long-term Ocean-atmosphere-Sediment CArbon cycle Reservoir Model v2.0.4

    Directory of Open Access Journals (Sweden)

    R. E. Zeebe

    2012-01-01

    Full Text Available The LOSCAR model is designed to efficiently compute the partitioning of carbon between ocean, atmosphere, and sediments on time scales ranging from centuries to millions of years. While a variety of computationally inexpensive carbon cycle models are already available, many are missing a critical sediment component, which is indispensable for long-term integrations. One of LOSCAR's strengths is the coupling of ocean-atmosphere routines to a computationally efficient sediment module. This allows, for instance, adequate computation of CaCO3 dissolution, calcite compensation, and long-term carbon cycle fluxes, including weathering of carbonate and silicate rocks. The ocean component includes various biogeochemical tracers such as total carbon, alkalinity, phosphate, oxygen, and stable carbon isotopes. LOSCAR's configuration of ocean geometry is flexible and allows for easy switching between modern and paleo-versions. We have previously published applications of the model tackling future projections of ocean chemistry and weathering, pCO2 sensitivity to carbon cycle perturbations throughout the Cenozoic, and carbon/calcium cycling during the Paleocene-Eocene Thermal Maximum. The focus of the present contribution is the detailed description of the model including numerical architecture, processes and parameterizations, tuning, and examples of input and output. Typical CPU integration times of LOSCAR are of order seconds for several thousand model years on current standard desktop machines. The LOSCAR source code in C can be obtained from the author by sending a request to loscar.model@gmail.com.

  19. Ocean-atmosphere dynamics during Hurricane Ida and Nor'Ida: An application of the coupled ocean-atmosphere-wave-sediment transport (COAWST) modeling system

    Science.gov (United States)

    Olabarrieta, Maitane; Warner, John C.; Armstrong, Brandy N.; Zambon, Joseph B.; He, Ruoying

    2012-01-01

    -based parameterization (OOST) provided the best results for wind and wave growth prediction. However, the best agreement between the measured (CODAR) and computed surface currents and storm surge values was obtained with the wave steepness-based roughness parameterization (TY2001), although the differences obtained with respect to DGHQ were not significant. The influence of sea surface temperature (SST) fields on the atmospheric boundary layer dynamics was examined; in particular, we evaluated how the SST affects wind wave generation, surface currents and storm surges. The integrated hydrograph and integrated wave height, parameters that are highly correlated with the storm damage potential, were found to be highly sensitive to the ocean surface roughness parameterization.

  20. Role of upper-most crustal composition in the evolution of the Precambrian ocean-atmosphere system

    Science.gov (United States)

    Large, R. R.; Mukherjee, I.; Zhukova, I.; Corkrey, R.; Stepanov, A.; Danyushevsky, L. V.

    2018-04-01

    Recent research has emphasized the potential relationships between supercontinent cycles, mountain building, nutrient flux, ocean-atmosphere chemistry and the origin of life. The composition of the Upper-Most Continental Crust (UMCC) also figures prominently in these relationships, and yet little detailed data on each component of this complex relationship has been available for assessment. Here we provide a new set of data on the trace element concentrations, including the Rare Earth Elements (REE), in the matrix of 52 marine black shale formations spread globally through the Archean and Proterozoic. The data support previous studies on the temporal geochemistry of shales, but with some important differences. Results indicate a change in provenance of the black shales (upper-most crustal composition), from more mafic in the Archean prior to 2700 Ma, to more felsic from 2700 to 2200 Ma, followed by a return to mafic compositions from 2200 to 1850 Ma. Around 1850 to 1800 Ma there is a rapid change to uniform felsic compositions, which remained for a billion years to 800 Ma. The shale matrix geochemistry supports the assertion that the average upper-most continental source rocks for the shales changed from a mix of felsic, mafic and ultramafic prior to 2700 Ma to more felsic after 1850 Ma, with an extended transition period between. The return to more mafic UMCC from 2200 to 1850 Ma is supported by the frequency of Large Igneous Provinces (LIPs) and banded iron formations, which suggest a peak in major mantle-connected plume events and associated Fe-rich hydrothermal activity over this period. Support for the change to felsic UMCC around 1850 Ma is provided by previous geological data which shows that felsic magmas, including, A-type granites and K-Th-U-rich granites intruded vast areas of the continental crust, peaking around 1850 Ma and declining to 1000 Ma. The implications of this change in UMCC are far reaching and may go some way to explain the distinct

  1. Southern hemisphere climate variability as represented by an ocean-atmosphere coupled model

    CSIR Research Space (South Africa)

    Beraki, A

    2012-09-01

    Full Text Available in the atmospheric circulation. The ability of predicting these modes of climate variability on longer timescales is vital. Potential predictability is usually measured as a signal-to-noise contrast between the slowly evolving and chaotic components of the climate...

  2. Changing characteristics of streamflow in the Midwest and its relation to oceanic-atmospheric oscillations

    Science.gov (United States)

    Thakur, B.; Pathak, P.; Kalra, A.; Ahmad, S.

    2016-12-01

    The identification of primary drivers of streamflow may prove beneficial in forecasting streamflow in the Midwestern U.S. In the past researches, streamflow in the region have been strongly correlated with El Niño-Southern Oscillation (ENSO), Atlantic Multidecadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO). The present study takes in to account the pre-defined Pacific and Atlantic Ocean regions (e.g., ENSO, PDO, AMO) along with new regions with an intent to identify new significantly correlated regions. This study assesses the interrelationship between sea surface temperatures (SST) anomalies in the Pacific and Atlantic Ocean and seasonal streamflow in the Midwestern U.S. Average Pacific and Atlantic Ocean SST anomalies, were calculated for 2 different 3 month series: September-November and December-February so as to create a lead time varying from 3 to 9 months. Streamflow were averaged for three seasons: spring (April-June), spring-summer (April-August) and summer (June-August). The correlation between streamflow and SST is analyzed using singular value decomposition for a period of 1960-2013. The result of the study showed several regions-other than the known Pacific and Atlantic Ocean regions- that were significantly correlated with streamflow stations. Higher correlation between the climate indices and streamflow were observed as the lead time decreased. The identification of the associations between SST and streamflow and significant SST regions in the Pacific and Atlantic Ocean may enhance the skill of streamflow predictability and water management in the region.

  3. Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS), Version 5.0, Revision 2.0 (User’s Guide)

    Science.gov (United States)

    2012-05-03

    output (I/O) system. The framework provides tools for common modeling functions, as well as regridding, data decomposition, and communication on...Within this script, the user must specify both the site (DSRC or local) and the platform ( DAVINCI , EINSTEIN, or local machine) on which COAMPS is...being run. For example: site=navy_dsrc (for DSRC usage) site=nrlssc (for local NRL-SSC usage) platform= davinci or einstein (for DSRC usage

  4. Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) Version 5.0, Rev. 2.0 (User’s Guide)

    Science.gov (United States)

    2012-05-03

    output (I/O) system. The framework provides tools for common modeling functions, as well as regridding, data decomposition, and communication on...Within this script, the user must specify both the site (DSRC or local) and the platform ( DAVINCI , EINSTEIN, or local machine) on which COAMPS is...being run. For example: site=navy_dsrc (for DSRC usage) site=nrlssc (for local NRL-SSC usage) platform= davinci or einstein (for DSRC usage

  5. User’s Guide for the Coupled Ocean/Atmospheric Mesoscale Prediction System (COAMPS) Version 5.0

    Science.gov (United States)

    2010-03-30

    provides tools for common modeling functions, as well as regridding, data decomposition, and communication on parallel computers. NRL/MR/7320...specified gncomDir. If running COAMPS at the DSRC (e.g. BABBAGE, DAVINCI , or EINSTEIN), the global NCOM files will be copied to /scr/[user]/COAMPS/data...the site (DSRC or local) and the platform (BABBAGE. DAVINCI , EINSTEIN, or local machine) on which COAMPS is being run. site=navy_dsrc (for DSRC

  6. Investigation of hurricane Ivan using the coupled ocean-atmosphere-wave-sediment transport (COAWST) model

    Science.gov (United States)

    Zambon, Joseph B.; He, Ruoying; Warner, John C.

    2014-01-01

    The coupled ocean–atmosphere–wave–sediment transport (COAWST) model is used to hindcast Hurricane Ivan (2004), an extremely intense tropical cyclone (TC) translating through the Gulf of Mexico. Sensitivity experiments with increasing complexity in ocean–atmosphere–wave coupled exchange processes are performed to assess the impacts of coupling on the predictions of the atmosphere, ocean, and wave environments during the occurrence of a TC. Modest improvement in track but significant improvement in intensity are found when using the fully atmosphere–ocean-wave coupled configuration versus uncoupled (e.g., standalone atmosphere, ocean, or wave) model simulations. Surface wave fields generated in the fully coupled configuration also demonstrates good agreement with in situ buoy measurements. Coupled and uncoupled model-simulated sea surface temperature (SST) fields are compared with both in situ and remote observations. Detailed heat budget analysis reveals that the mixed layer temperature cooling in the deep ocean (on the shelf) is caused primarily by advection (equally by advection and diffusion).

  7. Modeling long-term carbon residue in the ocean-atmosphere system following large CO2 emissions

    Science.gov (United States)

    Towles, N. J.; Olson, P.; Gnanadesikan, A.

    2013-12-01

    We use the LOSCAR carbon cycle model (Zeebe et al., 2009; Zeebe, 2012) to calculate the residual carbon in the ocean and atmosphere following large CO2 emissions. We consider the system response to CO2 emissions ranging from 100 to 20000 PgC, and emission durations from 100 yr to 100 kyr, subject to a wide range of system parameters such as the strengths of silicate weathering and the oceanic biological carbon pump. We define the carbon gain factor as the ratio of residual carbon in the ocean-atmosphere to the total emitted carbon. For moderate sized emissions shorter than about 50 kyr, we find that the carbon gain factor grows during the emission and peaks at about 1.7, primarily due to the erosion of carbonate marine sediments. In contrast, for longer emissions, the carbon gain factor peaks at a smaller value, and for very large emissions (more than 5000 PgC), the gain factor decreases with emission size due to carbonate sediment exhaustion. This gain factor is sensitive to model parameters such as low latitude efficiency of the biological pump. The timescale for removal of the residual carbon (reducing the carbon gain factor to zero) depends strongly on the assumed sensitivity of silicate weathering to atmospheric pCO2, and ranges from less than one million years to several million years.

  8. Time-slice analysis of the Australian summer monsoon during the late Quaternary using the Fast Ocean Atmosphere Model

    Science.gov (United States)

    Marshall, A. G.; Lynch, A. H.

    2006-10-01

    We use the Fast Ocean Atmosphere Model (FOAM) to investigate the variation in the Australian summer monsoon over the last 55 000 years. A synthesis of palaeoenvironmental observations is used to constrain the model for six time slices: 55, 35, 21, 11, 6 and 0 ka. Both inter-hemispheric forcing and the seasonal timing of local insolation changes play key, and interacting, roles on the evolution and intensity of the monsoon.During the onset to the monsoon, a heat low develops to the west of Australia over the Indian Ocean in all time slices, but with varying strengths. Divergent outflow from Asia converges with the cyclonic flow to bring increased rainfall to northern Australia and the maritime continent. The relative importance of a low pressure pull and the high pressure push varies according to the strength of the pressure anomalies. Only in the middle Holocene is the low pressure pull the dominant forcing mechanism. At 21 ka, the climate shift to colder mean temperatures determines the large-scale dynamics of the monsoon.The general picture that emerges from these results is consistent with available palaeodata but highlights the importance of a broad regional perspective to ascribe the driving mechanisms at different times. Copyright

  9. Contribution of the bubbles to gas transfer across the ocean-atmosphere interface

    International Nuclear Information System (INIS)

    Memery, Laurent

    1983-05-01

    A first theoretical approach to gas transfer by bubbles is undertaken. Certain parameters which are neglected by smooth air-water interface models are studied. It is found that transfer velocity increases when solubility decreases. Further, bubble overpressure leads to water supersaturation at equilibrium, this supersaturation being more significant for less soluble gases. Although the transfer velocity remains roughly constant for a variable concentration gradient far from equilibrium, its range of variation becomes infinite near equilibrium. Because the notion of transfer velocity is not useful near equilibrium, attention is turned directly to the flux itself: the flux is a linear function of the concentration gradient. At least for tracers the coefficients of this function are entirely defined by the physico-chemical properties of the gas and by the bubble distribution. The dissertation is divided in three parts: - a synthesis which sums up the main experimental and theoretical results of the study of the influence of the bubbles created by breaking waves on gas transfer, - an article published in 'Journal of Geophysical Research', - an article submitted to 'Tellus'. (author) [fr

  10. Ocean-Atmosphere Coupling associated with Typhoons/ Hurricane and their impacts on marine ecosystem (Invited)

    Science.gov (United States)

    Tang, D. L.

    2010-12-01

    DanLing TANG South China Sea Institute of Oceanology, Chinese Academy of Sciences,Guangzhou, China Phone (86) 13924282728; Fax/Tel: (86) 020 89023203 (off), 020 89023191 (Lab),Email,lingzistdl@126.com, Typhoon / hurricane activities and their impacts on environments have been strengthening in both intensity and spatial coverage, along with global changes in the past several decades; however, our knowledge about impact of typhoon on the marine ecosystem is very scarce. We have conducted a series studies in the South China Sea (SCS), investigating phytoplankton, sea surface temperature (SST), fishery data and related factors before, during, and after typhoon. Satellite remote sensing and in situ observation data obtained from research cruise were applied. Our study showed that typhoon can support nutrients to surface phytoplankton by inducing upwelling and vertical mixing, and typhoon rain can also nourish marine phytoplankton; both typhoon winds and rain can enhance production of marine phytoplankton. Slow-moving typhoon induced phytoplankton blooms of higher Chlorophyll-a (Chl-a), the strong typhoon induced phytoplankton blooms of a large area. We conservatively estimate that typhoon periods may account for 3.5% of the annual primary production in the oligotrophic SCS. It indicated that one typhoon may induce transport of nutrient-rich water from depth and from the coast to offshore regions, nourishing phytoplankton biomass. More observations confirmed that typhoon can induce cold eddy, and cold eddy can support eddy-shape phytoplankton bloom by upwelling. We have suggested a new index to evaluate typhoon impact on marine ecosystem and environment. This is the first time to report moving eddies and eddy-shape phytoplankton blooms associated with tropical cyclone, the relationship among tropical cyclone, cold eddy upwelling and eddy-shape phytoplankton bloom may give some viewpoint on the tropical cyclone's affection on the mesoscale circulation. Those studies may

  11. Statistical and dynamical assessment of land-ocean-atmosphere interactions across North Africa

    Science.gov (United States)

    Yu, Yan

    North Africa is highly vulnerable to hydrologic variability and extremes, including impacts of climate change. The current understanding of oceanic versus terrestrial drivers of North African droughts and pluvials is largely model-based, with vast disagreement among models in terms of the simulated oceanic impacts and vegetation feedbacks. Regarding oceanic impacts, the relative importance of the tropical Pacific, tropical Indian, and tropical Atlantic Oceans in regulating the North African rainfall variability, as well as the underlying mechanism, remains debated among different modeling studies. Classic theory of land-atmosphere interactions across the Sahel ecotone, largely based on climate modeling experiments, has promoted positive vegetation-rainfall feedbacks associated with a dominant surface albedo mechanism. However, neither the proposed positive vegetation-rainfall feedback with its underlying albedo mechanism, nor its relative importance compared with oceanic drivers, has been convincingly demonstrated up to now using observational data. Here, the multivariate Generalized Equilibrium Feedback Assessment (GEFA) is applied in order to identify the observed oceanic and terrestrial drivers of North African climate and quantify their impacts. The reliability of the statistical GEFA method is first evaluated against dynamical experiments within the Community Earth System Model (CESM). In order to reduce the sampling error caused by short data records, the traditional GEFA approach is refined through stepwise GEFA, in which unimportant forcings are dropped through stepwise selection. In order to evaluate GEFA's reliability in capturing oceanic impacts, the atmospheric response to a sea-surface temperature (SST) forcing across the tropical Pacific, tropical Indian, and tropical Atlantic Ocean is estimated independently through ensembles of dynamical experiments and compared with GEFA-based assessments. Furthermore, GEFA's performance in capturing terrestrial

  12. The crucial role of ocean-atmosphere coupling on the Indian monsoon anomalous response during dipole events

    Energy Technology Data Exchange (ETDEWEB)

    Krishnan, R.; Swapna, P.; Ayantika, D.C.; Mujumdar, M. [Indian Institute of Tropical Meteorology, Climate and Global Modelling Division, Pune (India); Sundaram, Suchithra [Indian Institute of Tropical Meteorology, Climate and Global Modelling Division, Pune (India); Universite Catholique de Louvain, Institut d' Astronomie de Geophysique G. Lemaitre, Louvain-La-Neuve (Belgium); Kumar, Vinay [Indian Institute of Tropical Meteorology, Climate and Global Modelling Division, Pune (India); Florida State University, Department of Meteorology, Tallahassee, FL (United States)

    2011-07-15

    This paper examines an issue concerning the simulation of anomalously wet Indian summer monsoons like 1994 which co-occurred with strong positive Indian Ocean Dipole (IOD) conditions in the tropical Indian Ocean. Contrary to observations it has been noticed that standalone atmospheric general circulation models (AGCM) forced with observed SST boundary condition, consistently depicted a decrease of the summer monsoon rainfall during 1994 over the Indian region. Given the ocean-atmosphere coupling during IOD events, we have examined whether the failure of standalone AGCM simulations in capturing wet Indian monsoons like 1994 can be remedied by including a simple form of coupling that allows the monsoon circulation to dynamically interact with the IOD anomalies. With this view, we have performed a suite of simulations by coupling an AGCM to a slab-ocean model with spatially varying mixed-layer-depth (MLD) specified from observations for the 1994 IOD; as well as four other cases (1983, 1997, 2006, 2007). The specification of spatially varying MLD from observations allows us to constrain the model to observed IOD conditions. It is seen that the inclusion of coupling significantly improves the large-scale circulation response by strengthening the monsoon cross-equatorial flow; leading to precipitation enhancement over the subcontinent and rainfall decrease over south-eastern tropical Indian Ocean - in a manner broadly consistent with observations. A plausible physical mechanism is suggested to explain the monsoonal response in the coupled frame-work. These results warrant the need for improved monsoon simulations with fully coupled models to be able to better capture the observed monsoon interannual variability. (orig.)

  13. Implementation of the vortex force formalism in the coupled ocean-atmosphere-wave-sediment transport (COAWST) modeling system for inner shelf and surf zone applications

    Science.gov (United States)

    Kumar, Nirnimesh; Voulgaris, George; Warner, John C.; Olabarrieta, Maitane

    2012-01-01

    The coupled ocean-atmosphere-wave-sediment transport modeling system (COAWST) enables simulations that integrate oceanic, atmospheric, wave and morphological processes in the coastal ocean. Within the modeling system, the three-dimensional ocean circulation module (ROMS) is coupled with the wave generation and propagation model (SWAN) to allow full integration of the effect of waves on circulation and vice versa. The existing wave-current coupling component utilizes a depth dependent radiation stress approach. In here we present a new approach that uses the vortex force formalism. The formulation adopted and the various parameterizations used in the model as well as their numerical implementation are presented in detail. The performance of the new system is examined through the presentation of four test cases. These include obliquely incident waves on a synthetic planar beach and a natural barred beach (DUCK' 94); normal incident waves on a nearshore barred morphology with rip channels; and wave-induced mean flows outside the surf zone at the Martha's Vineyard Coastal Observatory (MVCO).

  14. Collaboratory for the Study of Earthquake Predictability

    Science.gov (United States)

    Schorlemmer, D.; Jordan, T. H.; Zechar, J. D.; Gerstenberger, M. C.; Wiemer, S.; Maechling, P. J.

    2006-12-01

    Earthquake prediction is one of the most difficult problems in physical science and, owing to its societal implications, one of the most controversial. The study of earthquake predictability has been impeded by the lack of an adequate experimental infrastructure---the capability to conduct scientific prediction experiments under rigorous, controlled conditions and evaluate them using accepted criteria specified in advance. To remedy this deficiency, the Southern California Earthquake Center (SCEC) is working with its international partners, which include the European Union (through the Swiss Seismological Service) and New Zealand (through GNS Science), to develop a virtual, distributed laboratory with a cyberinfrastructure adequate to support a global program of research on earthquake predictability. This Collaboratory for the Study of Earthquake Predictability (CSEP) will extend the testing activities of SCEC's Working Group on Regional Earthquake Likelihood Models, from which we will present first results. CSEP will support rigorous procedures for registering prediction experiments on regional and global scales, community-endorsed standards for assessing probability-based and alarm-based predictions, access to authorized data sets and monitoring products from designated natural laboratories, and software to allow researchers to participate in prediction experiments. CSEP will encourage research on earthquake predictability by supporting an environment for scientific prediction experiments that allows the predictive skill of proposed algorithms to be rigorously compared with standardized reference methods and data sets. It will thereby reduce the controversies surrounding earthquake prediction, and it will allow the results of prediction experiments to be communicated to the scientific community, governmental agencies, and the general public in an appropriate research context.

  15. Clinical studies of biomarkers in suicide prediction

    OpenAIRE

    Jokinen, Jussi

    2007-01-01

    Suicide is a major clinical problem in psychiatry and suicidal behaviours can be seen as a nosological entity per se. Predicting suicide is difficult due to its low base-rate and the limited specificity of clinical predictors. Prospective biological studies suggest that dysfunctions in the hypothalamo pituitary adrenal (HPA) axis and the serotonergic system have predictive power for suicide in mood disorders. Suicide attempt is the most robust clinical predictor making suici...

  16. Predicting Students Drop Out: A Case Study

    Science.gov (United States)

    Dekker, Gerben W.; Pechenizkiy, Mykola; Vleeshouwers, Jan M.

    2009-01-01

    The monitoring and support of university freshmen is considered very important at many educational institutions. In this paper we describe the results of the educational data mining case study aimed at predicting the Electrical Engineering (EE) students drop out after the first semester of their studies or even before they enter the study program…

  17. Collaborative project. Ocean-atmosphere interaction from meso-to planetary-scale. Mechanisms, parameterization, and variability

    Energy Technology Data Exchange (ETDEWEB)

    Small, Richard [National Center for Atmospheric Research, Boulder, CO (United States); Bryan, Frank [National Center for Atmospheric Research, Boulder, CO (United States); Tribbia, Joseph [National Center for Atmospheric Research, Boulder, CO (United States); Park, Sungsu [National Center for Atmospheric Research, Boulder, CO (United States); Dennis, John [National Center for Atmospheric Research, Boulder, CO (United States); Saravanan, R. [National Center for Atmospheric Research, Boulder, CO (United States); Schneider, Niklas [National Center for Atmospheric Research, Boulder, CO (United States); Kwon, Young-Oh [National Center for Atmospheric Research, Boulder, CO (United States)

    2015-06-11

    This project aims to improve long term global climate simulations by resolving ocean mesoscale activity and the corresponding response in the atmosphere. The main computational objectives are; i) to perform and assess Community Earth System Model (CESM) simulations with the new Community Atmospheric Model (CAM) spectral element dynamical core; ii) use static mesh refinement to focus on oceanic fronts; iii) develop a new Earth System Modeling tool to investigate the atmospheric response to fronts by selectively filtering surface flux fields in the CESM coupler. The climate research objectives are 1) to improve the coupling of ocean fronts and the atmospheric boundary layer via investigations of dependency on model resolution and stability functions: 2) to understand and simulate the ensuing tropospheric response that has recently been documented in observations: and 3) to investigate the relationship of ocean frontal variability to low frequency climate variability and the accompanying storm tracks and extremes in high resolution simulations. This is a collaborative multi-institution project consisting of computational scientists, climate scientists and climate model developers. It specifically aims at DOE objectives of advancing simulation and predictive capability of climate models through improvements in resolution and physical process representation.

  18. Progress Towards Achieving the Challenge of Indian Summer Monsoon Climate Simulation in a Coupled Ocean-Atmosphere Model

    Science.gov (United States)

    Hazra, Anupam; Chaudhari, Hemantkumar S.; Saha, Subodh Kumar; Pokhrel, Samir; Goswami, B. N.

    2017-10-01

    Simulation of the spatial and temporal structure of the monsoon intraseasonal oscillations (MISOs), which have effects on the seasonal mean and annual cycle of Indian summer monsoon (ISM) rainfall, remains a grand challenge for the state-of-the-art global coupled models. Biases in simulation of the amplitude and northward propagation of MISOs and related dry rainfall bias over ISM region in climate models are limiting the current skill of monsoon prediction. Recent observations indicate that the convective microphysics of clouds may be critical in simulating the observed MISOs. The hypothesis is strongly supported by high fidelity in simulation of the amplitude and space-time spectra of MISO by a coupled climate model, when our physically based modified cloud microphysics scheme is implemented in conjunction with a modified new Simple Arakawa Schubert (nSAS) convective parameterization scheme. Improved simulation of MISOs appears to have been aided by much improved simulation of the observed high cloud fraction and convective to stratiform rain fractions and resulted into a much improved simulation of the ISM rainfall, monsoon onset, and the annual cycle.

  19. Impacts of the Mesoscale Ocean-Atmosphere Coupling on the Peru-Chile Ocean Dynamics: The Current-Induced Wind Stress Modulation

    Science.gov (United States)

    Oerder, V.; Colas, F.; Echevin, V.; Masson, S.; Lemarié, F.

    2018-02-01

    The ocean dynamical responses to the surface current-wind stress interaction at the oceanic mesoscale are investigated in the South-East Pacific using a high-resolution regional ocean-atmosphere coupled model. Two simulations are compared: one includes the surface current in the wind stress computation while the other does not. In the coastal region, absolute wind velocities are different between the two simulations but the wind stress remains very similar. As a consequence, the mean regional oceanic circulation is almost unchanged. On the contrary, the mesoscale activity is strongly reduced when taking into account the effect of the surface current on the wind stress. This is caused by a weakening of the eddy kinetic energy generation near the coast by the wind work and to intensified offshore eddy damping. We show that, above coherent eddies, the current-stress interaction generates eddy damping through Ekman pumping and eddy kinetic energy dissipation through wind work. This alters significantly the coherent eddy vertical structures compared with the control simulation, weakening the temperature and vorticity anomalies and increasing strongly the vertical velocity anomalies associated to eddies.

  20. Heavy Rainfall Episodes in the Eastern Northeast Brazil Linked to Large-Scale Ocean-Atmosphere Conditions in the Tropical Atlantic

    Directory of Open Access Journals (Sweden)

    Yves K. Kouadio

    2012-01-01

    Full Text Available Relationships between simultaneous occurrences of distinctive atmospheric easterly wave (EW signatures that cross the south-equatorial Atlantic, intense mesoscale convective systems (lifespan > 2 hour that propagate westward over the western south-equatorial Atlantic, and subsequent strong rainfall episodes (anomaly > 10 mm·day−1 that occur in eastern Northeast Brazil (ENEB are investigated. Using a simple diagnostic analysis, twelve cases with EW lifespan ranging between 3 and 8 days and a mean velocity of 8 m·s−1 were selected and documented during each rainy season of 2004, 2005, and 2006. These cases, which represent 50% of the total number of strong rainfall episodes and 60% of the rainfall amount over the ENEB, were concomitant with an acceleration of the trade winds over the south-equatorial Atlantic, an excess of moisture transported westward from Africa to America, and a strengthening of the convective activity in the oceanic region close to Brazil. Most of these episodes occurred during positive sea surface temperature anomaly patterns over the entire south-equatorial Atlantic and low-frequency warm conditions within the oceanic mixing layer. A real-time monitoring and the simulation of this ocean-atmosphere relationship could help in forecasting such dramatic rainfall events.

  1. Comparative Study of Bancruptcy Prediction Models

    Directory of Open Access Journals (Sweden)

    Isye Arieshanti

    2013-09-01

    Full Text Available Early indication of bancruptcy is important for a company. If companies aware of  potency of their bancruptcy, they can take a preventive action to anticipate the bancruptcy. In order to detect the potency of a bancruptcy, a company can utilize a a model of bancruptcy prediction. The prediction model can be built using a machine learning methods. However, the choice of machine learning methods should be performed carefully. Because the suitability of a model depends on the problem specifically. Therefore, in this paper we perform a comparative study of several machine leaning methods for bancruptcy prediction. According to the comparative study, the performance of several models that based on machine learning methods (k-NN, fuzzy k-NN, SVM, Bagging Nearest Neighbour SVM, Multilayer Perceptron(MLP, Hybrid of MLP + Multiple Linear Regression, it can be showed that fuzzy k-NN method achieve the best performance with accuracy 77.5%

  2. The ocean-atmosphere response to wind-induced thermocline changes in the tropical South Western Indian Ocean

    NARCIS (Netherlands)

    Manola, Iris; Selten, F. M.; De Ruijter, W. P M; Hazeleger, W.

    2014-01-01

    In the Indian Ocean basin the sea surface temperatures (SSTs) are most sensitive to changes in the oceanic depth of the thermocline in the region of the Seychelles Dome. Observational studies have suggested that the strong SST variations in this region influence the atmospheric evolution around the

  3. Application of coupled ocean-atmospheric RTM SCIATRAN to retrieve chlorophyll and CDOM fluorescence from space: first results

    OpenAIRE

    Wolanin, Aleksandra; Dinter, Tilman; Rozanov, V.; Bracher, Astrid

    2012-01-01

    Colored dissolved organic matter (CDOM) is the main abiotic photoreactive constituent in marine waters. It strongly absorbs light in UV and blue region of light spectrum, reducing potentially harmful UV radiation, but also - when abundant - limiting the amount of light available for photosynthesis. DOM is a significant element in the carbon cycle and thus its optical properties have been studied extensively to estimate CDOM concentration and characterize its chemical composition. It has...

  4. Assessment of Urbanization on the Integrated Land-Ocean-Atmosphere Environment in Coastal Metropolis in Preparation for HyspIRI

    Science.gov (United States)

    Sequera, Pedro; McDonald, Kyle C.; Gonzalez, Jorge; Arend, Mark; Krakauer, Nir; Bornstein, Robert; Luvll, Jeffrey

    2012-01-01

    The need for comprehensive studies of the relationships between past and projected changes of regional climate and human activity in comple x urban environments has been well established. The HyspIRI preparato ry airborne activities in California, associated science and applicat ions research, and eventually HyspIRI itself provide an unprecedented opportunity for development and implementation of an integrated data and modeling analysis system focused on coastal urban environments. We will utilize HyspIRI preparatory data collections in developing ne w remote sensing-based tools for investigating the integrated urban e nvironment, emphasizing weather, climate, and energy demands in compl ex coastal cities.

  5. Quality-Controlled Underway Oceanographic and Meteorological Data from the Center for Ocean-Atmospheric Predictions Center (COAPS) - Shipboard Automated Meteorological and Oceanographic System (SAMOS)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Florida State University has been operating a data assembly center (DAC) to collect, quality evaluate, and distribute Shipboard Automated Meteorological and...

  6. Validation Test Report for the Coupled Ocean/Atmosphere MesoscalePrediction System (COAMPS) Version 5.0: Ocean/Wave Component Validation

    Science.gov (United States)

    2012-12-31

    wind flow ahead of the next extratropical low pressure system entering Europe . Figure 3.4-4 shows the mean SWH difference and mean NCOM-only and...RED) TC TRACKS ARE SHOWN. CIRCLES ON BOTH TRACKS REPRESENT HOURLY LOCATIONS OF THE STORM CENTERS. ..................................... 18  FIGURE...conditions such as wave boundary conditions, tides, wind, and storm surge. A quasi-stationary approach is used with stationary SWAN computations in a

  7. Dynamical seasonal climate prediction using an ocean-atmosphere coupled climate model developed in partnership between South Africa and the IRI

    CSIR Research Space (South Africa)

    Beraki, AF

    2014-02-01

    Full Text Available dedicated a large amount of resources to utilize Atmospheric General Circulation Models 66 (AGCMs) as operational seasonal forecast tools (Landman et al. 2012). These models 67 have all been developed outside of South Africa, but have been used extensively... Niña seasons (Landman et al. 2012; Landman and Beraki 2012). As noted above, coupled 99 models are largely assumed or hypothesized to represent the state of the art of seasonal 100 forecasting. In fact, it has been conclusively shown through...

  8. Predicting students drop out : a case study

    NARCIS (Netherlands)

    Dekker, G.W.; Pechenizkiy, M.; Vleeshouwers, J.M.; Barnes, T.; Desmarais, M.; Romero, C.; Ventura, S.

    2009-01-01

    The monitoring and support of university freshmen is considered very important at many educational institutions. In this paper we describe the results of the educational data mining case study aimed at predicting the Electrical Engineering (EE) students drop out after the first semester of their

  9. Impact of resolving the diurnal cycle in an ocean-atmosphere GCM. Pt. 1: a diurnally forced OGCM

    Energy Technology Data Exchange (ETDEWEB)

    Bernie, D.J. [University of Reading, National Centre for Atmospheric Science - Climate, Department of Meteorology, Reading (United Kingdom); Laboratoire d' Oceanographie et du Climat, Experimentation et Approches Numeriques, IPSL, Paris (France); Met Office Hadley Centre, Exeter, EX1 3PB (United Kingdom); Guilyardi, E. [University of Reading, National Centre for Atmospheric Science - Climate, Department of Meteorology, Reading (United Kingdom); Laboratoire d' Oceanographie et du Climat, Experimentation et Approches Numeriques, IPSL, Paris (France); Madec, G. [Laboratoire d' Oceanographie et du Climat, Experimentation et Approches Numeriques, IPSL, Paris (France); Slingo, J.M.; Woolnough, S.J. [University of Reading, National Centre for Atmospheric Science - Climate, Department of Meteorology, Reading (United Kingdom)

    2007-11-15

    The diurnal cycle is a fundamental time scale in the climate system, at which the upper ocean and atmosphere are routinely observed to vary. Current climate models, however, are not configured to resolve the diurnal cycle in the upper ocean or the interaction of the ocean and atmosphere on these time scales. This study examines the diurnal cycle of the tropical upper ocean and its climate impacts. In the present paper, the first of two, a high vertical resolution ocean general circulation model (OGCM), with modified physics, is developed which is able to resolve the diurnal cycle of sea surface temperature (SST) and current variability in the upper ocean. It is then validated against a satellite derived parameterization of diurnal SST variability and in-situ current observations. The model is then used to assess rectification of the intraseasonal SST response to the Madden-Julian oscillation (MJO) by the diurnal cycle of SST. Across the equatorial Indo-Pacific it is found that the diurnal cycle increases the intraseasonal SST response to the MJO by around 20%. In the Pacific, the diurnal cycle also modifies the exchange of momentum between equatorially divergent Ekman currents and the meridionally convergent geostrophic currents beneath, resulting in a 10% increase in the strength of the Ekman cells and equatorial upwelling. How the thermodynamic and dynamical impacts of the diurnal cycle effect the mean state, and variability, of the climate system cannot be fully investigated in the constrained design of ocean-only experiments presented here. The second part of this study, published separately, addresses the climate impacts of the diurnal cycle in the coupled system by coupling the OGCM developed here to an atmosphere general circulation model. (orig.)

  10. Ocean-Atmosphere CO2 Fluxes in the North Atlantic Subtropical Gyre: Association with Biochemical and Physical Factors during Spring

    Directory of Open Access Journals (Sweden)

    Macarena Burgos

    2015-08-01

    Full Text Available Sea surface partial pressure of CO2 (pCO2 was measured continuously in a transect of the North Atlantic subtropical gyre between Santo Domingo, Dominican Republic (18.1° N, 68.5° W and Vigo, Spain (41.9° N, 11.8° W during spring 2011. Additional biogeochemical and physical variables measured to identify factors controlling the surface pCO2 were analyzed in discrete samples collected at 16 sites along the transect at the surface and to a depth of 200 m. Sea surface pCO2 varied between 309 and 662 μatm, and showed differences between the western and eastern subtropical gyre. The subtropical gyre acted as a net CO2 sink, with a mean flux of −5.5 ± 2.2 mmol m−2 day−1. The eastern part of the transect, close to the North Atlantic Iberian upwelling off the Galician coast, was a CO2 source with an average flux of 33.5 ± 9.0 mmol m−2 day−1. Our results highlight the importance of making more surface pCO2 observations in the area located east of the Azores Islands since air-sea CO2 fluxes there are poorly studied.

  11. Geochemistry of coral from Papua New Guinea as a proxy for ENSO ocean-atmosphere interactions in the Pacific Warm Pool

    Science.gov (United States)

    Ayliffe, Linda K.; Bird, Michael I.; Gagan, Michael K.; Isdale, Peter J.; Scott-Gagan, Heather; Parker, Bruce; Griffin, David; Nongkas, Michael; McCulloch, Malcolm T.

    2004-12-01

    A Porites sp. coral growing offshore from the Sepik and Ramu Rivers in equatorial northern Papua New Guinea has yielded an accurate 20-year history (1977-1996) of sea surface temperature (SST), river discharge, and wind-induced mixing of the upper water column. Depressions in average SSTs of about 0.5-1.0 °C (indicated by coral Sr/Ca) and markedly diminished freshwater runoff to the coastal ocean (indicated by coral δ18O, δ13C and UV fluorescence) are evident during the El Niño - Southern Oscillation (ENSO) events of 1982-1983, 1987 and 1991-1993. The perturbations recorded by the coral are in good agreement with changes in instrumental SST and river discharge/precipitation records, which are known to be diagnostic of the response of the Pacific Warm Pool ocean-atmosphere system to El Niño. Consideration of coastal ocean dynamics indicates that the establishment of northwest monsoon winds promotes mixing of near-surface waters to greater depths in the first quarter of most years, making the coral record sensitive to changes in the Asian-Australian monsoon cycle. Sudden cooling of SSTs by ˜1°C following westerly wind episodes, as indicated by the coral Sr/Ca, is consistent with greater mixing in the upper water column at these times. Furthermore, the coral UV fluorescence and oxygen isotope data indicate minimal contribution of river runoff to surface ocean waters at the beginning of most years, during the time of maximum discharge. This abrupt shift in flood-plume behaviour appears to reflect the duration and magnitude of northwest monsoon winds, which tend to disperse flood plume waters to a greater extent in the water column when wind-mixing is enhanced. Our results suggest that a multi-proxy geochemical approach to the production of long coral records should provide comprehensive reconstructions of tropical paleoclimate processes operating on interannual timescales.

  12. Predicting Success Study Using Students GPA Category

    Directory of Open Access Journals (Sweden)

    Awan Setiawan

    2015-07-01

    Full Text Available Abstract. Maintaining student graduation rates are the main tasks of a University. High rates of student graduation and the quality of graduates is a success indicator of a university, which will have an impact on public confidence as stakeholders of higher education and the National Accreditation Board as a regulator (government. Making predictions of student graduation and determine the factors that hinders will be a valuable input for University. Data mining system facilitates the University to create the segmentation of students’ performance and prediction of their graduation. Segmentation of student by their performance can be classified in a quadrant chart is divided into 4 segments based on grade point average and the growth rate of students performance index per semester. Standard methodology in data mining i.e CRISP-DM (Cross Industry Standard Procedure for Data Mining will be implemented in this research. Making predictions, graduation can be done through the modeling process by utilizing the college database. Some algorithms such as C5, C & R Tree, CHAID, and Logistic Regression tested in order to find the best model. This research utilizes student performance data for several classes. Parameters used in addition to GPA also included the master's students data are expected to build the student profile data. The outcome of the study is the student category based on their study performance and prediction of graduation. Based on this prediction, the  university may recommend actions to be taken to improve the student  achievement index and graduation rates.Keywords: graduation, segmentation, quadrant GPA, data mining, modeling algorithms

  13. Predicting Success Study Using Students GPA Category

    Directory of Open Access Journals (Sweden)

    Awan Setiawan

    2015-06-01

    Full Text Available Abstract. Maintaining student graduation rates are the main tasks of a University. High rates of student graduation and the quality of graduates is a success indicator of a university, which will have an impact on public confidence as stakeholders of higher education and the National Accreditation Board as a regulator (government. Making predictions of student graduation and determine the factors that hinders will be a valuable input for University. Data mining system facilitates the University to create the segmentation of students’ performance and prediction of their graduation. Segmentation of student by their performance can be classified in a quadrant chart is divided into 4 segments based on grade point average and the growth rate of students performance index per semester. Standard methodology in data mining i.e CRISP-DM (Cross Industry Standard Procedure for Data Mining will be implemented in this research. Making predictions, graduation can be done through the modeling process by utilizing the college database. Some algorithms such as C5, C & R Tree, CHAID, and Logistic Regression tested in order to find the best model. This research utilizes student performance data for several classes. Parameters used in addition to GPA also included the master's students data are expected to build the student profile data. The outcome of the study is the student category based on their study performance and prediction of graduation. Based on this prediction, the university may recommend actions to be taken to improve the student achievement index and graduation rates. Keywords: graduation, segmentation, quadrant GPA, data mining, modeling algorithms

  14. Experimental and Numerical Studies of Atmosphere Water Interactions

    KAUST Repository

    Bou-Zeid, Elie

    2011-07-04

    Understanding and quantifying the interaction of the atmosphere with underlying water surfaces is of great importance for a wide range of scientific fields such as water resources management, climate studies of ocean-atmosphere exchange, and regional weat

  15. A comparison of observed extreme water levels at the German Bight elaborated through an extreme value analysis (EVA) with extremes derived from a regionally coupled ocean-atmospheric climate model (MPI-OM)

    Science.gov (United States)

    Möller, Jens; Heinrich, Hartmut

    2017-04-01

    As a consequence of climate change atmospheric and oceanographic extremes and their potential impacts on coastal regions are of growing concern for governmental authorities responsible for the transportation infrastructure. Highest risks for shipping as well as for rail and road traffic originate from combined effects of extremes of storm surges and heavy rainfall which sometimes lead to insufficient dewatering of inland waterways. The German Ministry of Transport and digital Infrastructure therefore has tasked its Network of Experts to investigate the possible evolutions of extreme threats for low lands and especially for Kiel Canal, which is an important shortcut for shipping between the North and Baltic Seas. In this study we present results of a comparison of an Extreme Value Analysis (EVA) carried out on gauge observations and values derived from a coupled Regional Ocean-Atmosphere Climate Model (MPI-OM). High water levels at the coasts of the North and Baltic Seas are one of the most important hazards which increase the risk of flooding of the low-lying land and prevents such areas from an adequate dewatering. In this study changes in the intensity (magnitude of the extremes) and duration of extreme water levels (above a selected threshold) are investigated for several gauge stations with data partly reaching back to 1843. Different methods are used for the extreme value statistics, (1) a stationary general Pareto distribution (GPD) model as well as (2) an instationary statistical model for better reproduction of the impact of climate change. Most gauge stations show an increase of the mean water level of about 1-2 mm/year, with a stronger increase of the highest water levels and a decrease (or lower increase) of the lowest water levels. Also, the duration of possible dewatering time intervals for the Kiel-Canal was analysed. The results for the historical gauge station observations are compared to the statistics of modelled water levels from the coupled

  16. Case studies in archaeological predictive modelling

    NARCIS (Netherlands)

    Verhagen, Jacobus Wilhelmus Hermanus Philippus

    2007-01-01

    In this thesis, a collection of papers is put together dealing with various quantitative aspects of predictive modelling and archaeological prospection. Among the issues covered are the effects of survey bias on the archaeological data used for predictive modelling, and the complexities of testing

  17. Traffic Predictive Control: Case Study and Evaluation

    Science.gov (United States)

    2017-06-26

    This project developed a quantile regression method for predicting future traffic flow at a signalized intersection by combining both historical and real-time data. The algorithm exploits nonlinear correlations in historical measurements and efficien...

  18. Initialization and Predictability of a Coupled ENSO Forecast Model

    Science.gov (United States)

    Chen, Dake; Zebiak, Stephen E.; Cane, Mark A.; Busalacchi, Antonio J.

    1997-01-01

    The skill of a coupled ocean-atmosphere model in predicting ENSO has recently been improved using a new initialization procedure in which initial conditions are obtained from the coupled model, nudged toward observations of wind stress. The previous procedure involved direct insertion of wind stress observations, ignoring model feedback from ocean to atmosphere. The success of the new scheme is attributed to its explicit consideration of ocean-atmosphere coupling and the associated reduction of "initialization shock" and random noise. The so-called spring predictability barrier is eliminated, suggesting that such a barrier is not intrinsic to the real climate system. Initial attempts to generalize the nudging procedure to include SST were not successful; possible explanations are offered. In all experiments forecast skill is found to be much higher for the 1980s than for the 1970s and 1990s, suggesting decadal variations in predictability.

  19. Flow prediction models using macroclimatic variables and multivariate statistical techniques in the Cauca River Valley

    International Nuclear Information System (INIS)

    Carvajal Escobar Yesid; Munoz, Flor Matilde

    2007-01-01

    The project this centred in the revision of the state of the art of the ocean-atmospheric phenomena that you affect the Colombian hydrology especially The Phenomenon Enos that causes a socioeconomic impact of first order in our country, it has not been sufficiently studied; therefore it is important to approach the thematic one, including the variable macroclimates associated to the Enos in the analyses of water planning. The analyses include revision of statistical techniques of analysis of consistency of hydrological data with the objective of conforming a database of monthly flow of the river reliable and homogeneous Cauca. Statistical methods are used (Analysis of data multivariante) specifically The analysis of principal components to involve them in the development of models of prediction of flows monthly means in the river Cauca involving the Lineal focus as they are the model autoregressive AR, ARX and Armax and the focus non lineal Net Artificial Network.

  20. Maintainability Prediction and Analysis Study. Revision A

    Science.gov (United States)

    1978-07-01

    basiL methodology for predicting Mmax (0) when an accurate representation of the overall repair time distribution is desired. The meth- odology...asia 4 a m a a2a ma a a .a.Ja .9 . ap- aba e a a aa a a a a a a aaaa 9 a * a*sSa - a - a...a a a a a i-a4a m a a a a a s-a a a a: 2 .. ama a a a a

  1. Looking for students' personal characteristics predicting study outcome.

    NARCIS (Netherlands)

    Dr. A. Bakx; Theo Bergen; Dr. Cyrille A.C. Van Bragt; Marcel Croon

    2011-01-01

    Abstract The central goal of this study is to clarify to what degree former education and students' personal characteristics (the 'Big Five personality characteristics', personal orientations on learning and students' study approach) may predict study outcome (required credits and study

  2. CFD Validation Studies for Hypersonic Flow Prediction

    Science.gov (United States)

    Gnoffo, Peter A.

    2001-01-01

    A series of experiments to measure pressure and heating for code validation involving hypersonic, laminar, separated flows was conducted at the Calspan-University at Buffalo Research Center (CUBRC) in the Large Energy National Shock (LENS) tunnel. The experimental data serves as a focus for a code validation session but are not available to the authors until the conclusion of this session. The first set of experiments considered here involve Mach 9.5 and Mach 11.3 N2 flow over a hollow cylinder-flare with 30 degree flare angle at several Reynolds numbers sustaining laminar, separated flow. Truncated and extended flare configurations are considered. The second set of experiments, at similar conditions, involves flow over a sharp, double cone with fore-cone angle of 25 degrees and aft-cone angle of 55 degrees. Both sets of experiments involve 30 degree compressions. Location of the separation point in the numerical simulation is extremely sensitive to the level of grid refinement in the numerical predictions. The numerical simulations also show a significant influence of Reynolds number on extent of separation. Flow unsteadiness was easily introduced into the double cone simulations using aggressive relaxation parameters that normally promote convergence.

  3. Auditing predictive models : a case study in crop growth

    NARCIS (Netherlands)

    Metselaar, K.

    1999-01-01

    Methods were developed to assess and quantify the predictive quality of simulation models, with the intent to contribute to evaluation of model studies by non-scientists. In a case study, two models of different complexity, LINTUL and SUCROS87, were used to predict yield of forage maize

  4. Application of a Reduced Order Kalman Filter to Initialize a Coupled Atmosphere-Ocean Model: Impact on the Prediction of El Nino

    Science.gov (United States)

    Ballabrera-Poy, Joaquim; Busalacchi, Antonio J.; Murtugudde, Ragu

    2000-01-01

    A reduced order Kalman Filter, based on a simplification of the Singular Evolutive Extended Kalman (SEEK) filter equations, is used to assimilate observed fields of the surface wind stress, sea surface temperature and sea level into the nonlinear coupled ocean-atmosphere model. The SEEK filter projects the Kalman Filter equations onto a subspace defined by the eigenvalue decomposition of the error forecast matrix, allowing its application to high dimensional systems. The Zebiak and Cane model couples a linear reduced gravity ocean model with a single vertical mode atmospheric model of Zebiak. The compatibility between the simplified physics of the model and each observed variable is studied separately and together. The results show the ability of the model to represent the simultaneous value of the wind stress, SST and sea level, when the fields are limited to the latitude band 10 deg S - 10 deg N. In this first application of the Kalman Filter to a coupled ocean-atmosphere prediction model, the sea level fields are assimilated in terms of the Kelvin and Rossby modes of the thermocline depth anomaly. An estimation of the error of these modes is derived from the projection of an estimation of the sea level error over such modes. This method gives a value of 12 for the error of the Kelvin amplitude, and 6 m of error for the Rossby component of the thermocline depth. The ability of the method to reconstruct the state of the equatorial Pacific and predict its time evolution is demonstrated. The method is shown to be quite robust for predictions I up to six months, and able to predict the onset of the 1997 warm event fifteen months before its occurrence.

  5. Modeling Seizure Self-Prediction: An E-Diary Study

    Science.gov (United States)

    Haut, Sheryl R.; Hall, Charles B.; Borkowski, Thomas; Tennen, Howard; Lipton, Richard B.

    2013-01-01

    Purpose A subset of patients with epilepsy successfully self-predicted seizures in a paper diary study. We conducted an e-diary study to ensure that prediction precedes seizures, and to characterize the prodromal features and time windows that underlie self-prediction. Methods Subjects 18 or older with LRE and ≥3 seizures/month maintained an e-diary, reporting AM/PM data daily, including mood, premonitory symptoms, and all seizures. Self-prediction was rated by, “How likely are you to experience a seizure [time frame]”? Five choices ranged from almost certain (>95% chance) to very unlikely. Relative odds of seizure (OR) within time frames was examined using Poisson models with log normal random effects to adjust for multiple observations. Key Findings Nineteen subjects reported 244 eligible seizures. OR for prediction choices within 6hrs was as high as 9.31 (1.92,45.23) for “almost certain”. Prediction was most robust within 6hrs of diary entry, and remained significant up to 12hrs. For 9 best predictors, average sensitivity was 50%. Older age contributed to successful self-prediction, and self-prediction appeared to be driven by mood and premonitory symptoms. In multivariate modeling of seizure occurrence, self-prediction (2.84; 1.68,4.81), favorable change in mood (0.82; 0.67,0.99) and number of premonitory symptoms (1,11; 1.00,1.24) were significant. Significance Some persons with epilepsy can self-predict seizures. In these individuals, the odds of a seizure following a positive prediction are high. Predictions were robust, not attributable to recall bias, and were related to self awareness of mood and premonitory features. The 6-hour prediction window is suitable for the development of pre-emptive therapy. PMID:24111898

  6. Prediction of postoperative pain: a systematic review of predictive experimental pain studies

    DEFF Research Database (Denmark)

    Werner, Mads Utke; Mjöbo, Helena N; Nielsen, Per R

    2010-01-01

    Quantitative testing of a patient's basal pain perception before surgery has the potential to be of clinical value if it can accurately predict the magnitude of pain and requirement of analgesics after surgery. This review includes 14 studies that have investigated the correlation between...... preoperative responses to experimental pain stimuli and clinical postoperative pain and demonstrates that the preoperative pain tests may predict 4-54% of the variance in postoperative pain experience depending on the stimulation methods and the test paradigm used. The predictive strength is much higher than...

  7. Looking for students'personal characteristics predicting study outcome

    NARCIS (Netherlands)

    Bergen, T.C.M.; Bragt, van C.A.C.; Bakx, A.W.E.A.; Croon, M.A.

    2011-01-01

    Abstract The central goal of this study is to clarify to what degree former education and students’ personal characteristics (the ‘Big Five personality characteristics’, personal orientations on learning and students’ study approach) may predict study outcome (required credits and study

  8. Ocean-atmosphere dynamics during Hurricane Ida and Nor'Ida: An application of the coupled ocean-;atmosphere–wave–sediment transport (COAWST) modeling system

    Science.gov (United States)

    Olabarrieta, Maitane; Warner, John C.; Armstrong, Brandy N.; Zambon, Joseph B.; He, Ruoying

    2012-01-01

    -based parameterization (OOST) provided the best results for wind and wave growth prediction. However, the best agreement between the measured (CODAR) and computed surface currents and storm surge values was obtained with the wave steepness-based roughness parameterization (TY2001), although the differences obtained with respect to DGHQ were not significant. The influence of sea surface temperature (SST) fields on the atmospheric boundary layer dynamics was examined; in particular, we evaluated how the SST affects wind wave generation, surface currents and storm surges. The integrated hydrograph and integrated wave height, parameters that are highly correlated with the storm damage potential, were found to be highly sensitive to the ocean surface roughness parameterization.

  9. Principles for guiding the ONKALO prediction-outcome studies

    International Nuclear Information System (INIS)

    Andersson, J.; Hudson, J.A.; Anttila, P.; Koskinen, L.; Pitkaenen, P.; Hautojaervi, A.; Wikstroem, L.

    2005-09-01

    This document provides the necessary foundation for establishing the strategy for the Prediction-Outcome studies currently being conducted by the ONKALO Modelling Task Force (OMTF) during the construction of the ONKALO ramp. These studies relate to the geology, rock mechanics, hydrogeology and hydrogeochemistry. The purpose of the Prediction-Outcome campaign currently underway in the ONKALO ramp tunnel is to optimize Posiva's ability to predict rock conditions ahead of the excavation face. The aim of the work is: to enhance confidence in ability to predict rock conditions in general - and especially for the repository volumes; (later) testing and verification of repository design rules as it would not be possible to make too many additional boreholes in repository volume; and to support the ongoing construction work and make possible the application of the CEIC method. The document also presents current plans for at what stages of the ONKALO construction predictions and outcome assessments will be made as well as current plans for what properties and impacts will be predicted. These plans will evidently be subject to revision during the course of the work. (orig.)

  10. Impact of lower stratospheric ozone on seasonal prediction systems

    Directory of Open Access Journals (Sweden)

    Kelebogile Mathole

    2014-03-01

    Full Text Available We conducted a comparison of trends in lower stratospheric temperatures and summer zonal wind fields based on 27 years of reanalysis data and output from hindcast simulations using a coupled ocean-atmospheric general circulation model (OAGCM. Lower stratospheric ozone in the OAGCM was relaxed to the observed climatology and increasing greenhouse gas concentrations were neglected. In the reanalysis, lower stratospheric ozone fields were better represented than in the OAGCM. The spring lower stratospheric/ upper tropospheric cooling in the polar cap observed in the reanalysis, which is caused by a direct ozone depletion in the past two decades and is in agreement with previous studies, did not appear in the OAGCM. The corresponding summer tropospheric response also differed between data sets. In the reanalysis, a statistically significant poleward trend of the summer jet position was found, whereas no such trend was found in the OAGCM. Furthermore, the jet position in the reanalysis exhibited larger interannual variability than that in the OAGCM. We conclude that these differences are caused by the absence of long-term lower stratospheric ozone changes in the OAGCM. Improper representation or non-inclusion of such ozone variability in a prediction model could adversely affect the accuracy of the predictability of summer rainfall forecasts over South Africa.

  11. Study on Predicting Axial Load Capacity of CFST Columns

    Science.gov (United States)

    Ravi Kumar, H.; Muthu, K. U.; Kumar, N. S.

    2017-11-01

    This work presents an analytical study and experimental study on the behaviour and ultimate load carrying capacity of axially compressed self-compacting concrete-filled steel tubular columns. Results of tests conducted by various researchers on 213 samples concrete-filled steel tubular columns are reported and present authors experimental data are reported. Two theoretical equations were derived for the prediction of the ultimate axial load strength of concrete-filled steel tubular columns. The results from prediction were compared with the experimental data. Validation to the experimental results was made.

  12. Analysis of the Effects of SST and Model Resolutions on the Identification of the 1993 Superstorm Using an Ocean-Atmosphere Coupled Regional System

    Science.gov (United States)

    Aktas, D.; Velissariou, P.; Chassignet, E.; Bourassa, M. A.

    2014-12-01

    The non-tropical storm, the 12-14 March 1993 Superstorm, which called the Storm of the Century had a wide reaching effect on the Northern Gulf of Mexico region and the East Coast of the United States. Previous studies show that the initial development of the storm could not be simulated accurately enough to represent the intensity and the evolution of the storm over the Gulf of Mexico region. The aim of this study is to identify the effects of the air-sea fluxes, the sea surface temperature (SST) and the model resolution on determining the intensity and the track of the storm more accurately. To this end, the outputs from two-way coupled model runs were examined to analyze the storm characteristics. Model configurations have been set within a coupled system framework that includes the atmospheric model Weather Research & Forecasting Model (WRF) and the ocean model Regional Ocean Model (ROMS). Three WRF domains assigned 15 km, 5 km and ~1.6 km resolutions, respectively and an 8 km resolution ROMS domain were used in the coupled system. The initial and boundary conditions for WRF were extracted from the NCEP Climate Forecast System Reanalysis (CFSR) products and the Hybrid Coordinate Ocean Model (HYCOM) generated SSTs while, the conditions for ROMS were extracted from HYCOM. Comparisons were performed against NOAA buoys and GridSAT brightness temperatures. Minimum mean sea level pressure (MSLP), maximum wind speed and storm locations were examined. Time series for MSLP and wind speed were used to illustrate how air-sea interaction and resolution changes storm intensity along the track. The results showing the RMS differences on the storm location and intensity of the storm are also presented.

  13. Use of Ocean Remote Sensing Data to Enhance Predictions with a Coupled General Circulation Model

    Science.gov (United States)

    Rienecker, Michele M.

    1999-01-01

    Surface height, sea surface temperature and surface wind observations from satellites have given a detailed time sequence of the initiation and evolution of the 1997/98 El Nino. The data have beet complementary to the subsurface TAO moored data in their spatial resolution and extent. The impact of satellite observations on seasonal prediction in the tropical Pacific using a coupled ocean-atmosphere general circulation model will be presented.

  14. Analysis of Sea Surface Fluxes at the Yellow Sea and East China Sea in Mid-Holocene Based on a Flexible Global Ocean-Atmosphere-Land System Model%基于耦合气候系统模式的中全新世黄、东海海表通量分析

    Institute of Scientific and Technical Information of China (English)

    薛玉虎; 毛新燕; 颜秀花; 赵传湖

    2014-01-01

    对中全新世(6,ka时期)海洋和气候的研究可加深人们对现阶段气候变化和海洋环境的认识,为预测未来海洋与气候环境变化提供一个重要参照.文章分析一个耦合气候系统模式 FGOALS-s2.0的模式结果,首先对其工业革命前(0,ka 时期)东亚地区夏季降水及冬、夏季10,m 风场的模拟结果进行评估,然后进一步对中全新世和工业革命前黄、东海海表大气强迫的季节变化进行了对比.结果显示:模式模拟出0,ka 时期东亚夏季降水从东南洋面至西北内陆减少的空间分布特点,冬、夏季10,m风场亦与观测大体一致;6,ka时期夏季,黄、东海风速较0,ka时期增大约0.8,m/s,16%左右;黄海风应力旋度值为正,东海为负,与0,ka 时期相比旋度绝对值均增大;同时,两海区接收的太阳短波辐射较0,ka 时期均增加,短波辐射的差异是中全新世夏季黄、东海海表的净热吸收增加的主要因子.6,ka 时期冬季,黄、东海北风加强,东海增加量在0.5~1.0,m/s,幅度约为10%,较黄海更为明显;两海区在冬季的净热释放也较0,ka 时期增大,东海释放更甚;冬季黄、东海风应力旋度较0,ka时期则无太大差别.研究表明,由于6,ka时期太阳辐射季节循环的改变,造成了黄、东海夏季风增强,海表净热通量也发生相应变化,该时期大气强迫场的变化可能会使黄、东海表层水温分布趋势发生较大改变,进而影响陆架环流格局.%It is significant to study the variations of ocean and climate between mid-Holocene(6,ka)and the present soas to provide reference for future climate prediction. Based on the results of a coupled ocean-atmosphere model FGOALS-s2.0, East Asian monsoon rainfall as well the surface wind in both summer and winter of the pre-Industrial(0,ka)are evaluated. And then atmospheric forcing on the Yellow Sea(YS)and the East China Sea(ECS)in winter

  15. Sedimentary organic matter and carbonate variations in the Chukchi Borderland in association with ice sheet and ocean-atmosphere dynamics over the last 155 kyr

    Science.gov (United States)

    Rella, S. F.; Uchida, M.

    2012-12-01

    Knowledge on past variability of sedimentary organic carbon in the Arctic Ocean is important to assess natural carbon cycling and transport processes related to global climate changes. However, the late Pleistocene oceanographic history of the Arctic is still poorly understood. In the present study we show sedimentary records of total organic carbon (TOC), CaCO3, benthic foraminiferal δ18O and the coarse grain size fraction from a piston core recovered from the northern Northwind Ridge in the far western Arctic Ocean. TOC shows orbital-scale increases and decreases during the past ~155 kyr that can be respectively correlated to the waxing and waning of large ice sheets dominating the Eurasian Arctic, suggesting advection of fine suspended matter derived from glacial erosion to the Northwind Ridge by eastward flowing intermediate water and/or surface water and sea ice during cold periods. At millennial scales, increases in TOC might correlate to a suite of Dansgaard-Oeschger Stadials between 120 and 45 ka BP indicating a possible response to abrupt northern hemispheric temperature changes. Between 70 and 45 ka BP, closures and openings of the Bering Strait could have additionally influenced TOC variability. CaCO3 contents tend to anti-correlate with TOC on both orbital and millennial time scales, which we interpret in terms of enhanced sediment advection from the carbonate-rich Canadian Arctic via an extended Beaufort Gyre during warm periods and increased organic carbon advection from the Siberian Arctic during cold periods when the Beaufort Gyre contracted. We propose that this pattern may be related to orbital- and millennial-scale variations of dominant atmospheric surface pressure systems expressed in mode shifts of the Arctic Oscillation.

  16. Sedimentary organic matter and carbonate variations in the Chukchi Borderland in association with ice sheet and ocean-atmosphere dynamics over the last 155 kyr

    Directory of Open Access Journals (Sweden)

    S. F. Rella

    2011-12-01

    Full Text Available Knowledge on past variability of sedimentary organic carbon in the Arctic Ocean is important to assess natural carbon cycling and transport processes related to global climate changes. However, the late Pleistocene oceanographic history of the Arctic is still poorly understood. In the present study we show sedimentary records of total organic carbon (TOC, CaCO3, benthic foraminiferal δ18O and the coarse grain size fraction from a piston core recovered from the northern Northwind Ridge in the far western Arctic Ocean, a region potentially sensitively responding to past variability in surface current regimes and sedimentary processes such as coastal erosion. An age model based on oxygen stratigraphy, radiocarbon dating and lithological constraints suggests that the piston core records paleoenvironmental changes of the last 155 kyr. TOC shows orbital-scale increases and decreases that can be respectively correlated to the waxing and waning of large ice sheets dominating the Eurasian Arctic, suggesting advection of fine suspended matter derived from glacial erosion to the Northwind Ridge by eastward flowing intermediate water and/or surface water and sea ice during cold episodes of the last two glacial-interglacial cycles. At millennial scales, increases in TOC might correlate to a suite of Dansgaard-Oeschger Stadials between 120 and 45 ka before present (BP indicating a possible response to abrupt northern hemispheric temperature changes. Between 70 and 45 ka BP, closures and openings of the Bering Strait could have additionally influenced TOC variability. CaCO3 content tends to anti-correlate with TOC on both orbital and millennial time scales, which we interpret in terms of enhanced sediment advection from the carbonate-rich Canadian Arctic via an extended Beaufort Gyre during warm periods of the last two glacial-interglacial cycles and increased organic carbon advection from the Siberian Arctic during cold

  17. Research and development studies for predicting the thermal fatigue

    International Nuclear Information System (INIS)

    Moulin, D.; Garnier, J.; Fissolo, A.; Lejeail, Y.; Stephan, J.M.; Moinereau, D.; Masson, J.

    2001-01-01

    This paper presents some studies in development or realized in the EDF and CEA laboratories, concerning the thermal fatigue damage in nuclear reactor components. The first part presents the basic principles and the methods of lifetime prediction. The second part gives some examples on sodium loop, water loop, welded junctions resistance to thermal fatigue and tests on fatigue specimen. (A.L.B.)

  18. Neurological abnormalities predict disability: the LADIS (Leukoaraiosis And DISability) study

    NARCIS (Netherlands)

    Poggesi, A.; Gouw, A.; van der Flier, W.M.; Pracucci, G.; Chabriat, H.; Erkinjuntti, T.; Fazekas, F.; Ferro, J.M.; Blahak, C.; Langhorne, P.; O'Brien, J.; Schmidt, R.; Visser, M.C.; Wahlund, L.O.; Waldemar, G.; Wallin, A.; Scheltens, P.; Inzitari, D.; Pantoni, L.

    2014-01-01

    To investigate the role of neurological abnormalities and magnetic resonance imaging (MRI) lesions in predicting global functional decline in a cohort of initially independent-living elderly subjects. The Leukoaraiosis And DISability (LADIS) Study, involving 11 European centres, was primarily aimed

  19. Water Habitat Study: Prediction Makes It More Meaningful.

    Science.gov (United States)

    Glasgow, Dennis R.

    1982-01-01

    Suggests a teaching strategy for water habitat studies to help students make a meaningful connection between physiochemical data (dissolved oxygen content, pH, and water temperature) and biological specimens they collect. Involves constructing a poster and using it to make predictions. Provides sample poster. (DC)

  20. Sexual abuse predicts functional somatic symptoms : An adolescent population study

    NARCIS (Netherlands)

    Bonvanie, Irma J.; van Gils, Anne; Janssens, Karin A. M.; Rosmalen, Judith G. M.

    The main aim of this study was to investigate the effect of childhood sexual abuse on medically not well explained or functional somatic symptoms (FSSs) in adolescents. We hypothesized that sexual abuse predicts higher levels of FSSs and that anxiety and depression contribute to this relationship.

  1. Falls prediction in elderly people : A 1-year prospective study

    NARCIS (Netherlands)

    Swanenburg, Jaap; de Bruin, Eling D.; Uebelhart, Daniel; Mulder, Theo

    The aim of the present study was to determine whether force plate variables in single- and dual-task situations are able to predict the risk of multiple falls in a community-dwelling elderly population. Two hundred and seventy elderly persons (225 females, 45 males; age, 73 7 years) performed

  2. The life prediction study of Rokkasho reprocessing plant materials

    International Nuclear Information System (INIS)

    Kiuchi, K.; Yano, M.; Takizawa, M.; Shibata, S.

    1998-01-01

    The life prediction study of major equipment materials used in heavily corrosive nitric acid solutions of the RRP was carried out. The nitric acid recovery made of type 304ULC austenitic steel and the dissolver made of type 705 metallic zirconium are selected on the present study. This study is composed of major three programs, namely, the mock-up tests by small-sized equipments simulated to the practical design, laboratory tests for examining corrosion controlling factors by small specimens and to establish the data base system for the life prediction. Important parameters on this study was extracted with analyzing the past data of the life prediction on the Tokai reprocessing equipments. The mock-ups design was made by considering the quantitative evaluation of the most important parts on objective equipments, namely, heat conducting tubes in an acid recovery evaporator and a thermal jacket in a dissolver. From pre-examinations, the effects of radioactive species, nitric acid solution chemistry, the corrosion mechanisms were elucidated. Mock-up testing conditions corrosion monitoring methods and a data base concept for the the life prediction were selected from pre-examination data by referencing the plant operation planning. (author)

  3. Predicting Word Reading Ability: A Quantile Regression Study

    Science.gov (United States)

    McIlraith, Autumn L.

    2018-01-01

    Predictors of early word reading are well established. However, it is unclear if these predictors hold for readers across a range of word reading abilities. This study used quantile regression to investigate predictive relationships at different points in the distribution of word reading. Quantile regression analyses used preschool and…

  4. Pedestrian Path Prediction with Recursive Bayesian Filters: A Comparative Study

    NARCIS (Netherlands)

    Schneider, N.; Gavrila, D.M.

    2013-01-01

    In the context of intelligent vehicles, we perform a comparative study on recursive Bayesian filters for pedestrian path prediction at short time horizons (< 2s). We consider Extended Kalman Filters (EKF) based on single dynamical models and Interacting Multiple Models (IMM) combining several such

  5. Updating risk prediction tools: a case study in prostate cancer.

    Science.gov (United States)

    Ankerst, Donna P; Koniarski, Tim; Liang, Yuanyuan; Leach, Robin J; Feng, Ziding; Sanda, Martin G; Partin, Alan W; Chan, Daniel W; Kagan, Jacob; Sokoll, Lori; Wei, John T; Thompson, Ian M

    2012-01-01

    Online risk prediction tools for common cancers are now easily accessible and widely used by patients and doctors for informed decision-making concerning screening and diagnosis. A practical problem is as cancer research moves forward and new biomarkers and risk factors are discovered, there is a need to update the risk algorithms to include them. Typically, the new markers and risk factors cannot be retrospectively measured on the same study participants used to develop the original prediction tool, necessitating the merging of a separate study of different participants, which may be much smaller in sample size and of a different design. Validation of the updated tool on a third independent data set is warranted before the updated tool can go online. This article reports on the application of Bayes rule for updating risk prediction tools to include a set of biomarkers measured in an external study to the original study used to develop the risk prediction tool. The procedure is illustrated in the context of updating the online Prostate Cancer Prevention Trial Risk Calculator to incorporate the new markers %freePSA and [-2]proPSA measured on an external case-control study performed in Texas, U.S.. Recent state-of-the art methods in validation of risk prediction tools and evaluation of the improvement of updated to original tools are implemented using an external validation set provided by the U.S. Early Detection Research Network. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  6. Scaling and predictability in stock markets: a comparative study.

    Directory of Open Access Journals (Sweden)

    Huishu Zhang

    Full Text Available Most people who invest in stock markets want to be rich, thus, many technical methods have been created to beat the market. If one knows the predictability of the price series in different markets, it would be easier for him/her to make the technical analysis, at least to some extent. Here we use one of the most basic sold-and-bought trading strategies to establish the profit landscape, and then calculate the parameters to characterize the strength of predictability. According to the analysis of scaling of the profit landscape, we find that the Chinese individual stocks are harder to predict than US ones, and the individual stocks are harder to predict than indexes in both Chinese stock market and US stock market. Since the Chinese (US stock market is a representative of emerging (developed markets, our comparative study on the markets of these two countries is of potential value not only for conducting technical analysis, but also for understanding physical mechanisms of different kinds of markets in terms of scaling.

  7. Scaling and predictability in stock markets: a comparative study.

    Science.gov (United States)

    Zhang, Huishu; Wei, Jianrong; Huang, Jiping

    2014-01-01

    Most people who invest in stock markets want to be rich, thus, many technical methods have been created to beat the market. If one knows the predictability of the price series in different markets, it would be easier for him/her to make the technical analysis, at least to some extent. Here we use one of the most basic sold-and-bought trading strategies to establish the profit landscape, and then calculate the parameters to characterize the strength of predictability. According to the analysis of scaling of the profit landscape, we find that the Chinese individual stocks are harder to predict than US ones, and the individual stocks are harder to predict than indexes in both Chinese stock market and US stock market. Since the Chinese (US) stock market is a representative of emerging (developed) markets, our comparative study on the markets of these two countries is of potential value not only for conducting technical analysis, but also for understanding physical mechanisms of different kinds of markets in terms of scaling.

  8. Short and inflamed cervix predicts spontaneous preterm birth (COLIBRI study).

    Science.gov (United States)

    Raiche, Evelyne; Ouellet, Annie; Berthiaume, Maryse; Rousseau, Éric; Pasquier, Jean-Charles

    2014-07-01

    To develop a new strategy of predicting spontaneous preterm birth (sPTB) by combination of transvaginal ultrasound (TVUS) assessment and inflammatory proteins detection in vaginal secretions. Prospective study of 87 women referred for cervical length assessment with a standardized TVUS combined to vaginal secretions sampling. Samples were analyzed for presence of 10 cytokines. Main outcome was sPTB (women at a median gestational age of 35.6 weeks of gestation. Short cervix (women with a short inflamed cervix. COLIBRI study used a novel, single-step method of vaginal secretions sampling during TVUS and demonstrated that combination of short cervix and IL-8 in vaginal secretions is a promising sPTB predictive test.

  9. Predicting introductory programming performance: A multi-institutional multivariate study

    Science.gov (United States)

    Bergin, Susan; Reilly, Ronan

    2006-12-01

    A model for predicting student performance on introductory programming modules is presented. The model uses attributes identified in a study carried out at four third-level institutions in the Republic of Ireland. Four instruments were used to collect the data and over 25 attributes were examined. A data reduction technique was applied and a logistic regression model using 10-fold stratified cross validation was developed. The model used three attributes: Leaving Certificate Mathematics result (final mathematics examination at second level), number of hours playing computer games while taking the module and programming self-esteem. Prediction success was significant with 80% of students correctly classified. The model also works well on a per-institution level. A discussion on the implications of the model is provided and future work is outlined.

  10. Predicting pulmonary tuberculosis in immigrants: a retrospective cohort study.

    Science.gov (United States)

    Heffernan, Courtney; Doroshenko, Alexander; Egedahl, Mary Lou; Barrie, James; Senthilselvan, Ambikaipakan; Long, Richard

    2018-04-01

    Our objective was to investigate whether pulmonary tuberculosis (PTB) can be predicted from features of a targeted medical history and basic laboratory investigations in immigrants. A retrospective cohort of 391 foreign-born adults referred to the Edmonton Tuberculosis Clinic (Edmonton, AB, Canada) was studied using multiple logistic regression analysis to predict PTB. Seven characteristics of disease were used as explanatory variables. Cross-validation assessed performance. Each predictor was tested on two outcomes: "culture-positive" and "smear-positive". Receiver operating characteristic (ROC) curves were generated and the area under the ROC curve (AUC) was quantified. Symptoms, subacute duration of symptoms, risk factors for reactivation of latent TB infection and anaemia were all associated with a positive culture (adjusted OR 1.79, 2.24, 1.72 and 2.28, respectively; p<0.05). Symptoms, inappropriate prescription of broad-spectrum antibiotics and a "typical" chest radiograph were associated with smear-positive PTB (adjusted OR 2.91, 1.55 and 12.34, respectively; p<0.05). ROC curve analysis was used to test e ach model, yielding AUC=0.91 for the outcome "culture-positive" disease and AUC=0.94 for the outcome "smear-positive" disease. PTB among the foreign-born can be predicted from a targeted medical history and basic laboratory investigations, raising the threshold of suspicion in settings where the disease is relatively rare.

  11. A study on the mechanism and prediction of mine subsidence

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Byung-Chan; Moon, Hyun-Koo [Hanyang University, Seoul(Korea)

    2001-06-30

    The ground subsidence problem due to the increasing number of abandoned coal mines becomes serious. Recently, the sinkhole type subsidence occurred in many abandoned mines has raised an urgent stability question on the nearby railroads, bridges and buildings. But the study on the mechanism of discontinuous subsidence has not attracted much attention in the past. This study is mainly concerned with the mechanism and prediction of mine subsidence. Analyzed and presented in this study are the maximum possible height of roof caving for various shapes of caved zone using bulking factor approach, the critical depth of protective coal seam using the limit equilibrium method, and the factor of safety of stops using the limit equilibrium method with the friction angle and cohesion of rock. As prediction tools the influence function method and the probabilistic method are presented. An empirical equation is obtained from the subsidence data in Chulam and Chungsung areas and applied to Manhang coal mine. The probability of subsidence in Manhang area turned out to be high according to the subsidence frequency of 9.66. (author). 12 refs., 7 tabs., 21 figs.

  12. Ultrasound cervical length in predicting preterm birth: Prospective study

    Directory of Open Access Journals (Sweden)

    Achour Radhouane

    2017-08-01

    Full Text Available Background Preterm birth is a leading cause of perinatal morbidity and mortality and represents a major public health problem. It is associated with a 15–20 per cent mortality rate and remains responsible for 75 per cent of perinatal deaths in foetuses without anomalies. Aims The aim of this study was to evaluate the importance of cervical length measured in the first trimester (11–14 Weeks of amenorrhea “WA” and the second trimester (20–24 Weeks of amenorrhea” WA” in an asymptomatic population of singleton pregnancies to assess the risk of spontaneous preterm birth compared to the digital assessment. Methods We conducted a prospective, longitudinal study involving 117 asymptomatic women with singleton pregnancies between January and December 2015. Results In our study, the clinical examination had a low positive predictive value and a low sensibility for screening women at risk of preterm delivery. Cervical length less than 35mm between 12–14WA and 30mm between 22–24WA predicts the occurrence of preterm birth with a high sensitivity (Se, and specificity (Sp. Conclusion We conclude that ultrasound screening of preterm delivery is now highly recommended.

  13. Breast cancer data analysis for survivability studies and prediction.

    Science.gov (United States)

    Shukla, Nagesh; Hagenbuchner, Markus; Win, Khin Than; Yang, Jack

    2018-03-01

    Breast cancer is the most common cancer affecting females worldwide. Breast cancer survivability prediction is challenging and a complex research task. Existing approaches engage statistical methods or supervised machine learning to assess/predict the survival prospects of patients. The main objectives of this paper is to develop a robust data analytical model which can assist in (i) a better understanding of breast cancer survivability in presence of missing data, (ii) providing better insights into factors associated with patient survivability, and (iii) establishing cohorts of patients that share similar properties. Unsupervised data mining methods viz. the self-organising map (SOM) and density-based spatial clustering of applications with noise (DBSCAN) is used to create patient cohort clusters. These clusters, with associated patterns, were used to train multilayer perceptron (MLP) model for improved patient survivability analysis. A large dataset available from SEER program is used in this study to identify patterns associated with the survivability of breast cancer patients. Information gain was computed for the purpose of variable selection. All of these methods are data-driven and require little (if any) input from users or experts. SOM consolidated patients into cohorts of patients with similar properties. From this, DBSCAN identified and extracted nine cohorts (clusters). It is found that patients in each of the nine clusters have different survivability time. The separation of patients into clusters improved the overall survival prediction accuracy based on MLP and revealed intricate conditions that affect the accuracy of a prediction. A new, entirely data driven approach based on unsupervised learning methods improves understanding and helps identify patterns associated with the survivability of patient. The results of the analysis can be used to segment the historical patient data into clusters or subsets, which share common variable values and

  14. Selecting Optimal Random Forest Predictive Models: A Case Study on Predicting the Spatial Distribution of Seabed Hardness

    Science.gov (United States)

    Li, Jin; Tran, Maggie; Siwabessy, Justy

    2016-01-01

    Spatially continuous predictions of seabed hardness are important baseline environmental information for sustainable management of Australia’s marine jurisdiction. Seabed hardness is often inferred from multibeam backscatter data with unknown accuracy and can be inferred from underwater video footage at limited locations. In this study, we classified the seabed into four classes based on two new seabed hardness classification schemes (i.e., hard90 and hard70). We developed optimal predictive models to predict seabed hardness using random forest (RF) based on the point data of hardness classes and spatially continuous multibeam data. Five feature selection (FS) methods that are variable importance (VI), averaged variable importance (AVI), knowledge informed AVI (KIAVI), Boruta and regularized RF (RRF) were tested based on predictive accuracy. Effects of highly correlated, important and unimportant predictors on the accuracy of RF predictive models were examined. Finally, spatial predictions generated using the most accurate models were visually examined and analysed. This study confirmed that: 1) hard90 and hard70 are effective seabed hardness classification schemes; 2) seabed hardness of four classes can be predicted with a high degree of accuracy; 3) the typical approach used to pre-select predictive variables by excluding highly correlated variables needs to be re-examined; 4) the identification of the important and unimportant predictors provides useful guidelines for further improving predictive models; 5) FS methods select the most accurate predictive model(s) instead of the most parsimonious ones, and AVI and Boruta are recommended for future studies; and 6) RF is an effective modelling method with high predictive accuracy for multi-level categorical data and can be applied to ‘small p and large n’ problems in environmental sciences. Additionally, automated computational programs for AVI need to be developed to increase its computational efficiency and

  15. Climate drift of AMOC, North Atlantic salinity and arctic sea ice in CFSv2 decadal predictions

    Science.gov (United States)

    Huang, Bohua; Zhu, Jieshun; Marx, Lawrence; Wu, Xingren; Kumar, Arun; Hu, Zeng-Zhen; Balmaseda, Magdalena A.; Zhang, Shaoqing; Lu, Jian; Schneider, Edwin K.; Kinter, James L., III

    2015-01-01

    There are potential advantages to extending operational seasonal forecast models to predict decadal variability but major efforts are required to assess the model fidelity for this task. In this study, we examine the North Atlantic climate simulated by the NCEP Climate Forecast System, version 2 (CFSv2), using a set of ensemble decadal hindcasts and several 30-year simulations initialized from realistic ocean-atmosphere states. It is found that a substantial climate drift occurs in the first few years of the CFSv2 hindcasts, which represents a major systematic bias and may seriously affect the model's fidelity for decadal prediction. In particular, it is noted that a major reduction of the upper ocean salinity in the northern North Atlantic weakens the Atlantic meridional overturning circulation (AMOC) significantly. This freshening is likely caused by the excessive freshwater transport from the Arctic Ocean and weakened subtropical water transport by the North Atlantic Current. A potential source of the excessive freshwater is the quick melting of sea ice, which also causes unrealistically thin ice cover in the Arctic Ocean. Our sensitivity experiments with adjusted sea ice albedo parameters produce a sustainable ice cover with realistic thickness distribution. It also leads to a moderate increase of the AMOC strength. This study suggests that a realistic freshwater balance, including a proper sea ice feedback, is crucial for simulating the North Atlantic climate and its variability.

  16. The Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management

    Directory of Open Access Journals (Sweden)

    César Hernández-Hernández

    2017-06-01

    Full Text Available Electricity load forecasting, optimal power system operation and energy management play key roles that can bring significant operational advantages to microgrids. This paper studies how methods based on time series and neural networks can be used to predict energy demand and production, allowing them to be combined with model predictive control. Comparisons of different prediction methods and different optimum energy distribution scenarios are provided, permitting us to determine when short-term energy prediction models should be used. The proposed prediction models in addition to the model predictive control strategy appear as a promising solution to energy management in microgrids. The controller has the task of performing the management of electricity purchase and sale to the power grid, maximizing the use of renewable energy sources and managing the use of the energy storage system. Simulations were performed with different weather conditions of solar irradiation. The obtained results are encouraging for future practical implementation.

  17. Autonomous orientation predicts longevity: New findings from the Nun Study.

    Science.gov (United States)

    Weinstein, Netta; Legate, Nicole; Ryan, William S; Hemmy, Laura

    2018-03-10

    Work on longevity has found protective social, cognitive, and emotional factors, but to date we have little understanding of the impact of motivational dynamics. Autonomy orientation, or stable patterns of self-regulation, is theorized to be a protective factor for long-term mental and physical health (Ryan & Deci, 2017), and it is therefore a prime candidate for examining how stable psychosocial factors are linked to longevity, or life expectancy. Essays written in the 1930s by participants in the Nun Study were coded for indicators of an autonomy orientation. These were selected in line with an extensive theoretical literature based in self-determination theory (Deci & Ryan, 1985). Essays were coded for the propensity for choice in action, susceptibility to pressure, self-reflection, integration of experiences, and parental support for autonomy. These coded variables were used to predict age of death. Using 176 codable essays provided by now-deceased participants, linear regression analyses revealed that choiceful behavior, self-reflection, and parent autonomy support predicted age of death. Participants who demonstrated these stable and beneficial motivational characteristics lived longer. Personality constructs reflecting a healthy form of self-regulation are associated with long-term health. Implications for health interventions are discussed. © 2018 The Authors. Journal of Personality Published by Wiley Periodicals, Inc.

  18. Stroke-induced immunodepression and dysphagia independently predict stroke-associated pneumonia - The PREDICT study.

    Science.gov (United States)

    Hoffmann, Sarah; Harms, Hendrik; Ulm, Lena; Nabavi, Darius G; Mackert, Bruno-Marcel; Schmehl, Ingo; Jungehulsing, Gerhard J; Montaner, Joan; Bustamante, Alejandro; Hermans, Marcella; Hamilton, Frank; Göhler, Jos; Malzahn, Uwe; Malsch, Carolin; Heuschmann, Peter U; Meisel, Christian; Meisel, Andreas

    2017-12-01

    Stroke-associated pneumonia is a frequent complication after stroke associated with poor outcome. Dysphagia is a known risk factor for stroke-associated pneumonia but accumulating evidence suggests that stroke induces an immunodepressive state increasing susceptibility for stroke-associated pneumonia. We aimed to confirm that stroke-induced immunodepression syndrome is associated with stroke-associated pneumonia independently from dysphagia by investigating the predictive properties of monocytic HLA-DR expression as a marker of immunodepression as well as biomarkers for inflammation (interleukin-6) and infection (lipopolysaccharide-binding protein). This was a prospective, multicenter study with 11 study sites in Germany and Spain, including 486 patients with acute ischemic stroke. Daily screening for stroke-associated pneumonia, dysphagia and biomarkers was performed. Frequency of stroke-associated pneumonia was 5.2%. Dysphagia and decreased monocytic HLA-DR were independent predictors for stroke-associated pneumonia in multivariable regression analysis. Proportion of pneumonia ranged between 0.9% in the higher monocytic HLA-DR quartile (≥21,876 mAb/cell) and 8.5% in the lower quartile (≤12,369 mAb/cell). In the presence of dysphagia, proportion of pneumonia increased to 5.9% and 18.8%, respectively. Patients without dysphagia and normal monocytic HLA-DR expression had no stroke-associated pneumonia risk. We demonstrate that dysphagia and stroke-induced immunodepression syndrome are independent risk factors for stroke-associated pneumonia. Screening for immunodepression and dysphagia might be useful for identifying patients at high risk for stroke-associated pneumonia.

  19. Multi-model assessment of the impact of soil moisture initialization on mid-latitude summer predictability

    Science.gov (United States)

    Ardilouze, Constantin; Batté, L.; Bunzel, F.; Decremer, D.; Déqué, M.; Doblas-Reyes, F. J.; Douville, H.; Fereday, D.; Guemas, V.; MacLachlan, C.; Müller, W.; Prodhomme, C.

    2017-12-01

    Land surface initial conditions have been recognized as a potential source of predictability in sub-seasonal to seasonal forecast systems, at least for near-surface air temperature prediction over the mid-latitude continents. Yet, few studies have systematically explored such an influence over a sufficient hindcast period and in a multi-model framework to produce a robust quantitative assessment. Here, a dedicated set of twin experiments has been carried out with boreal summer retrospective forecasts over the 1992-2010 period performed by five different global coupled ocean-atmosphere models. The impact of a realistic versus climatological soil moisture initialization is assessed in two regions with high potential previously identified as hotspots of land-atmosphere coupling, namely the North American Great Plains and South-Eastern Europe. Over the latter region, temperature predictions show a significant improvement, especially over the Balkans. Forecast systems better simulate the warmest summers if they follow pronounced dry initial anomalies. It is hypothesized that models manage to capture a positive feedback between high temperature and low soil moisture content prone to dominate over other processes during the warmest summers in this region. Over the Great Plains, however, improving the soil moisture initialization does not lead to any robust gain of forecast quality for near-surface temperature. It is suggested that models biases prevent the forecast systems from making the most of the improved initial conditions.

  20. Predicting water solubility of congeners: Chloronaphthalenes-A case study

    Energy Technology Data Exchange (ETDEWEB)

    Puzyn, Tomasz, E-mail: puzi@qsar.eu.org [Faculty of Chemistry, University of Gdansk, Sobieskiego 18, 80-952 Gdansk (Poland); Mostrag, Aleksandra; Falandysz, Jerzy [Faculty of Chemistry, University of Gdansk, Sobieskiego 18, 80-952 Gdansk (Poland); Kholod, Yana; Leszczynski, Jerzy [NSF CREST Nanotoxicity Center, Department of Chemistry, Jackson State University, 1325 Lynch St, Jackson, MS 39217-0510 (United States)

    2009-10-30

    Since the important physicochemical data for chloronaphtalenes (PCNs) are still scarce, we have predicted water solubility (log S) of all 75 congeners with the Quantitative Structure-Property Relationship (QSPR) scheme. The values of log S, predicted by the most efficient model, varied from 0.01 to 1660 {mu}g dm{sup -3} (2.85 x 10{sup -11}-1.02 x 10{sup -5} mol dm{sup -3}), depending on the number of chlorine atoms present in the molecule and the substitution pattern. We found that the main factor determining relative differences in solubility between the congeners is the solvent accessible volume related to the cavitation process occurring in the solvent. The results are presented as a case study of QSPR modeling for those Persistent Organic Pollutants (POPs) that exist as families of congeners. By investigating the impact of (i) the way of the molecular descriptors' calculation, (ii) the size of applied database and (iii) chemometric method of modeling (Multiple Linear Regression, MLR, and/or Partial Least Squares regression, PLS) on the quality of the models we proposed general recommendations for dealing with congeners. We found that the combination of the B3LYP functional with 6-311++G(d,p) basis set was the most optimal technique of the molecular descriptors' calculation for congeners when comparing with semi-empirical PM3, ab initio Hartee-Fock (HF), and Moller-Pleset 2 (MP2) method carried out with different-size basis sets. Moreover, the model developed with a larger and more general database that includes chloronaphthalenes, polychlorinated dibezno-p-dioxins, furans and biphenyls predicted the values of log S for PCNs noticeable worse than the model calibrated only on PCNs. In the later case it was possible to obtain satisfactory results by employing even the simplest MLR method and only one molecular descriptor. The values of log S were also calculated with the WSKOWIN and COSMO-RS models as the reference techniques and then compared to our

  1. Predicting water solubility of congeners: Chloronaphthalenes-A case study

    International Nuclear Information System (INIS)

    Puzyn, Tomasz; Mostrag, Aleksandra; Falandysz, Jerzy; Kholod, Yana; Leszczynski, Jerzy

    2009-01-01

    Since the important physicochemical data for chloronaphtalenes (PCNs) are still scarce, we have predicted water solubility (log S) of all 75 congeners with the Quantitative Structure-Property Relationship (QSPR) scheme. The values of log S, predicted by the most efficient model, varied from 0.01 to 1660 μg dm -3 (2.85 x 10 -11 -1.02 x 10 -5 mol dm -3 ), depending on the number of chlorine atoms present in the molecule and the substitution pattern. We found that the main factor determining relative differences in solubility between the congeners is the solvent accessible volume related to the cavitation process occurring in the solvent. The results are presented as a case study of QSPR modeling for those Persistent Organic Pollutants (POPs) that exist as families of congeners. By investigating the impact of (i) the way of the molecular descriptors' calculation, (ii) the size of applied database and (iii) chemometric method of modeling (Multiple Linear Regression, MLR, and/or Partial Least Squares regression, PLS) on the quality of the models we proposed general recommendations for dealing with congeners. We found that the combination of the B3LYP functional with 6-311++G(d,p) basis set was the most optimal technique of the molecular descriptors' calculation for congeners when comparing with semi-empirical PM3, ab initio Hartee-Fock (HF), and Moller-Pleset 2 (MP2) method carried out with different-size basis sets. Moreover, the model developed with a larger and more general database that includes chloronaphthalenes, polychlorinated dibezno-p-dioxins, furans and biphenyls predicted the values of log S for PCNs noticeable worse than the model calibrated only on PCNs. In the later case it was possible to obtain satisfactory results by employing even the simplest MLR method and only one molecular descriptor. The values of log S were also calculated with the WSKOWIN and COSMO-RS models as the reference techniques and then compared to our results.

  2. A Realistic Seizure Prediction Study Based on Multiclass SVM.

    Science.gov (United States)

    Direito, Bruno; Teixeira, César A; Sales, Francisco; Castelo-Branco, Miguel; Dourado, António

    2017-05-01

    A patient-specific algorithm, for epileptic seizure prediction, based on multiclass support-vector machines (SVM) and using multi-channel high-dimensional feature sets, is presented. The feature sets, combined with multiclass classification and post-processing schemes aim at the generation of alarms and reduced influence of false positives. This study considers 216 patients from the European Epilepsy Database, and includes 185 patients with scalp EEG recordings and 31 with intracranial data. The strategy was tested over a total of 16,729.80[Formula: see text]h of inter-ictal data, including 1206 seizures. We found an overall sensitivity of 38.47% and a false positive rate per hour of 0.20. The performance of the method achieved statistical significance in 24 patients (11% of the patients). Despite the encouraging results previously reported in specific datasets, the prospective demonstration on long-term EEG recording has been limited. Our study presents a prospective analysis of a large heterogeneous, multicentric dataset. The statistical framework based on conservative assumptions, reflects a realistic approach compared to constrained datasets, and/or in-sample evaluations. The improvement of these results, with the definition of an appropriate set of features able to improve the distinction between the pre-ictal and nonpre-ictal states, hence minimizing the effect of confounding variables, remains a key aspect.

  3. Do infant behaviors following immunization predict attachment? An exploratory study.

    Science.gov (United States)

    Horton, Rachel; Pillai Riddell, Rebecca; Moran, Greg; Lisi, Diana

    2016-01-01

    The relationship between infant behaviors during routine immunization, pre- and post-needle, and infant attachment was explored. A total of 130 parent-infant dyads were recruited from a larger longitudinal study and videotaped during routine immunization at 12 months and the Strange Situation Procedure (SSP) at 14 months. Six infant behaviors were coded for 1-minute pre-needle and 3-minutes post-needle. Attachment was operationalized according to the secure/avoidant/resistant/disorganized categories. As expected, none of the pre-needle behaviors predicted attachment. Proximity-seeking post-needle significantly discriminated attachment categorizations. Secure infants were more likely to seek proximity to caregivers post-needle in comparison with avoidant and disorganized infants. Proximity-seeking following immunization was positively correlated with proximity-seeking during the SSP and negatively correlated with avoidance and disorganization during the SSP. Infant proximity-seeking during immunization is associated with attachment security and parallels behaviors observed during the SSP. More research is needed to identify behavioral markers of disorganization.

  4. Reduce manual curation by combining gene predictions from multiple annotation engines, a case study of start codon prediction.

    Directory of Open Access Journals (Sweden)

    Thomas H A Ederveen

    Full Text Available Nowadays, prokaryotic genomes are sequenced faster than the capacity to manually curate gene annotations. Automated genome annotation engines provide users a straight-forward and complete solution for predicting ORF coordinates and function. For many labs, the use of AGEs is therefore essential to decrease the time necessary for annotating a given prokaryotic genome. However, it is not uncommon for AGEs to provide different and sometimes conflicting predictions. Combining multiple AGEs might allow for more accurate predictions. Here we analyzed the ab initio open reading frame (ORF calling performance of different AGEs based on curated genome annotations of eight strains from different bacterial species with GC% ranging from 35-52%. We present a case study which demonstrates a novel way of comparative genome annotation, using combinations of AGEs in a pre-defined order (or path to predict ORF start codons. The order of AGE combinations is from high to low specificity, where the specificity is based on the eight genome annotations. For each AGE combination we are able to derive a so-called projected confidence value, which is the average specificity of ORF start codon prediction based on the eight genomes. The projected confidence enables estimating likeliness of a correct prediction for a particular ORF start codon by a particular AGE combination, pinpointing ORFs notoriously difficult to predict start codons. We correctly predict start codons for 90.5±4.8% of the genes in a genome (based on the eight genomes with an accuracy of 81.1±7.6%. Our consensus-path methodology allows a marked improvement over majority voting (9.7±4.4% and with an optimal path ORF start prediction sensitivity is gained while maintaining a high specificity.

  5. Study on MPGA-BP of Gravity Dam Deformation Prediction

    Directory of Open Access Journals (Sweden)

    Xiaoyu Wang

    2017-01-01

    Full Text Available Displacement is an important physical quantity of hydraulic structures deformation monitoring, and its prediction accuracy is the premise of ensuring the safe operation. Most existing metaheuristic methods have three problems: (1 falling into local minimum easily, (2 slowing convergence, and (3 the initial value’s sensitivity. Resolving these three problems and improving the prediction accuracy necessitate the application of genetic algorithm-based backpropagation (GA-BP neural network and multiple population genetic algorithm (MPGA. A hybrid multiple population genetic algorithm backpropagation (MPGA-BP neural network algorithm is put forward to optimize deformation prediction from periodic monitoring surveys of hydraulic structures. This hybrid model is employed for analyzing the displacement of a gravity dam in China. The results show the proposed model is superior to an ordinary BP neural network and statistical regression model in the aspect of global search, convergence speed, and prediction accuracy.

  6. Blast Impact Prediction Studies at Ghana Manganese Company ...

    African Journals Online (AJOL)

    Michael

    2015-06-01

    Jun 1, 2015 ... Keywords: Blast impact, Environment, Prediction, Regulatory threshold. 1 Introduction ... Noise is an environmental nuisance. .... explosion energy released into the ground generates vibration waves within the rock. Several.

  7. Prediction of Job Performance: Review of Military Studies

    Science.gov (United States)

    1982-03-01

    an assessment center to predict filed leadership performance of Army officers and NCOs. Proceedings of the 19th Annual Military Testing Association...C. Behaviors, results, and organizational effectiveness: The problem of criteria. In Dunnette, M. D. (Ed.), Handbook of Industrial and organizatin ...than for the Navy enlisted group. 30. Dyer, F. N., & Hlilligoss, R. Z. Using an assessment center to predict field leadership performance of Army

  8. Prediction in Child Development: A Longitudinal Study of Adoptive and Nonadoptive Families. The Delaware Family Study.

    Science.gov (United States)

    Hoopes, Janet L.

    A longitudinal study was conducted to determine factors predicting successful adoptions before placement and to identify differences and similarities between adoptive and biological families. Data collected on both adopted children and on their adoptive families before placement was related to data collected on the same children and families 6…

  9. Development and validation of a risk model for prediction of hazardous alcohol consumption in general practice attendees: the predictAL study.

    Science.gov (United States)

    King, Michael; Marston, Louise; Švab, Igor; Maaroos, Heidi-Ingrid; Geerlings, Mirjam I; Xavier, Miguel; Benjamin, Vicente; Torres-Gonzalez, Francisco; Bellon-Saameno, Juan Angel; Rotar, Danica; Aluoja, Anu; Saldivia, Sandra; Correa, Bernardo; Nazareth, Irwin

    2011-01-01

    Little is known about the risk of progression to hazardous alcohol use in people currently drinking at safe limits. We aimed to develop a prediction model (predictAL) for the development of hazardous drinking in safe drinkers. A prospective cohort study of adult general practice attendees in six European countries and Chile followed up over 6 months. We recruited 10,045 attendees between April 2003 to February 2005. 6193 European and 2462 Chilean attendees recorded AUDIT scores below 8 in men and 5 in women at recruitment and were used in modelling risk. 38 risk factors were measured to construct a risk model for the development of hazardous drinking using stepwise logistic regression. The model was corrected for over fitting and tested in an external population. The main outcome was hazardous drinking defined by an AUDIT score ≥8 in men and ≥5 in women. 69.0% of attendees were recruited, of whom 89.5% participated again after six months. The risk factors in the final predictAL model were sex, age, country, baseline AUDIT score, panic syndrome and lifetime alcohol problem. The predictAL model's average c-index across all six European countries was 0.839 (95% CI 0.805, 0.873). The Hedge's g effect size for the difference in log odds of predicted probability between safe drinkers in Europe who subsequently developed hazardous alcohol use and those who did not was 1.38 (95% CI 1.25, 1.51). External validation of the algorithm in Chilean safe drinkers resulted in a c-index of 0.781 (95% CI 0.717, 0.846) and Hedge's g of 0.68 (95% CI 0.57, 0.78). The predictAL risk model for development of hazardous consumption in safe drinkers compares favourably with risk algorithms for disorders in other medical settings and can be a useful first step in prevention of alcohol misuse.

  10. Development and validation of a risk model for prediction of hazardous alcohol consumption in general practice attendees: the predictAL study.

    Directory of Open Access Journals (Sweden)

    Michael King

    Full Text Available Little is known about the risk of progression to hazardous alcohol use in people currently drinking at safe limits. We aimed to develop a prediction model (predictAL for the development of hazardous drinking in safe drinkers.A prospective cohort study of adult general practice attendees in six European countries and Chile followed up over 6 months. We recruited 10,045 attendees between April 2003 to February 2005. 6193 European and 2462 Chilean attendees recorded AUDIT scores below 8 in men and 5 in women at recruitment and were used in modelling risk. 38 risk factors were measured to construct a risk model for the development of hazardous drinking using stepwise logistic regression. The model was corrected for over fitting and tested in an external population. The main outcome was hazardous drinking defined by an AUDIT score ≥8 in men and ≥5 in women.69.0% of attendees were recruited, of whom 89.5% participated again after six months. The risk factors in the final predictAL model were sex, age, country, baseline AUDIT score, panic syndrome and lifetime alcohol problem. The predictAL model's average c-index across all six European countries was 0.839 (95% CI 0.805, 0.873. The Hedge's g effect size for the difference in log odds of predicted probability between safe drinkers in Europe who subsequently developed hazardous alcohol use and those who did not was 1.38 (95% CI 1.25, 1.51. External validation of the algorithm in Chilean safe drinkers resulted in a c-index of 0.781 (95% CI 0.717, 0.846 and Hedge's g of 0.68 (95% CI 0.57, 0.78.The predictAL risk model for development of hazardous consumption in safe drinkers compares favourably with risk algorithms for disorders in other medical settings and can be a useful first step in prevention of alcohol misuse.

  11. Learning Political Science with Prediction Markets: An Experimental Study

    Science.gov (United States)

    Ellis, Cali Mortenson; Sami, Rahul

    2012-01-01

    Prediction markets are designed to aggregate the information of many individuals to forecast future events. These markets provide participants with an incentive to seek information and a forum for interaction, making markets a promising tool to motivate student learning. We carried out a quasi-experiment in an introductory political science class…

  12. A Comparative Study Using CFD to Predict Iced Airfoil Aerodynamics

    Science.gov (United States)

    Chi, x.; Li, Y.; Chen, H.; Addy, H. E.; Choo, Y. K.; Shih, T. I-P.

    2005-01-01

    WIND, Fluent, and PowerFLOW were used to predict the lift, drag, and moment coefficients of a business-jet airfoil with a rime ice (rough and jagged, but no protruding horns) and with a glaze ice (rough and jagged end has two or more protruding horns) for angles of attack from zero to and after stall. The performance of the following turbulence models were examined by comparing predictions with available experimental data. Spalart-Allmaras (S-A), RNG k-epsilon, shear-stress transport, v(sup 2)-f, and a differential Reynolds stress model with and without non-equilibrium wall functions. For steady RANS simulations, WIND and FLUENT were found to give nearly identical results if the grid about the iced airfoil, the turbulence model, and the order of accuracy of the numerical schemes used are the same. The use of wall functions was found to be acceptable for the rime ice configuration and the flow conditions examined. For rime ice, the S-A model was found to predict accurately until near the stall angle. For glaze ice, the CFD predictions were much less satisfactory for all turbulence models and codes investigated because of the large separated region produced by the horns. For unsteady RANS, WIND and FLUENT did not provide better results. PowerFLOW, based on the Lattice Boltzmann method, gave excellent results for the lift coefficient at and near stall for the rime ice, where the flow is inherently unsteady.

  13. Improving Marital Prediction: A Model and a Pilot Study.

    Science.gov (United States)

    Dean, Dwight G.; Lucas, Wayne L.

    A model for the prediction of marital adjustment is proposed which presents selected social background factors (e.g., education) and interactive factors (e.g., Bienvenu's Communication scale, Hurvitz' Role Inventory, Dean's Emotional Maturity and Commitment scales, Rosenberg's Self-Esteem scale) in order to account for as much of the variance in…

  14. Case Studies of Predictive Analysis Applications in Law Enforcement

    Science.gov (United States)

    2015-12-01

    Management, December 2, 2006, 12– 15; Whiting, “Predict the Future-Or Try, Anyway.” 59 Hsinchun Chen, Roger HL Chiang, and Veda C. Storey, “Business...Roger HL Chiang, and Veda C. Storey. “Business Intelligence and Analytics: From Big Data to Big Impact.” MIS Quarterly 36, no. 4 (2012). http

  15. Safety of Workers in Indian Mines: Study, Analysis, and Prediction

    Directory of Open Access Journals (Sweden)

    Shikha Verma

    2017-09-01

    Conclusion: Unsafe acts of the worker are the most critical human factors identified to be controlled on priority basis. A significant association of factors (namely age, experience of the worker, and shift of work with unsafe acts performed by the operator is identified based upon which the FRA-based accident prediction model is proposed.

  16. Scientific reporting is suboptimal for aspects that characterize genetic risk prediction studies: a review of published articles based on the Genetic RIsk Prediction Studies statement.

    Science.gov (United States)

    Iglesias, Adriana I; Mihaescu, Raluca; Ioannidis, John P A; Khoury, Muin J; Little, Julian; van Duijn, Cornelia M; Janssens, A Cecile J W

    2014-05-01

    Our main objective was to raise awareness of the areas that need improvements in the reporting of genetic risk prediction articles for future publications, based on the Genetic RIsk Prediction Studies (GRIPS) statement. We evaluated studies that developed or validated a prediction model based on multiple DNA variants, using empirical data, and were published in 2010. A data extraction form based on the 25 items of the GRIPS statement was created and piloted. Forty-two studies met our inclusion criteria. Overall, more than half of the evaluated items (34 of 62) were reported in at least 85% of included articles. Seventy-seven percentage of the articles were identified as genetic risk prediction studies through title assessment, but only 31% used the keywords recommended by GRIPS in the title or abstract. Seventy-four percentage mentioned which allele was the risk variant. Overall, only 10% of the articles reported all essential items needed to perform external validation of the risk model. Completeness of reporting in genetic risk prediction studies is adequate for general elements of study design but is suboptimal for several aspects that characterize genetic risk prediction studies such as description of the model construction. Improvements in the transparency of reporting of these aspects would facilitate the identification, replication, and application of genetic risk prediction models. Copyright © 2014 Elsevier Inc. All rights reserved.

  17. Negative correlation learning for customer churn prediction: a comparison study.

    Science.gov (United States)

    Rodan, Ali; Fayyoumi, Ayham; Faris, Hossam; Alsakran, Jamal; Al-Kadi, Omar

    2015-01-01

    Recently, telecommunication companies have been paying more attention toward the problem of identification of customer churn behavior. In business, it is well known for service providers that attracting new customers is much more expensive than retaining existing ones. Therefore, adopting accurate models that are able to predict customer churn can effectively help in customer retention campaigns and maximizing the profit. In this paper we will utilize an ensemble of Multilayer perceptrons (MLP) whose training is obtained using negative correlation learning (NCL) for predicting customer churn in a telecommunication company. Experiments results confirm that NCL based MLP ensemble can achieve better generalization performance (high churn rate) compared with ensemble of MLP without NCL (flat ensemble) and other common data mining techniques used for churn analysis.

  18. Negative Correlation Learning for Customer Churn Prediction: A Comparison Study

    OpenAIRE

    Rodan, Ali; Fayyoumi, Ayham; Faris, Hossam; Alsakran, Jamal; Al-Kadi, Omar

    2015-01-01

    Recently, telecommunication companies have been paying more attention toward the problem of identification of customer churn behavior. In business, it is well known for service providers that attracting new customers is much more expensive than retaining existing ones. Therefore, adopting accurate models that are able to predict customer churn can effectively help in customer retention campaigns and maximizing the profit. In this paper we will utilize an ensemble of Multilayer perceptrons ...

  19. Study of redundant Models in reliability prediction of HXMT's HES

    International Nuclear Information System (INIS)

    Wang Jinming; Liu Congzhan; Zhang Zhi; Ji Jianfeng

    2010-01-01

    Two redundant equipment structures of HXMT's HES are proposed firstly, the block backup and dual system cold-redundancy. Then prediction of the reliability is made by using parts count method. Research of comparison and analysis is also performed on the two proposals. A conclusion is drawn that a higher reliability and longer service life could be offered by taking a redundant equipment structure of block backup. (authors)

  20. Study on real-time elevator brake failure predictive system

    Science.gov (United States)

    Guo, Jun; Fan, Jinwei

    2013-10-01

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

  1. A risk prediction model for xerostomia: a retrospective cohort study.

    Science.gov (United States)

    Villa, Alessandro; Nordio, Francesco; Gohel, Anita

    2016-12-01

    We investigated the prevalence of xerostomia in dental patients and built a xerostomia risk prediction model by incorporating a wide range of risk factors. Socio-demographic data, past medical history, self-reported dry mouth and related symptoms were collected retrospectively from January 2010 to September 2013 for all new dental patients. A logistic regression framework was used to build a risk prediction model for xerostomia. External validation was performed using an independent data set to test the prediction power. A total of 12 682 patients were included in this analysis (54.3%, females). Xerostomia was reported by 12.2% of patients. The proportion of people reporting xerostomia was higher among those who were taking more medications (OR = 1.11, 95% CI = 1.08-1.13) or recreational drug users (OR = 1.4, 95% CI = 1.1-1.9). Rheumatic diseases (OR = 2.17, 95% CI = 1.88-2.51), psychiatric diseases (OR = 2.34, 95% CI = 2.05-2.68), eating disorders (OR = 2.28, 95% CI = 1.55-3.36) and radiotherapy (OR = 2.00, 95% CI = 1.43-2.80) were good predictors of xerostomia. For the test model performance, the ROC-AUC was 0.816 and in the external validation sample, the ROC-AUC was 0.799. The xerostomia risk prediction model had high accuracy and discriminated between high- and low-risk individuals. Clinicians could use this model to identify the classes of medications and systemic diseases associated with xerostomia. © 2015 John Wiley & Sons A/S and The Gerodontology Association. Published by John Wiley & Sons Ltd.

  2. Biomarker kinetics in the prediction of VAP diagnosis: results from the BioVAP study

    NARCIS (Netherlands)

    Póvoa, Pedro; Martin-Loeches, Ignacio; Ramirez, Paula; Bos, Lieuwe D.; Esperatti, Mariano; Silvestre, Joana; Gili, Gisela; Goma, Gema; Berlanga, Eugenio; Espasa, Mateu; Gonçalves, Elsa; Torres, Antoni; Artigas, Antonio

    2016-01-01

    Prediction of diagnosis of ventilator-associated pneumonia (VAP) remains difficult. Our aim was to assess the value of biomarker kinetics in VAP prediction. We performed a prospective, multicenter, observational study to evaluate predictive accuracy of biomarker kinetics, namely C-reactive protein

  3. Study on prediction model of irradiation embrittlement for reactor pressure vessel steel

    International Nuclear Information System (INIS)

    Wang Rongshan; Xu Chaoliang; Huang Ping; Liu Xiangbing; Ren Ai; Chen Jun; Li Chengliang

    2014-01-01

    The study on prediction model of irradiation embrittlement for reactor pres- sure vessel (RPV) steel is an important method for long term operation. According to the deep analysis of the previous prediction models developed worldwide, the drawbacks of these models were given and a new irradiation embrittlement prediction model PMIE-2012 was developed. A corresponding reliability assessment was carried out by irradiation surveillance data. The assessment results show that the PMIE-2012 have a high reliability and accuracy on irradiation embrittlement prediction. (authors)

  4. Predicting AKI in emergency admissions: an external validation study of the acute kidney injury prediction score (APS).

    Science.gov (United States)

    Hodgson, L E; Dimitrov, B D; Roderick, P J; Venn, R; Forni, L G

    2017-03-08

    Hospital-acquired acute kidney injury (HA-AKI) is associated with a high risk of mortality. Prediction models or rules may identify those most at risk of HA-AKI. This study externally validated one of the few clinical prediction rules (CPRs) derived in a general medicine cohort using clinical information and data from an acute hospitals electronic system on admission: the acute kidney injury prediction score (APS). External validation in a single UK non-specialist acute hospital (2013-2015, 12 554 episodes); four cohorts: adult medical and general surgical populations, with and without a known preadmission baseline serum creatinine (SCr). Performance assessed by discrimination using area under the receiver operating characteristic curves (AUCROC) and calibration. HA-AKI incidence within 7 days (kidney disease: improving global outcomes (KDIGO) change in SCr) was 8.1% (n=409) of medical patients with known baseline SCr, 6.6% (n=141) in those without a baseline, 4.9% (n=204) in surgical patients with baseline and 4% (n=49) in those without. Across the four cohorts AUCROC were: medical with known baseline 0.65 (95% CIs 0.62 to 0.67) and no baseline 0.71 (0.67 to 0.75), surgical with baseline 0.66 (0.62 to 0.70) and no baseline 0.68 (0.58 to 0.75). For calibration, in medicine and surgical cohorts with baseline SCr, Hosmer-Lemeshow p values were non-significant, suggesting acceptable calibration. In the medical cohort, at a cut-off of five points on the APS to predict HA-AKI, positive predictive value was 16% (13-18%) and negative predictive value 94% (93-94%). Of medical patients with HA-AKI, those with an APS ≥5 had a significantly increased risk of death (28% vs 18%, OR 1.8 (95% CI 1.1 to 2.9), p=0.015). On external validation the APS on admission shows moderate discrimination and acceptable calibration to predict HA-AKI and may be useful as a severity marker when HA-AKI occurs. Harnessing linked data from primary care may be one way to achieve more accurate

  5. The Prediction of Training Proficiency in Firefighters: A Study of Predictive Validity in Spain

    Directory of Open Access Journals (Sweden)

    Alfredo Berges

    2018-02-01

    Full Text Available The present study provides results of criterion validity in the selection of firefighters in Spain. The predictors were cognitive skills, job knowledge, and physical aptitudes, and the criterion was training proficiency. The process involves 639 candidates, but only 44 complete successfully the selection process. Our results support previous evidence showing that general cognitive ability is the best predictor of training proficiency, with an operational validity of .57. With respect to the other predictors, job knowledge presented an operational validity of .55 and physical tests of .49. In addition, multiple regression analysis showed that cognitive aptitude explains 33% of the variance, but when physical aptitudes are included the explained variance increases to 50%. If we also add job knowledge, explained variance increases to 55%. Our study offers recent results of criterion validity in a barely investigated job, gathered in a country other than the one where prior research had been carried out.

  6. Barium depletion study on impregnated cathodes and lifetime prediction

    International Nuclear Information System (INIS)

    Roquais, J.M.; Poret, F.; Doze, R. le; Ricaud, J.L.; Monterrin, A.; Steinbrunn, A.

    2003-01-01

    In the thermionic cathodes used in cathode ray-tubes (CRTs), barium is the key element for the electronic emission. In the case of the dispenser cathodes made of a porous tungsten pellet impregnated with Ba, Ca aluminates, the evaporation of Ba determines the cathode lifetime with respect to emission performance in the CRT. The Ba evaporation results in progressive depletion of the impregnating material inside the pellet. In the present work, the Ba depletion with time has been extensively characterized over a large range of cathode temperature. Calculations using the depletion data allowed modeling of the depletion as a function of key parameters. The link between measured depletion and emission in tubes has been established, from which an end-of-life criterion was deduced. Taking modeling into account, predicting accelerated life-tests were performed using high-density maximum emission current (MIK)

  7. Approaches to studying predict academic performance in undergraduate occupational therapy students: a cross-cultural study.

    Science.gov (United States)

    Bonsaksen, Tore; Brown, Ted; Lim, Hua Beng; Fong, Kenneth

    2017-05-02

    Learning outcomes may be a result of several factors including the learning environment, students' predispositions, study efforts, cultural factors and approaches towards studying. This study examined the influence of demographic variables, education-related factors, and approaches to studying on occupational therapy students' Grade Point Average (GPA). Undergraduate occupational therapy students (n = 712) from four countries completed the Approaches and Study Skills Inventory for Students (ASSIST). Demographic background, education-related factors, and ASSIST scores were used in a hierarchical linear regression analysis to predict the students' GPA. Being older, female and more time engaged in self-study activities were associated with higher GPA among the students. In addition, five ASSIST subscales predicted higher GPA: higher scores on 'seeking meaning', 'achieving', and 'lack of purpose', and lower scores on 'time management' and 'fear of failure'. The full model accounted for 9.6% of the variance related to the occupational therapy students' GPA. To improve academic performance among occupational therapy students, it appears important to increase their personal search for meaning and motivation for achievement, and to reduce their fear of failure. The results should be interpreted with caution due to small effect sizes and a modest amount of variance explained by the regression model, and further research on predictors of academic performance is required.

  8. Incorporating information on predicted solvent accessibility to the co-evolution-based study of protein interactions.

    Science.gov (United States)

    Ochoa, David; García-Gutiérrez, Ponciano; Juan, David; Valencia, Alfonso; Pazos, Florencio

    2013-01-27

    A widespread family of methods for studying and predicting protein interactions using sequence information is based on co-evolution, quantified as similarity of phylogenetic trees. Part of the co-evolution observed between interacting proteins could be due to co-adaptation caused by inter-protein contacts. In this case, the co-evolution is expected to be more evident when evaluated on the surface of the proteins or the internal layers close to it. In this work we study the effect of incorporating information on predicted solvent accessibility to three methods for predicting protein interactions based on similarity of phylogenetic trees. We evaluate the performance of these methods in predicting different types of protein associations when trees based on positions with different characteristics of predicted accessibility are used as input. We found that predicted accessibility improves the results of two recent versions of the mirrortree methodology in predicting direct binary physical interactions, while it neither improves these methods, nor the original mirrortree method, in predicting other types of interactions. That improvement comes at no cost in terms of applicability since accessibility can be predicted for any sequence. We also found that predictions of protein-protein interactions are improved when multiple sequence alignments with a richer representation of sequences (including paralogs) are incorporated in the accessibility prediction.

  9. Predicting implementation from organizational readiness for change: a study protocol

    Directory of Open Access Journals (Sweden)

    Kelly P Adam

    2011-07-01

    Full Text Available Abstract Background There is widespread interest in measuring organizational readiness to implement evidence-based practices in clinical care. However, there are a number of challenges to validating organizational measures, including inferential bias arising from the halo effect and method bias - two threats to validity that, while well-documented by organizational scholars, are often ignored in health services research. We describe a protocol to comprehensively assess the psychometric properties of a previously developed survey, the Organizational Readiness to Change Assessment. Objectives Our objective is to conduct a comprehensive assessment of the psychometric properties of the Organizational Readiness to Change Assessment incorporating methods specifically to address threats from halo effect and method bias. Methods and Design We will conduct three sets of analyses using longitudinal, secondary data from four partner projects, each testing interventions to improve the implementation of an evidence-based clinical practice. Partner projects field the Organizational Readiness to Change Assessment at baseline (n = 208 respondents; 53 facilities, and prospectively assesses the degree to which the evidence-based practice is implemented. We will conduct predictive and concurrent validities using hierarchical linear modeling and multivariate regression, respectively. For predictive validity, the outcome is the change from baseline to follow-up in the use of the evidence-based practice. We will use intra-class correlations derived from hierarchical linear models to assess inter-rater reliability. Two partner projects will also field measures of job satisfaction for convergent and discriminant validity analyses, and will field Organizational Readiness to Change Assessment measures at follow-up for concurrent validity (n = 158 respondents; 33 facilities. Convergent and discriminant validities will test associations between organizational readiness and

  10. Study on Noise Prediction Model and Control Schemes for Substation

    Science.gov (United States)

    Gao, Yang; Liu, Songtao

    2014-01-01

    With the government's emphasis on environmental issues of power transmission and transformation project, noise pollution has become a prominent problem now. The noise from the working transformer, reactor, and other electrical equipment in the substation will bring negative effect to the ambient environment. This paper focuses on using acoustic software for the simulation and calculation method to control substation noise. According to the characteristics of the substation noise and the techniques of noise reduction, a substation's acoustic field model was established with the SoundPLAN software to predict the scope of substation noise. On this basis, 4 reasonable noise control schemes were advanced to provide some helpful references for noise control during the new substation's design and construction process. And the feasibility and application effect of these control schemes can be verified by using the method of simulation modeling. The simulation results show that the substation always has the problem of excessive noise at boundary under the conventional measures. The excess noise can be efficiently reduced by taking the corresponding noise reduction methods. PMID:24672356

  11. Importance of metabolism in pharmacological studies: possible in vitro predictability

    International Nuclear Information System (INIS)

    Delaforge, M.

    1998-01-01

    Metabolic transformation of drug leads to the formation of a large number of secondary compounds. These metabolites may (a) participate to the elimination of the patent drug, (b) have similar or different therapeutic effects compared to the parent drug (c) exert toxic effects. Cytochromes P450 are the main enzymes involved in the biotransformation of exogenous drugs, leading to oxidized, reduced or peroxidized metabolites. Different isozymes of P450 are present in already all the organs and differ by their affinity for substrate families. P450 3A is the most abundant P450 protein in the adult human liver and is able to transform hundreds of substrates into either drugs or endogenous compounds such as testosterone. Its catalytic activities are regulated either by induction or by inhibition. Attempts to predict metabolic transformation of a given drug are based on the amount of P450 expressed in heterologous systems, induction, and inhibition experiments and by comparison to classical P450 substrates. Erythromycin metabolism and its P450 effects are used to illustrate the complexity and the consequences of metabolic transformation of a given drug

  12. Rumination, Age, and Years of Experience: A Predictive Study of Burnout

    Science.gov (United States)

    McDuffy, Moriel S.

    2016-01-01

    This study used a non-experimental design to examine whether job satisfaction, rumination, age and years of experience predict burnout among human service workers serving high-risk populations. The study also used a stepwise regression to assess whether job satisfaction, rumination, age, or years of experience predict burnout equally. Burnout was…

  13. Deficiencies and possibilities for long-lead coupled climate prediction of the Western North Pacific-East Asian summer monsoon

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Sun-Seon; Ha, Kyung-Ja [Pusan National University, Division of Earth Environmental System, Busan (Korea, Republic of); Lee, June-Yi; Wang, Bin [University of Hawaii, Department of Meteorology and International Pacific Research Center, Honolulu, HI (United States); Schemm, Jae Kyung E. [Climate Prediction Center/NCEP, Camp Springs, MD (United States)

    2011-03-15

    Long-lead prediction of waxing and waning of the Western North Pacific (WNP)-East Asian (EA) summer monsoon (WNP-EASM) precipitation is a major challenge in seasonal time-scale climate prediction. In this study, deficiencies and potential for predicting the WNP-EASM precipitation and circulation one or two seasons ahead were examined using retrospective forecast data for the 26-year period of 1981-2006 from two operational couple models which are the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) and the Bureau of Meteorology Research Center (BMRC) Predictive Ocean-Atmosphere Model for Australia (POAMA). While both coupled models have difficulty in predicting summer mean precipitation anomalies over the region of interest, even for a 0-month lead forecast, they are capable of predicting zonal wind anomalies at 850 hPa several months ahead and, consequently, satisfactorily predict summer monsoon circulation indices for the EA region (EASMI) and for the WNP region (WNPSMI). It should be noted that the two models' multi-model ensemble (MME) reaches 0.40 of the correlation skill for the EASMI with a January initial condition and 0.75 for the WNPSMI with a February initial condition. Further analysis indicates that prediction reliability of the EASMI is related not only to the preceding El Nino and Southern Oscillation (ENSO) but also to simultaneous local SST variability. On other hand, better prediction of the WNPSMI is accompanied by a more realistic simulation of lead-lag relationship between the index and ENSO. It should also be noted that current coupled models have difficulty in capturing the interannual variability component of the WNP-EASM system which is not correlated with typical ENSO variability. To improve the long-lead seasonal prediction of the WNP-EASM precipitation, a statistical postprocessing was developed based on the multiple linear regression method. The method utilizes the MME prediction of the EASMI and

  14. Predictive Accuracy of a Cardiovascular Disease Risk Prediction Model in Rural South India – A Community Based Retrospective Cohort Study

    Directory of Open Access Journals (Sweden)

    Farah N Fathima

    2015-03-01

    Full Text Available Background: Identification of individuals at risk of developing cardiovascular diseases by risk stratification is the first step in primary prevention. Aims & Objectives: To assess the five year risk of developing a cardiovascular event from retrospective data and to assess the predictive accuracy of the non laboratory based National Health and Nutrition Examination Survey (NHANES risk prediction model among individuals in a rural South Indian population. Materials & Methods: A community based retrospective cohort study was conducted in three villages where risk stratification was done for all eligible adults aged between 35-74 years at the time of initial assessment using the NHANES risk prediction charts. Household visits were made after a period of five years by trained doctors to determine cardiovascular outcomes. Results: 521 people fulfilled the eligibility criteria of whom 486 (93.3% could be traced after five years. 56.8% were in low risk, 36.6% were in moderate risk and 6.6% were in high risk categories. 29 persons (5.97% had had cardiovascular events over the last five years of which 24 events (82.7% were nonfatal and five (17.25% were fatal. The mean age of the people who developed cardiovascular events was 57.24 ± 9.09 years. The odds ratios for the three levels of risk showed a linear trend with the odds ratios for the moderate risk and high risk category being 1.35 and 1.94 respectively with the low risk category as baseline. Conclusion: The non laboratory based NHANES charts did not accurately predict the occurrence of cardiovascular events in any of the risk categories.

  15. Perturbation method of studying the EI Niño oscillation with two parameters by using the delay sea-air oscillator model

    International Nuclear Information System (INIS)

    Du Zeng-Ji; Lin Wan-Tao; Mo Jia-Qi

    2012-01-01

    The EI Niño-southern oscillation (ENSO) is an interannual phenomenon involved in tropical Pacific ocean-atmosphere interactions. In this paper, we develop an asymptotic method of solving the nonlinear equation using the ENSO model. Based on a class of the oscillator of the ENSO model, a approximate solution of the corresponding problem is studied employing the perturbation method

  16. Childhood Psychopathology Predicts Adolescence-Onset Offending: A Longitudinal Study

    Science.gov (United States)

    Buck, Nicole; Verhulst, Frank; van Marle, Hjalmar; van der Ende, Jan

    2013-01-01

    Moffitt, Caspi, Harrington, and Milne (2002) found in a follow-up study that many of the supposedly adolescence-limited offenders had committed offenses past adolescence. This finding raises the question of whether adulthood starts later or whether there are two distinct delinquency types, adolescence limited and adolescence onset, each with its…

  17. Soil water balance scenario studies using predicted soil hydraulic parameters

    NARCIS (Netherlands)

    Nemes, A.; Wösten, J.H.M.; Bouma, J.; Várallyay, G.

    2006-01-01

    Pedotransfer functions (PTFs) have become a topic drawing increasing interest within the field of soil and environmental research because they can provide important soil physical data at relatively low cost. Few studies, however, explore which contributions PTFs can make to land-use planning, in

  18. A prediction study of a spark ignition supercharged hydrogen engine

    International Nuclear Information System (INIS)

    Al-Baghdadi, Maher A.R. Sadiq.; Al-Janabi, Haroun A.K. Shahad

    2003-01-01

    Hydrogen is found to be a suitable alternative fuel for spark ignition engines with certain drawbacks, such as high NO x emission and small power output. However, supercharging may solve such problems. In this study, the effects of equivalence ratio, compression ratio and inlet pressure on the performance and NO x emission of a four stroke supercharged hydrogen engine have been analyzed using a specially developed computer program. The results are verified and compared with experimental data obtained from tests on a Ricardo E6/US engine. A chart specifying the safe operation zone of the hydrogen engine has been produced. The safe operation zone means no pre-ignition, acceptable NO x emission, high engine efficiency and lower specific fuel consumption in comparison with the gasoline engine. The study also shows that supercharging is a more effective method to increase the output of a hydrogen engine rather than increasing the compression ratio of the engine at the knock limited equivalence ratio

  19. Obesity predicts primary health care visits: a cohort study.

    Science.gov (United States)

    Twells, Laurie K; Bridger, Tracey; Knight, John C; Alaghehbandan, Reza; Barrett, Brendan

    2012-02-01

    The objective of this study was to explore the relationship between body mass index (BMI), its association with chronic disease, and its impact on health services utilization in the province of Newfoundland and Labrador, Canada, from 1998 to 2002. A data linkage study was conducted involving a provincial health survey linked to 2 health care use administrative databases. The study population comprised 2345 adults between the ages of 20 and 64 years. Self-reported height and weight measures and other covariates, including chronic diseases, were obtained from a provincial survey. BMI categories include: normal weight (BMI 18.5-24.9), overweight (BMI 25-29.9), obese class I (BMI 30-34.9), obese class II (BMI ≥ 35), and obese class III (BMI ≥ 40). Survey responses were linked with objective physician and hospital health services utilization over a 5-year period. Weight classifications in the study sample were as follows: 37% normal, 39% overweight, 17% obese, and 6% morbidly obese. The obese and morbidly obese were more likely to report having serious chronic conditions after adjusting for age and sex. Only the morbidly obese group (BMI ≥ 35 kg/m(2)) had a significantly higher number of visits to a general practitioner (GP) over a 5-year period compared to the normal weight group (median 22.0 vs. 17.0, Pchronic conditions and other relevant covariates, being morbidly obese remained a significant predictor of GP visits (Pobesity is placing a burden at the primary health care level. More resources are needed in order to support GPs in their efforts to manage and treat obese adults who have associated comorbidities.

  20. Optimized feature subsets for epileptic seizure prediction studies.

    Science.gov (United States)

    Direito, Bruno; Ventura, Francisco; Teixeira, César; Dourado, António

    2011-01-01

    The reduction of the number of EEG features to give as inputs to epilepsy seizure predictors is a needed step towards the development of a transportable device for real-time warning. This paper presents a comparative study of three feature selection methods, based on Support Vector Machines. Minimum-Redundancy Maximum-Relevance, Recursive Feature Elimination, Genetic Algorithms, show that, for three patients of the European Database on Epilepsy, the most important univariate features are related to spectral information and statistical moments.

  1. A Satellite Mortality Study to Support Space Systems Lifetime Prediction

    Science.gov (United States)

    Fox, George; Salazar, Ronald; Habib-Agahi, Hamid; Dubos, Gregory

    2013-01-01

    Estimating the operational lifetime of satellites and spacecraft is a complex process. Operational lifetime can differ from mission design lifetime for a variety of reasons. Unexpected mortality can occur due to human errors in design and fabrication, to human errors in launch and operations, to random anomalies of hardware and software or even satellite function degradation or technology change, leading to unrealized economic or mission return. This study focuses on data collection of public information using, for the first time, a large, publically available dataset, and preliminary analysis of satellite lifetimes, both operational lifetime and design lifetime. The objective of this study is the illustration of the relationship of design life to actual lifetime for some representative classes of satellites and spacecraft. First, a Weibull and Exponential lifetime analysis comparison is performed on the ratio of mission operating lifetime to design life, accounting for terminated and ongoing missions. Next a Kaplan-Meier survivor function, standard practice for clinical trials analysis, is estimated from operating lifetime. Bootstrap resampling is used to provide uncertainty estimates of selected survival probabilities. This study highlights the need for more detailed databases and engineering reliability models of satellite lifetime that include satellite systems and subsystems, operations procedures and environmental characteristics to support the design of complex, multi-generation, long-lived space systems in Earth orbit.

  2. Mental distress predicts divorce over 16 years: the HUNT study.

    Science.gov (United States)

    Idstad, Mariann; Torvik, Fartein Ask; Borren, Ingrid; Rognmo, Kamilla; Røysamb, Espen; Tambs, Kristian

    2015-04-01

    The association between mental distress and divorce is well established in the literature. Explanations are commonly classified within two different frameworks; social selection (mentally distressed people are selected out of marriage) and social causation (divorce causes mental distress). Despite a relatively large body of literature on this subject, selection effects are somewhat less studied, and research based on data from both spouses is scarce. The purpose of the present study is to investigate selection effects both at the individual level and the couple level. The current study is based on couple-level data from a Norwegian representative sample including 20,233 couples. Long-term selection effects were tested for by means of Cox proportional hazard models, using mental distress in both partners at baseline as predictors of divorce the next 16 years. Three identical sets of analyses were run. The first included the total sample, whereas the second and third excluded couples who divorced within the first 4 or 8 years after baseline, respectively. An interaction term between mental distress in husband and in wife was specified and tested. Hazard of divorce was significantly higher in couples with one mentally distressed partner than in couples with no mental distress in all analyses. There was also a significant interaction effect showing that the hazard of divorce for couples with two mentally distressed partners was higher than for couples with one mentally distressed partner, but lower than what could be expected from the combined main effects of two mentally distressed partners. Our results suggest that mentally distressed individuals are selected out of marriage. We also found support for a couple-level effect in which spouse similarity in mental distress to a certain degree seems to protect against divorce.

  3. Predictive Software Measures based on Z Specifications - A Case Study

    Directory of Open Access Journals (Sweden)

    Andreas Bollin

    2012-07-01

    Full Text Available Estimating the effort and quality of a system is a critical step at the beginning of every software project. It is necessary to have reliable ways of calculating these measures, and, it is even better when the calculation can be done as early as possible in the development life-cycle. Having this in mind, metrics for formal specifications are examined with a view to correlations to complexity and quality-based code measures. A case study, based on a Z specification and its implementation in ADA, analyzes the practicability of these metrics as predictors.

  4. The potential of large studies for building genetic risk prediction models

    Science.gov (United States)

    NCI scientists have developed a new paradigm to assess hereditary risk prediction in common diseases, such as prostate cancer. This genetic risk prediction concept is based on polygenic analysis—the study of a group of common DNA sequences, known as singl

  5. Study on China’s Earthquake Prediction by Mathematical Analysis and its Application in Catastrophe Insurance

    Science.gov (United States)

    Jianjun, X.; Bingjie, Y.; Rongji, W.

    2018-03-01

    The purpose of this paper was to improve catastrophe insurance level. Firstly, earthquake predictions were carried out using mathematical analysis method. Secondly, the foreign catastrophe insurances’ policies and models were compared. Thirdly, the suggestions on catastrophe insurances to China were discussed. The further study should be paid more attention on the earthquake prediction by introducing big data.

  6. A prediction study of a spark ignition supercharged hydrogen engine

    Energy Technology Data Exchange (ETDEWEB)

    Al-Baghdadi, M.A.R.S.; Al-Janabi, H.A.K.S. [University of Babylon (Iraq). Dept. of Mechanical Engineering

    2003-12-01

    Hydrogen is found to be a suitable alternative fuel for spark ignition engines with certain drawbacks, such as high NO{sub x} emission and small power output. However, supercharging may solve such problems. In this study, the effects of equivalence ratio, compression ratio and inlet pressure on the performance and NO{sub x} emission of a four stroke supercharged hydrogen engine have been analyzed using a specially developed computer program. The results are verified and compared with experimental data obtained from tests on a Ricardo E6/US engine. A chart specifying the safe operation zone of the hydrogen engine has been produced. The safe operation zone means no pre-ignition, acceptable NO{sub x} emission, high engine efficiency and lower specific fuel consumption in comparison with the gasoline engine. The study also shows that supercharging is a more effective method to increase the output of a hydrogen engine rather than increasing the compression ratio of the engine at the knock limited equivalence ratio. (author)

  7. Compartmental analysis to predict biodistribution in radiopharmaceutical design studies

    Energy Technology Data Exchange (ETDEWEB)

    Lima, Marina F.; Pujatti, Priscilla B.; Araujo, Elaine B.; Mesquita, Carlos H. [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)], e-mail: mflima@ipen.br

    2009-07-01

    The use of compartmental analysis allows the mathematical separation of tissues and organs to determinate the concentration of activity in each fraction of interest. Although the radiochemical purity must observe Pharmacopoeia specification (values upper 95%), very lower contains of free radionuclides could contribute significantly as dose in the neighborhood organs and make tumor up take studies not viable in case of radiopharmaceutical on the basis of labeled peptides. Animal studies with a product of Lutetium-177 labeled Bombesin derivative ({sup 177}Lu-BBNP) developed in IPEN-CNEN/SP and free Lutetium-177 developed in CNEA/EZEIZA was used to show how subtract free {sup 177}Lu contribution over {sup 177}Lu-BBNP to estimate the radiopharmaceutical potential as diagnosis or therapy agent. The first approach of the studies included the knowledge of chemical kinetics and mimetism of the Lutetium and the possible targets of the diagnosis/therapy to choose the possible models to apply over the sampling standard methods used in experimental works. A model with only one physical compartment (whole body) and one chemical compartment ({sup 177}Lu-BBNP) generated with the compartmental analysis protocol ANACOMP showed high differences between experimental and theoretical values over 2.5 hours, in spite of the concentration of activity had been in a good statistics rang of measurement. The values used in this work were residence time from three different kinds of study with free {sup 177}Lu: whole body, average excretion and maximum excretion as a chemical compartment. Activity concentration values as time function in measurements of total whole body and activity measurement in samples of blood with projection to total circulating blood volume with {sup 177}Lu-BBNP. Considering the two sources of data in the same modeling a better consistence was obtained. The next step was the statistic treatment of biodistribution and dosimetry in mice (Balb C) considering three chemical

  8. A Predictive Study of Student Satisfaction in Online Education Programs

    Directory of Open Access Journals (Sweden)

    Yu-Chun Kuo

    2013-03-01

    Full Text Available This paper is intended to investigate the degree to which interaction and other predictors contribute to student satisfaction in online learning settings. This was a preliminary study towards a dissertation work which involved the establishment of interaction and satisfaction scales through a content validity survey. Regression analysis was performed to determine the contribution of predictor variables to student satisfaction. The effects of student background variables on predictors were explored. The results showed that learner-instructor interaction, learner-content interaction, and Internet self-efficacy were good predictors of student satisfaction while interactions among students and self-regulated learning did not contribute to student satisfaction. Learner-content interaction explained the largest unique variance in student satisfaction. Additionally, gender, class level, and time spent online per week seemed to have influence on learner-learner interaction, Internet self-efficacy, and self-regulation.

  9. PREDICTING THE INTENTION TO USE INTERNET – A COMPARATIVE STUDY

    Directory of Open Access Journals (Sweden)

    Slaven Brumec

    2006-06-01

    Full Text Available This article focuses on an application of the Triandis Model in researching Internet usage and the intention to use Internet. Unlike other TAM-based studies undertaken to date, the Triandis Model offers a sociological account of interaction between the various factors, particularly attitude, intention, and behavior. The technique of Structural Equation Modeling was used to assess the impact those factors have on intention to use the Internet in accordance with the relationships posited by the Triandis Model. The survey was administered to Croatian undergraduate students at and employed individuals. The survey results are compared to the results of a similar survey that was carried out by two universities in Hong Kong.

  10. A study on effects of cash flow patterns and auditors’ opinions in predicting financial distress

    Directory of Open Access Journals (Sweden)

    Fatemeh Namvar

    2013-07-01

    Full Text Available Bankruptcy has been one of the most important issues among investors in stock market and there are literally different techniques for predicting bankruptcy. In this paper, we study on effects of cash flow patterns and auditors’ opinions in predicting financial distress on some 80 selected firms traded on Tehran Stock Exchange over the period 2005-2011. In this study, the combination of cash flow patterns represent firm’s resource allocations and operational capabilities interacted with their strategy choices. In additions, predictions about each individual cash flow components, operational, investment, financial, are derived from economic theory, which forms a basis for the life proxy. We use cash flow patterns in the decline stage and compare the results with auditors’ opinions. The results indicate that cash flow patterns could predict financial distress companies in Iran. In addition, the effective cash flow patterns in predicting financial distress is more than auditors’ feedbacks.

  11. Machine learning methods to predict child posttraumatic stress: a proof of concept study.

    Science.gov (United States)

    Saxe, Glenn N; Ma, Sisi; Ren, Jiwen; Aliferis, Constantin

    2017-07-10

    The care of traumatized children would benefit significantly from accurate predictive models for Posttraumatic Stress Disorder (PTSD), using information available around the time of trauma. Machine Learning (ML) computational methods have yielded strong results in recent applications across many diseases and data types, yet they have not been previously applied to childhood PTSD. Since these methods have not been applied to this complex and debilitating disorder, there is a great deal that remains to be learned about their application. The first step is to prove the concept: Can ML methods - as applied in other fields - produce predictive classification models for childhood PTSD? Additionally, we seek to determine if specific variables can be identified - from the aforementioned predictive classification models - with putative causal relations to PTSD. ML predictive classification methods - with causal discovery feature selection - were applied to a data set of 163 children hospitalized with an injury and PTSD was determined three months after hospital discharge. At the time of hospitalization, 105 risk factor variables were collected spanning a range of biopsychosocial domains. Seven percent of subjects had a high level of PTSD symptoms. A predictive classification model was discovered with significant predictive accuracy. A predictive model constructed based on subsets of potentially causally relevant features achieves similar predictivity compared to the best predictive model constructed with all variables. Causal Discovery feature selection methods identified 58 variables of which 10 were identified as most stable. In this first proof-of-concept application of ML methods to predict childhood Posttraumatic Stress we were able to determine both predictive classification models for childhood PTSD and identify several causal variables. This set of techniques has great potential for enhancing the methodological toolkit in the field and future studies should seek to

  12. EPILAB: a software package for studies on the prediction of epileptic seizures.

    Science.gov (United States)

    Teixeira, C A; Direito, B; Feldwisch-Drentrup, H; Valderrama, M; Costa, R P; Alvarado-Rojas, C; Nikolopoulos, S; Le Van Quyen, M; Timmer, J; Schelter, B; Dourado, A

    2011-09-15

    A Matlab®-based software package, EPILAB, was developed for supporting researchers in performing studies on the prediction of epileptic seizures. It provides an intuitive and convenient graphical user interface. Fundamental concepts that are crucial for epileptic seizure prediction studies were implemented. This includes, for example, the development and statistical validation of prediction methodologies in long-term continuous recordings. Seizure prediction is usually based on electroencephalography (EEG) and electrocardiography (ECG) signals. EPILAB is able to process both EEG and ECG data stored in different formats. More than 35 time and frequency domain measures (features) can be extracted based on univariate and multivariate data analysis. These features can be post-processed and used for prediction purposes. The predictions may be conducted based on optimized thresholds or by applying classifications methods such as artificial neural networks, cellular neuronal networks, and support vector machines. EPILAB proved to be an efficient tool for seizure prediction, and aims to be a way to communicate, evaluate, and compare results and data among the seizure prediction community. Copyright © 2011 Elsevier B.V. All rights reserved.

  13. Predictive value of noninvasive measures of atherosclerosis for incident myocardial infarction - The Rotterdam study

    NARCIS (Netherlands)

    van der Meer, IM; Bots, ML; Hofman, A; del Sol, AI; van der Kuip, DAM; Witteman, JCM

    2004-01-01

    Background - Several noninvasive methods are available to investigate the severity of extracoronary atherosclerotic disease. No population- based study has yet examined whether differences exist between these measures with regard to their predictive value for myocardial infarction (MI) or whether a

  14. Development and validation of outcome prediction models for aneurysmal subarachnoid haemorrhage : The SAHIT multinational cohort study

    NARCIS (Netherlands)

    Jaja, Blessing N R; Saposnik, Gustavo; Lingsma, Hester F.; Macdonald, Erin; Thorpe, Kevin E.; Mamdani, Muhammed; Steyerberg, Ewout W.; Molyneux, Andrew; Manoel, Airton Leonardo De Oliveira; Schatlo, Bawarjan; Hanggi, Daniel; Hasan, David M.; Wong, George K C; Etminan, Nima; Fukuda, Hitoshi; Torner, James C.; Schaller, Karl L.; Suarez, Jose I.; Stienen, Martin N.; Vergouwen, Mervyn D.I.; Rinkel, Gabriel J.E.; Spears, Julian; Cusimano, Michael D.; Todd, Michael; Le Roux, Peter; Kirkpatrick, Peter J.; Pickard, John; Van Den Bergh, Walter M.; Murray, Gordon D; Johnston, S. Claiborne; Yamagata, Sen; Mayer, Stephan A.; Schweizer, Tom A.; Macdonald, R. Loch

    2018-01-01

    Objective To develop and validate a set of practical prediction tools that reliably estimate the outcome of subarachnoid haemorrhage from ruptured intracranial aneurysms (SAH). Design Cohort study with logistic regression analysis to combine predictors and treatment modality. Setting Subarachnoid

  15. Do daily fluctuations in inhibitory control predict alcohol consumption? : An ecological momentary assessment study

    NARCIS (Netherlands)

    Jones, Andrew; Tiplady, Brian; Houben, Katrijn; Nederkoorn, Chantal; Field, Matt

    RATIONALE: Deficient inhibitory control is predictive of increased alcohol consumption in the laboratory; however, little is known about this relationship in naturalistic, real-world settings. OBJECTIVES: In the present study, we implemented ecological momentary assessment methods to investigate the

  16. A predictive coding account of bistable perception - a model-based fMRI study.

    Directory of Open Access Journals (Sweden)

    Veith Weilnhammer

    2017-05-01

    Full Text Available In bistable vision, subjective perception wavers between two interpretations of a constant ambiguous stimulus. This dissociation between conscious perception and sensory stimulation has motivated various empirical studies on the neural correlates of bistable perception, but the neurocomputational mechanism behind endogenous perceptual transitions has remained elusive. Here, we recurred to a generic Bayesian framework of predictive coding and devised a model that casts endogenous perceptual transitions as a consequence of prediction errors emerging from residual evidence for the suppressed percept. Data simulations revealed close similarities between the model's predictions and key temporal characteristics of perceptual bistability, indicating that the model was able to reproduce bistable perception. Fitting the predictive coding model to behavioural data from an fMRI-experiment on bistable perception, we found a correlation across participants between the model parameter encoding perceptual stabilization and the behaviourally measured frequency of perceptual transitions, corroborating that the model successfully accounted for participants' perception. Formal model comparison with established models of bistable perception based on mutual inhibition and adaptation, noise or a combination of adaptation and noise was used for the validation of the predictive coding model against the established models. Most importantly, model-based analyses of the fMRI data revealed that prediction error time-courses derived from the predictive coding model correlated with neural signal time-courses in bilateral inferior frontal gyri and anterior insulae. Voxel-wise model selection indicated a superiority of the predictive coding model over conventional analysis approaches in explaining neural activity in these frontal areas, suggesting that frontal cortex encodes prediction errors that mediate endogenous perceptual transitions in bistable perception. Taken together

  17. A predictive coding account of bistable perception - a model-based fMRI study.

    Science.gov (United States)

    Weilnhammer, Veith; Stuke, Heiner; Hesselmann, Guido; Sterzer, Philipp; Schmack, Katharina

    2017-05-01

    In bistable vision, subjective perception wavers between two interpretations of a constant ambiguous stimulus. This dissociation between conscious perception and sensory stimulation has motivated various empirical studies on the neural correlates of bistable perception, but the neurocomputational mechanism behind endogenous perceptual transitions has remained elusive. Here, we recurred to a generic Bayesian framework of predictive coding and devised a model that casts endogenous perceptual transitions as a consequence of prediction errors emerging from residual evidence for the suppressed percept. Data simulations revealed close similarities between the model's predictions and key temporal characteristics of perceptual bistability, indicating that the model was able to reproduce bistable perception. Fitting the predictive coding model to behavioural data from an fMRI-experiment on bistable perception, we found a correlation across participants between the model parameter encoding perceptual stabilization and the behaviourally measured frequency of perceptual transitions, corroborating that the model successfully accounted for participants' perception. Formal model comparison with established models of bistable perception based on mutual inhibition and adaptation, noise or a combination of adaptation and noise was used for the validation of the predictive coding model against the established models. Most importantly, model-based analyses of the fMRI data revealed that prediction error time-courses derived from the predictive coding model correlated with neural signal time-courses in bilateral inferior frontal gyri and anterior insulae. Voxel-wise model selection indicated a superiority of the predictive coding model over conventional analysis approaches in explaining neural activity in these frontal areas, suggesting that frontal cortex encodes prediction errors that mediate endogenous perceptual transitions in bistable perception. Taken together, our current work

  18. Feasibility Study on the Satellite Rainfall Data for Prediction of Sediment- Related Disaster by the Japanese Prediction Methodology

    Science.gov (United States)

    Shimizu, Y.; Ishizuka, T.; Osanai, N.; Okazumi, T.

    2014-12-01

    In this study, the sediment-related disaster prediction method which based ground gauged rainfall-data, currently practiced in Japan was coupled with satellite rainfall data and applied to domestic large-scale sediment-related disasters. The study confirmed the feasibility of this integrated method. In Asia, large-scale sediment-related disasters which can sweep away an entire settlement occur frequently. Leyte Island suffered from a huge landslide in 2004, and Typhoon Molakot in 2009 caused huge landslides in Taiwan. In the event of these sediment-related disasters, immediate responses by central and local governments are crucial in crisis management. In general, there are not enough rainfall gauge stations in developing countries. Therefore national and local governments have little information to determine the risk level of water induced disasters in their service areas. In the Japanese methodology, a criterion is set by combining two indices: the short-term rainfall index and long-term rainfall index. The short-term rainfall index is defined as the 60-minute total rainfall; the long-term rainfall index as the soil-water index, which is an estimation of the retention status of fallen rainfall in soil. In July 2009, a high-density sediment related disaster, or a debris flow, occurred in Hofu City of Yamaguchi Prefecture, in the western region of Japan. This event was calculated by the Japanese standard methodology, and then analyzed for its feasibility. Hourly satellite based rainfall has underestimates compared with ground based rainfall data. Long-term index correlates with each other. Therefore, this study confirmed that it is possible to deliver information on the risk level of sediment-related disasters such as shallow landslides and debris flows. The prediction method tested in this study is expected to assist for timely emergency responses to rainfall-induced natural disasters in sparsely gauged areas. As the Global Precipitation Measurement (GPM) Plan

  19. Prediction of movement intention using connectivity within motor-related network: An electrocorticography study.

    Science.gov (United States)

    Kang, Byeong Keun; Kim, June Sic; Ryun, Seokyun; Chung, Chun Kee

    2018-01-01

    Most brain-machine interface (BMI) studies have focused only on the active state of which a BMI user performs specific movement tasks. Therefore, models developed for predicting movements were optimized only for the active state. The models may not be suitable in the idle state during resting. This potential maladaptation could lead to a sudden accident or unintended movement resulting from prediction error. Prediction of movement intention is important to develop a more efficient and reasonable BMI system which could be selectively operated depending on the user's intention. Physical movement is performed through the serial change of brain states: idle, planning, execution, and recovery. The motor networks in the primary motor cortex and the dorsolateral prefrontal cortex are involved in these movement states. Neuronal communication differs between the states. Therefore, connectivity may change depending on the states. In this study, we investigated the temporal dynamics of connectivity in dorsolateral prefrontal cortex and primary motor cortex to predict movement intention. Movement intention was successfully predicted by connectivity dynamics which may reflect changes in movement states. Furthermore, dorsolateral prefrontal cortex is crucial in predicting movement intention to which primary motor cortex contributes. These results suggest that brain connectivity is an excellent approach in predicting movement intention.

  20. Accuracy of clinical prediction rules in peptic ulcer perforation: an observational study

    DEFF Research Database (Denmark)

    Buck, David Levarett; Vester-Andersen, Morten; Møller, Morten Hylander

    2012-01-01

    Abstract Objective. The aim of the present study was to compare the ability of four clinical prediction rules to predict adverse outcome in perforated peptic ulcer (PPU): the Boey score, the American Society of Anesthesiologists (ASA) score, the Acute Physiology and Chronic Health Evaluation...... and breastfeeding women, non-surgically treated patients, patients with malignant ulcers, and patients with perforation of other organs were excluded. Primary outcome measure: 30-day mortality rate. Statistical analysis: the ability of four clinical prediction rules to distinguish survivors from non...

  1. Studies of the Raman Spectra of Cyclic and Acyclic Molecules: Combination and Prediction Spectrum Methods

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Taijin; Assary, Rajeev S.; Marshall, Christopher L.; Gosztola, David J.; Curtiss, Larry A.; Stair, Peter C.

    2012-04-02

    A combination of Raman spectroscopy and density functional methods was employed to investigate the spectral features of selected molecules: furfural, 5-hydroxymethyl furfural (HMF), methanol, acetone, acetic acid, and levulinic acid. The computed spectra and measured spectra are in excellent agreement, consistent with previous studies. Using the combination and prediction spectrum method (CPSM), we were able to predict the important spectral features of two platform chemicals, HMF and levulinic acid.The results have shown that CPSM is a useful alternative method for predicting vibrational spectra of complex molecules in the biomass transformation process.

  2. Prediction of methotrexate intolerance in juvenile idiopathic arthritis: a prospective, observational cohort study.

    Science.gov (United States)

    van Dijkhuizen, Evert Hendrik Pieter; Bulatović Ćalasan, Maja; Pluijm, Saskia M F; de Rotte, Maurits C F J; Vastert, Sebastiaan J; Kamphuis, Sylvia; de Jonge, Robert; Wulffraat, Nico M

    2015-01-01

    Methotrexate (MTX) is an effective and safe drug in the treatment of juvenile idiopathic arthritis (JIA). Despite its safety, MTX-related gastrointestinal adverse effects before and after MTX administration, termed MTX intolerance, occur frequently, leading to non-compliance and potentially premature MTX termination. The aim of this study was to construct a risk model to predict MTX intolerance. In a prospective JIA cohort, clinical variables and single nucleotide polymorphisms were determined at MTX start. The Methotrexate Intolerance Severity Score was employed to measure MTX intolerance in the first year of treatment. MTX intolerance was most prevalent at 6 or 12 months after MTX start, which was defined as the outcome for the prediction model. The model was developed in 152 patients using multivariable logistic regression analysis and subsequently internally validated using bootstrapping. The prediction model included the following predictors: JIA category, antinuclear antibody, parent/patient assessment of pain, Juvenile Arthritis Disease Activity Score-27, thrombocytes, alanine aminotransferase and creatinine. The model classified 77.5% of patients correctly, and 66.7% of patients after internal validation by bootstrapping. The lowest predicted risk of MTX intolerance was 18.9% and the highest predicted risk was 85.9%. The prediction model was transformed into a risk score (range 0-17). At a cut-off of ≥6, sensitivity was 82.0%, specificity 56.1%, positive predictive value was 58.7% and negative predictive value 80.4%. This clinical prediction model showed moderate predictive power to detect MTX intolerance. To develop into a clinically usable tool, it should be validated in an independent cohort and updated with new predictors. Such an easy-to-use tool could then assist clinicians in identifying patients at risk to develop MTX intolerance, and in turn to monitor them closely and intervene timely in order to prevent the development of MTX intolerance

  3. Accurate Prediction of Motor Failures by Application of Multi CBM Tools: A Case Study

    Science.gov (United States)

    Dutta, Rana; Singh, Veerendra Pratap; Dwivedi, Jai Prakash

    2018-02-01

    Motor failures are very difficult to predict accurately with a single condition-monitoring tool as both electrical and the mechanical systems are closely related. Electrical problem, like phase unbalance, stator winding insulation failures can, at times, lead to vibration problem and at the same time mechanical failures like bearing failure, leads to rotor eccentricity. In this case study of a 550 kW blower motor it has been shown that a rotor bar crack was detected by current signature analysis and vibration monitoring confirmed the same. In later months in a similar motor vibration monitoring predicted bearing failure and current signature analysis confirmed the same. In both the cases, after dismantling the motor, the predictions were found to be accurate. In this paper we will be discussing the accurate predictions of motor failures through use of multi condition monitoring tools with two case studies.

  4. Factors predictive for incidence and remission of internet addiction in young adolescents: a prospective study.

    Science.gov (United States)

    Ko, Chih-Hung; Yen, Ju-Yu; Yen, Cheng-Fang; Lin, Huang-Chi; Yang, Ming-Jen

    2007-08-01

    The aim of the study is to determine the incidence and remission rates for Internet addiction and the associated predictive factors in young adolescents over a 1-year follow-up. This was a prospective, population-based investigation. Five hundred seventeen students (267 male and 250 female) were recruited from three junior high schools in southern Taiwan. The factors examined included gender, personality, mental health, self-esteem, family function, life satisfaction, and Internet activities. The result revealed that the 1-year incidence and remission rates for Internet addiction were 7.5% and 49.5% respectively. High exploratory excitability, low reward dependence, low self-esteem, low family function, and online game playing predicted the emergency of the Internet addiction. Further, low hostility and low interpersonal sensitivity predicted remission of Internet addiction. The factors predictive incidence and remission of Internet addiction identified in this study could be provided for prevention and promoting remission of Internet addiction in adolescents.

  5. Penalized regression techniques for prediction: a case study for predicting tree mortality using remotely sensed vegetation indices

    NARCIS (Netherlands)

    Lazaridis, D.C.; Verbesselt, J.; Robinson, A.P.

    2011-01-01

    Constructing models can be complicated when the available fitting data are highly correlated and of high dimension. However, the complications depend on whether the goal is prediction instead of estimation. We focus on predicting tree mortality (measured as the number of dead trees) from change

  6. Using data-driven approach for wind power prediction: A comparative study

    International Nuclear Information System (INIS)

    Taslimi Renani, Ehsan; Elias, Mohamad Fathi Mohamad; Rahim, Nasrudin Abd.

    2016-01-01

    Highlights: • Double exponential smoothing is the most accurate model in wind speed prediction. • A two-stage feature selection method is proposed to select most important inputs. • Direct prediction illustrates better accuracy than indirect prediction. • Adaptive neuro fuzzy inference system outperforms data mining algorithms. • Random forest performs the worst compared to other data mining algorithm. - Abstract: Although wind energy is intermittent and stochastic in nature, it is increasingly important in the power generation due to its sustainability and pollution-free. Increased utilization of wind energy sources calls for more robust and efficient prediction models to mitigate uncertainties associated with wind power. This research compares two different approaches in wind power forecasting which are indirect and direct prediction methods. In indirect method, several times series are applied to forecast the wind speed, whereas the logistic function with five parameters is then used to forecast the wind power. In this study, backtracking search algorithm with novel crossover and mutation operators is employed to find the best parameters of five-parameter logistic function. A new feature selection technique, combining the mutual information and neural network is proposed in this paper to extract the most informative features with a maximum relevancy and minimum redundancy. From the comparative study, the results demonstrate that, in the direct prediction approach where the historical weather data are used to predict the wind power generation directly, adaptive neuro fuzzy inference system outperforms five data mining algorithms namely, random forest, M5Rules, k-nearest neighbor, support vector machine and multilayer perceptron. Moreover, it is also found that the mean absolute percentage error of the direct prediction method using adaptive neuro fuzzy inference system is 1.47% which is approximately less than half of the error obtained with the

  7. Using Cash Flows to Predict Bankruptcy of Chemical Companies: Case Study Approach

    OpenAIRE

    Siow, Hui Wen

    2009-01-01

    The intent of this study is to present an argument for the usefulness of cash flow information in bankruptcy prediction, and whether cash flow information provide a superior prediction of business failure over the conventional accrual accounting information. In addition, this dissertation also aim to analyze other important factors leading to bankruptcy, particularly contingent liabilities in which the obligations are not accrued and accounted for, nor are they considered in conventional bank...

  8. Urine NGAL Predicts Severity of Acute Kidney Injury After Cardiac Surgery: A Prospective Study

    OpenAIRE

    Bennett, Michael; Dent, Catherine L.; Ma, Qing; Dastrala, Sudha; Grenier, Frank; Workman, Ryan; Syed, Hina; Ali, Salman; Barasch, Jonathan; Devarajan, Prasad

    2008-01-01

    Background and objectives: The authors have previously shown that urine neutrophil gelatinase-associated lipocalin (NGAL), measured by a research ELISA, is an early predictive biomarker of acute kidney injury (AKI) after cardiopulmonary bypass (CPB). In this study, whether an NGAL immunoassay developed for a standardized clinical platform (ARCHITECT analyzer®, Abbott Diagnostics Division, Abbott Laboratories, Abbott Park, IL) can predict AKI after CPB was tested.

  9. The predictive power of personality traits on insomnia symptoms: a longitudinal study among shift workers

    OpenAIRE

    Larsgård, Borgar

    2015-01-01

    Shift work can have adverse effects on employees' health, including symptoms of insomnia. This may cause severe problems both for employee and employer. The personality variables morningness, neuroticism and extraversion, along with some demographic variables (e.g. gender, age) have been found to correlate with insomnia symptoms, but predictive data have been scarce. This study sought to discover whether personality variables could predict insomnia. A hierarchical longitudinal (six months)...

  10. Predicting ongoing pregnancy chances after IVF and ICSI: A national prospective study

    OpenAIRE

    Lintsen, Bea; Eijkemans, René; Hunault, C.C.; Bouwmans-Frijters, Clazien; Hakkaart-van Roijen, Leona; Habbema, Dik; Braat, Didi

    2007-01-01

    textabstractBackground: The Dutch IVF guideline suggests triage of patients for IVF based on diagnostic category, duration of infertility and female age. There is no evidence for the effectiveness of these criteria. We evaluated the predictive value of patient characteristics that are used in the Dutch IVF guideline and developed a model that predicts the IVF ongoing pregnancy chance within 12 months. Methods: In a national prospective cohort study, pregnancy chances after IVF and ICSI treatm...

  11. Definition of tolerance to continuous hyperoxia in man - An abstract report of Predictive Studies V

    Science.gov (United States)

    Lambertsen, C. J.; Clark, J. M.; Gelfand, R.; Pisarello, J. B.; Cobbs, W. H.

    1987-01-01

    The overall goals, design, and procedures of Predictive Studies V are discussed as well as the specific elements of neural effects produced by prolonged hyperoxia. It is noted that Predictive Studies V study of oxygen poisoning in normal men during uninterrupted exposures to oxygen over the range of hyperbaric oxygen exposure most useful in diving, the treatment of gas lesion diseases, and general hyperbaric medicine. It is found that, throughout the study, the most striking observations were related to effects on visual function, on the lung, and the probable interactions of preconvulsive neural activity with effects on cardiovascular and respiratory-pulmonary functions.

  12. Disorder Prediction Methods, Their Applicability to Different Protein Targets and Their Usefulness for Guiding Experimental Studies

    Directory of Open Access Journals (Sweden)

    Jennifer D. Atkins

    2015-08-01

    Full Text Available The role and function of a given protein is dependent on its structure. In recent years, however, numerous studies have highlighted the importance of unstructured, or disordered regions in governing a protein’s function. Disordered proteins have been found to play important roles in pivotal cellular functions, such as DNA binding and signalling cascades. Studying proteins with extended disordered regions is often problematic as they can be challenging to express, purify and crystallise. This means that interpretable experimental data on protein disorder is hard to generate. As a result, predictive computational tools have been developed with the aim of predicting the level and location of disorder within a protein. Currently, over 60 prediction servers exist, utilizing different methods for classifying disorder and different training sets. Here we review several good performing, publicly available prediction methods, comparing their application and discussing how disorder prediction servers can be used to aid the experimental solution of protein structure. The use of disorder prediction methods allows us to adopt a more targeted approach to experimental studies by accurately identifying the boundaries of ordered protein domains so that they may be investigated separately, thereby increasing the likelihood of their successful experimental solution.

  13. Risk Prediction Using Genome-Wide Association Studies on Type 2 Diabetes

    Directory of Open Access Journals (Sweden)

    Sungkyoung Choi

    2016-12-01

    Full Text Available The success of genome-wide association studies (GWASs has enabled us to improve risk assessment and provide novel genetic variants for diagnosis, prevention, and treatment. However, most variants discovered by GWASs have been reported to have very small effect sizes on complex human diseases, which has been a big hurdle in building risk prediction models. Recently, many statistical approaches based on penalized regression have been developed to solve the “large p and small n” problem. In this report, we evaluated the performance of several statistical methods for predicting a binary trait: stepwise logistic regression (SLR, least absolute shrinkage and selection operator (LASSO, and Elastic-Net (EN. We first built a prediction model by combining variable selection and prediction methods for type 2 diabetes using Affymetrix Genome-Wide Human SNP Array 5.0 from the Korean Association Resource project. We assessed the risk prediction performance using area under the receiver operating characteristic curve (AUC for the internal and external validation datasets. In the internal validation, SLR-LASSO and SLR-EN tended to yield more accurate predictions than other combinations. During the external validation, the SLR-SLR and SLR-EN combinations achieved the highest AUC of 0.726. We propose these combinations as a potentially powerful risk prediction model for type 2 diabetes.

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

    Science.gov (United States)

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

    2018-05-15

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

  15. Can Post mTBI Neurological Soft Signs Predict Postconcussive and PTSD Symptoms : A Pilot Study

    Science.gov (United States)

    2014-02-01

    disorders , including post - traumatic stress disorder ( PTSD ), but they have scarcely been studied in TBI. The present study measured NSS in the...including post - traumatic stress disorder ( PTSD ), but they have scarcely been studied in TBI. The present study measured NSS in the acute aftermath of...Can Post mTBI Neurological Soft Signs Predict Postconcussive and PTSD Symptoms?: A Pilot Study 5a. CONTRACT NUMBER E-Mail:

  16. PREDICTION OF WATER QUALITY INDEX USING BACK PROPAGATION NETWORK ALGORITHM. CASE STUDY: GOMBAK RIVER

    Directory of Open Access Journals (Sweden)

    FARIS GORASHI

    2012-08-01

    Full Text Available The aim of this study is to enable prediction of water quality parameters with conjunction to land use attributes and to find a low-end alternative for water quality monitoring techniques, which are typically expensive and tedious. It also aims to ensure sustainable development, which is essentially has effects on water quality. The research approach followed in this study is via using artificial neural networks, and geographical information system to provide a reliable prediction model. Back propagation network algorithm was used for the purpose of this study. The proposed approach minimized most of anomalies associated with prediction methods and provided water quality prediction with precision. The study used 5 hidden nodes in this network. The network was optimized to complete 23145 cycles before it reaches the best error of 0.65. Stations 18 had shown the greatest fluctuation among the three stations as it reflects an area of on-going rapid development of Gombak river watershed. The results had shown a very close prediction with best error of 0.67 in a sensitivity test that was carried afterwards.

  17. The Development of Storm Surge Ensemble Prediction System and Case Study of Typhoon Meranti in 2016

    Science.gov (United States)

    Tsai, Y. L.; Wu, T. R.; Terng, C. T.; Chu, C. H.

    2017-12-01

    Taiwan is under the threat of storm surge and associated inundation, which is located at a potentially severe storm generation zone. The use of ensemble prediction can help forecasters to know the characteristic of storm surge under the uncertainty of track and intensity. In addition, it can help the deterministic forecasting. In this study, the kernel of ensemble prediction system is based on COMCOT-SURGE (COrnell Multi-grid COupled Tsunami Model - Storm Surge). COMCOT-SURGE solves nonlinear shallow water equations in Open Ocean and coastal regions with the nested-grid scheme and adopts wet-dry-cell treatment to calculate potential inundation area. In order to consider tide-surge interaction, the global TPXO 7.1 tide model provides the tidal boundary conditions. After a series of validations and case studies, COMCOT-SURGE has become an official operating system of Central Weather Bureau (CWB) in Taiwan. In this study, the strongest typhoon in 2016, Typhoon Meranti, is chosen as a case study. We adopt twenty ensemble members from CWB WRF Ensemble Prediction System (CWB WEPS), which differs from parameters of microphysics, boundary layer, cumulus, and surface. From box-and-whisker results, maximum observed storm surges were located in the interval of the first and third quartile at more than 70 % gauge locations, e.g. Toucheng, Chengkung, and Jiangjyun. In conclusion, the ensemble prediction can effectively help forecasters to predict storm surge especially under the uncertainty of storm track and intensity

  18. Predictive modelling for swallowing dysfunction after primary (chemo)radiation: results of a prospective observational study.

    Science.gov (United States)

    Christianen, Miranda E M C; Schilstra, Cornelis; Beetz, Ivo; Muijs, Christina T; Chouvalova, Olga; Burlage, Fred R; Doornaert, Patricia; Koken, Phil W; Leemans, C René; Rinkel, Rico N P M; de Bruijn, Marieke J; de Bock, G H; Roodenburg, Jan L N; van der Laan, Bernard F A M; Slotman, Ben J; Verdonck-de Leeuw, Irma M; Bijl, Hendrik P; Langendijk, Johannes A

    2012-10-01

    The purpose of this large multicentre prospective cohort study was to identify which dose volume histogram parameters and pre-treatment factors are most important to predict physician-rated and patient-rated radiation-induced swallowing dysfunction (RISD) in order to develop predictive models for RISD after curative (chemo) radiotherapy ((CH) RT). The study population consisted of 354 consecutive head and neck cancer patients treated with (CH) RT. The primary endpoint was grade 2 or more swallowing dysfunction according to the RTOG/EORTC late radiation morbidity scoring criteria at 6 months after (CH) RT. The secondary endpoints were patient-rated swallowing complaints as assessed with the EORTC QLQ-H&N35 questionnaire. To select the most predictive variables a multivariate logistic regression analysis with bootstrapping was used. At 6 months after (CH) RT the bootstrapping procedure revealed that a model based on the mean dose to the superior pharyngeal constrictor muscle (PCM) and mean dose to the supraglottic larynx was most predictive. For the secondary endpoints different predictive models were found: for problems with swallowing liquids the most predictive factors were the mean dose to the supraglottic larynx and radiation technique (3D-CRT versus IMRT). For problems with swallowing soft food the mean dose to the middle PCM, age (18-65 versus >65 years), tumour site (naso/oropharynx versus other sites) and radiation technique (3D-CRT versus IMRT) were the most predictive factors. For problems with swallowing solid food the most predictive factors were the mean dose to the superior PCM, the mean dose to the supraglottic larynx and age (18-65 versus >65 years). And for choking when swallowing the V60 of the oesophageal inlet muscle and the mean dose to the supraglottic larynx were the most predictive factors. Physician-rated and patient-rated RISD in head and neck cancer patients treated with (CH) RT cannot be predicted with univariate relationships between the

  19. Predicting Study Abroad Intentions Based on the Theory of Planned Behavior

    Science.gov (United States)

    Schnusenberg, Oliver; de Jong, Pieter; Goel, Lakshmi

    2012-01-01

    The emphasis on study abroad programs is growing in the academic context as U.S. based universities seek to incorporate a global perspective in education. Using a model that has underpinnings in the theory of planned behavior (TPB), we predict students' intention to participate in short-term study abroad program. We use TPB to identify behavioral,…

  20. A Predictive and Follow-Up Study of Abusive and Neglectful Families by Case Analysis.

    Science.gov (United States)

    Heap, Kari Killen

    1991-01-01

    A case analysis, predictive study, and follow-up study of 17 abused and/or neglected children found that the prognosis for abusive and/or neglectful parents is poorer when they are scored high on immaturity than when they are scored high on emotional problems. (BRM)

  1. Functional knowledge transfer for high-accuracy prediction of under-studied biological processes.

    Directory of Open Access Journals (Sweden)

    Christopher Y Park

    Full Text Available A key challenge in genetics is identifying the functional roles of genes in pathways. Numerous functional genomics techniques (e.g. machine learning that predict protein function have been developed to address this question. These methods generally build from existing annotations of genes to pathways and thus are often unable to identify additional genes participating in processes that are not already well studied. Many of these processes are well studied in some organism, but not necessarily in an investigator's organism of interest. Sequence-based search methods (e.g. BLAST have been used to transfer such annotation information between organisms. We demonstrate that functional genomics can complement traditional sequence similarity to improve the transfer of gene annotations between organisms. Our method transfers annotations only when functionally appropriate as determined by genomic data and can be used with any prediction algorithm to combine transferred gene function knowledge with organism-specific high-throughput data to enable accurate function prediction. We show that diverse state-of-art machine learning algorithms leveraging functional knowledge transfer (FKT dramatically improve their accuracy in predicting gene-pathway membership, particularly for processes with little experimental knowledge in an organism. We also show that our method compares favorably to annotation transfer by sequence similarity. Next, we deploy FKT with state-of-the-art SVM classifier to predict novel genes to 11,000 biological processes across six diverse organisms and expand the coverage of accurate function predictions to processes that are often ignored because of a dearth of annotated genes in an organism. Finally, we perform in vivo experimental investigation in Danio rerio and confirm the regulatory role of our top predicted novel gene, wnt5b, in leftward cell migration during heart development. FKT is immediately applicable to many bioinformatics

  2. Chemotherapy effectiveness and mortality prediction in surgically treated osteosarcoma dogs: A validation study.

    Science.gov (United States)

    Schmidt, A F; Nielen, M; Withrow, S J; Selmic, L E; Burton, J H; Klungel, O H; Groenwold, R H H; Kirpensteijn, J

    2016-03-01

    Canine osteosarcoma is the most common bone cancer, and an important cause of mortality and morbidity, in large purebred dogs. Previously we constructed two multivariable models to predict a dog's 5-month or 1-year mortality risk after surgical treatment for osteosarcoma. According to the 5-month model, dogs with a relatively low risk of 5-month mortality benefited most from additional chemotherapy treatment. In the present study, we externally validated these results using an independent cohort study of 794 dogs. External performance of our prediction models showed some disagreement between observed and predicted risk, mean difference: -0.11 (95% confidence interval [95% CI]-0.29; 0.08) for 5-month risk and 0.25 (95%CI 0.10; 0.40) for 1-year mortality risk. After updating the intercept, agreement improved: -0.0004 (95%CI-0.16; 0.16) and -0.002 (95%CI-0.15; 0.15). The chemotherapy by predicted mortality risk interaction (P-value=0.01) showed that the chemotherapy compared to no chemotherapy effectiveness was modified by 5-month mortality risk: dogs with a relatively lower risk of mortality benefited most from additional chemotherapy. Chemotherapy effectiveness on 1-year mortality was not significantly modified by predicted risk (P-value=0.28). In conclusion, this external validation study confirmed that our multivariable risk prediction models can predict a patient's mortality risk and that dogs with a relatively lower risk of 5-month mortality seem to benefit most from chemotherapy. Copyright © 2016 Elsevier B.V. All rights reserved.

  3. Prediction of quantitative phenotypes based on genetic networks: a case study in yeast sporulation

    Directory of Open Access Journals (Sweden)

    Shen Li

    2010-09-01

    Full Text Available Abstract Background An exciting application of genetic network is to predict phenotypic consequences for environmental cues or genetic perturbations. However, de novo prediction for quantitative phenotypes based on network topology is always a challenging task. Results Using yeast sporulation as a model system, we have assembled a genetic network from literature and exploited Boolean network to predict sporulation efficiency change upon deleting individual genes. We observe that predictions based on the curated network correlate well with the experimentally measured values. In addition, computational analysis reveals the robustness and hysteresis of the yeast sporulation network and uncovers several patterns of sporulation efficiency change caused by double gene deletion. These discoveries may guide future investigation of underlying mechanisms. We have also shown that a hybridized genetic network reconstructed from both temporal microarray data and literature is able to achieve a satisfactory prediction accuracy of the same quantitative phenotypes. Conclusions This case study illustrates the value of predicting quantitative phenotypes based on genetic network and provides a generic approach.

  4. A study on the predictability of acute lymphoblastic leukaemia response to treatment using a hybrid oncosimulator.

    Science.gov (United States)

    Ouzounoglou, Eleftherios; Kolokotroni, Eleni; Stanulla, Martin; Stamatakos, Georgios S

    2018-02-06

    Efficient use of Virtual Physiological Human (VPH)-type models for personalized treatment response prediction purposes requires a precise model parameterization. In the case where the available personalized data are not sufficient to fully determine the parameter values, an appropriate prediction task may be followed. This study, a hybrid combination of computational optimization and machine learning methods with an already developed mechanistic model called the acute lymphoblastic leukaemia (ALL) Oncosimulator which simulates ALL progression and treatment response is presented. These methods are used in order for the parameters of the model to be estimated for retrospective cases and to be predicted for prospective ones. The parameter value prediction is based on a regression model trained on retrospective cases. The proposed Hybrid ALL Oncosimulator system has been evaluated when predicting the pre-phase treatment outcome in ALL. This has been correctly achieved for a significant percentage of patient cases tested (approx. 70% of patients). Moreover, the system is capable of denying the classification of cases for which the results are not trustworthy enough. In that case, potentially misleading predictions for a number of patients are avoided, while the classification accuracy for the remaining patient cases further increases. The results obtained are particularly encouraging regarding the soundness of the proposed methodologies and their relevance to the process of achieving clinical applicability of the proposed Hybrid ALL Oncosimulator system and VPH models in general.

  5. Predicting survival of de novo metastatic breast cancer in Asian women: systematic review and validation study.

    Science.gov (United States)

    Miao, Hui; Hartman, Mikael; Bhoo-Pathy, Nirmala; Lee, Soo-Chin; Taib, Nur Aishah; Tan, Ern-Yu; Chan, Patrick; Moons, Karel G M; Wong, Hoong-Seam; Goh, Jeremy; Rahim, Siti Mastura; Yip, Cheng-Har; Verkooijen, Helena M

    2014-01-01

    In Asia, up to 25% of breast cancer patients present with distant metastases at diagnosis. Given the heterogeneous survival probabilities of de novo metastatic breast cancer, individual outcome prediction is challenging. The aim of the study is to identify existing prognostic models for patients with de novo metastatic breast cancer and validate them in Asia. We performed a systematic review to identify prediction models for metastatic breast cancer. Models were validated in 642 women with de novo metastatic breast cancer registered between 2000 and 2010 in the Singapore Malaysia Hospital Based Breast Cancer Registry. Survival curves for low, intermediate and high-risk groups according to each prognostic score were compared by log-rank test and discrimination of the models was assessed by concordance statistic (C-statistic). We identified 16 prediction models, seven of which were for patients with brain metastases only. Performance status, estrogen receptor status, metastatic site(s) and disease-free interval were the most common predictors. We were able to validate nine prediction models. The capacity of the models to discriminate between poor and good survivors varied from poor to fair with C-statistics ranging from 0.50 (95% CI, 0.48-0.53) to 0.63 (95% CI, 0.60-0.66). The discriminatory performance of existing prediction models for de novo metastatic breast cancer in Asia is modest. Development of an Asian-specific prediction model is needed to improve prognostication and guide decision making.

  6. A 3-Year Study of Predictive Factors for Positive and Negative Appendicectomies.

    Science.gov (United States)

    Chang, Dwayne T S; Maluda, Melissa; Lee, Lisa; Premaratne, Chandrasiri; Khamhing, Srisongham

    2018-03-06

    Early and accurate identification or exclusion of acute appendicitis is the key to avoid the morbidity of delayed treatment for true appendicitis or unnecessary appendicectomy, respectively. We aim (i) to identify potential predictive factors for positive and negative appendicectomies; and (ii) to analyse the use of ultrasound scans (US) and computed tomography (CT) scans for acute appendicitis. All appendicectomies that took place at our hospital from the 1st of January 2013 to the 31st of December 2015 were retrospectively recorded. Test results of potential predictive factors of acute appendicitis were recorded. Statistical analysis was performed using Fisher exact test, logistic regression analysis, sensitivity, specificity, and positive and negative predictive values calculation. 208 patients were included in this study. 184 patients had histologically proven acute appendicitis. The other 24 patients had either nonappendicitis pathology or normal appendix. Logistic regression analysis showed statistically significant associations between appendicitis and white cell count, neutrophil count, C-reactive protein, and bilirubin. Neutrophil count was the test with the highest sensitivity and negative predictive values, whereas bilirubin was the test with the highest specificity and positive predictive values (PPV). US and CT scans had high sensitivity and PPV for diagnosing appendicitis. No single test was sufficient to diagnose or exclude acute appendicitis by itself. Combining tests with high sensitivity (abnormal neutrophil count, and US and CT scans) and high specificity (raised bilirubin) may predict acute appendicitis more accurately.

  7. Development and validation of a predictive model for excessive postpartum blood loss: A retrospective, cohort study.

    Science.gov (United States)

    Rubio-Álvarez, Ana; Molina-Alarcón, Milagros; Arias-Arias, Ángel; Hernández-Martínez, Antonio

    2018-03-01

    postpartum haemorrhage is one of the leading causes of maternal morbidity and mortality worldwide. Despite the use of uterotonics agents as preventive measure, it remains a challenge to identify those women who are at increased risk of postpartum bleeding. to develop and to validate a predictive model to assess the risk of excessive bleeding in women with vaginal birth. retrospective cohorts study. "Mancha-Centro Hospital" (Spain). the elaboration of the predictive model was based on a derivation cohort consisting of 2336 women between 2009 and 2011. For validation purposes, a prospective cohort of 953 women between 2013 and 2014 were employed. Women with antenatal fetal demise, multiple pregnancies and gestations under 35 weeks were excluded METHODS: we used a multivariate analysis with binary logistic regression, Ridge Regression and areas under the Receiver Operating Characteristic curves to determine the predictive ability of the proposed model. there was 197 (8.43%) women with excessive bleeding in the derivation cohort and 63 (6.61%) women in the validation cohort. Predictive factors in the final model were: maternal age, primiparity, duration of the first and second stages of labour, neonatal birth weight and antepartum haemoglobin levels. Accordingly, the predictive ability of this model in the derivation cohort was 0.90 (95% CI: 0.85-0.93), while it remained 0.83 (95% CI: 0.74-0.92) in the validation cohort. this predictive model is proved to have an excellent predictive ability in the derivation cohort, and its validation in a latter population equally shows a good ability for prediction. This model can be employed to identify women with a higher risk of postpartum haemorrhage. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. Office gel sonovaginography for the prediction of posterior deep infiltrating endometriosis: a multicenter prospective observational study.

    Science.gov (United States)

    Reid, S; Lu, C; Hardy, N; Casikar, I; Reid, G; Cario, G; Chou, D; Almashat, D; Condous, G

    2014-12-01

    To use office gel sonovaginography (SVG) to predict posterior deep infiltrating endometriosis (DIE) in women undergoing laparoscopy. This was a multicenter prospective observational study carried out between January 2009 and February 2013. All women were of reproductive age, had a history of chronic pelvic pain and underwent office gel SVG assessment for the prediction of posterior compartment DIE prior to laparoscopic endometriosis surgery. Gel SVG findings were compared with laparoscopic findings to determine the diagnostic accuracy of office gel SVG for the prediction of posterior compartment DIE. In total, 189 women underwent preoperative gel SVG and laparoscopy for endometriosis. At laparoscopy, 57 (30%) women had posterior DIE and 43 (23%) had rectosigmoid/anterior rectal DIE. For the prediction of rectosigmoid/anterior rectal (i.e. bowel) DIE, gel SVG had an accuracy of 92%, sensitivity of 88%, specificity of 93%, positive predictive value (PPV) of 79%, negative predictive value (NPV) of 97%, positive likelihood ratio (LR+) of 12.9 and negative likelihood ratio (LR-) of 0.12 (P = 3.98E-25); for posterior vaginal wall and rectovaginal septum (RVS) DIE, respectively, the accuracy was 95% and 95%, sensitivity was 18% and 18%, specificity was 99% and 100%, PPV was 67% and 100%, NPV was 95% and 95%, LR+ was 32.4 and infinity and LR- was 0.82 and 0.82 (P = 0.009 and P = 0.003). Office gel SVG appears to be an effective outpatient imaging technique for the prediction of bowel DIE, with a higher accuracy for the prediction of rectosigmoid compared with anterior rectal DIE. Although the sensitivity for vaginal and RVS DIE was limited, gel SVG had a high specificity and NPV for all forms of posterior DIE, indicating that a negative gel SVG examination is highly suggestive of the absence of DIE at laparoscopy. Copyright © 2014 ISUOG. Published by John Wiley & Sons Ltd.

  9. Predictive tool of energy performance of cold storage in agrifood industries: The Portuguese case study

    International Nuclear Information System (INIS)

    Nunes, José; Neves, Diogo; Gaspar, Pedro D.; Silva, Pedro D.; Andrade, Luís P.

    2014-01-01

    Highlights: • A predictive tool for assessment of the energy performance in agrifood industries that use cold storage is developed. • The correlations used by the predictive tool result from the greatest number of data sets collected to date in Portugal. • Strong relationships between raw material, energy consumption and volume of cold stores were established. • Case studies were analyzed that demonstrate the applicability of the tool. • The tool results are useful in the decision-making process of practice measures for the improvement of energy efficiency. - Abstract: Food processing and conservation represent decisive factors for the sustainability of the planet given the significant growth of the world population in the last decades. Therefore, the cooling process during the manufacture and/or storage of food products has been subject of study and improvement in order to ensure the food supply with good quality and safety. A predictive tool for assessment of the energy performance in agrifood industries that use cold storage is developed in order to contribute to the improvement of the energy efficiency of this industry. The predictive tool is based on a set of characteristic correlated parameters: amount of raw material annually processed, annual energy consumption and volume of cold rooms. Case studies of application of the predictive tool consider industries in the meat sector, specifically slaughterhouses. The results obtained help on the decision-making of practice measures for improvement of the energy efficiency in this industry

  10. Workplace Deviance: A Predictive study of Occupational Stress and Emotional Intelligence among Secondary School teachers

    OpenAIRE

    Oguegbe Tochukwu Matthew; Uzoh Bonaventure Chigozie; Anyikwa Kosiso

    2014-01-01

    The study investigated workplace deviance:A predictive study of Occupational stress and Emotional intelligence among secondary school teachers. A total number of 198 teachers from Nigeria served as participants with the mean age of 32.98,standarddeviation 0f 9.26 and age range of 22 to 54years. Three instruments were used in the study: Workplace deviance scale, Occupational stress inventory and Emotional intelligence scale. The study adopted a correlational design with Pearson Product Moment ...

  11. The Role of the Indian Ocean Sector for Prediction of the Coupled Indo-Pacific System: Impact of Atmospheric Coupling

    Science.gov (United States)

    Hackert, E. C.; Busalacchi, A. J.; Carton, J.; Murtugudde, R.; Arkin, P.; Evans, M. N.

    2017-01-01

    Indian Ocean (IO) dynamics impact ENSO predictability by influencing wind and precipitation anomalies in the Pacific. To test if the upstream influence of the IO improves ENSO validation statistics, a combination of forced ocean, atmosphere, and coupled models are utilized. In one experiment, the full tropical Indo-Pacific region atmosphere is forced by observed interannual SST anomalies. In the other, the IO is forced by climatological SST. Differences between these two forced atmospheric model experiments spotlight a much richer wind response pattern in the Pacific than previous studies that used idealized forcing and simple linear atmospheric models. Weak westerlies are found near the equator similar to earlier literature. However, at initialization strong easterlies between 30 deg. S to 10 deg. S and 0 deg. N to 25 deg. N and equatorial convergence of the meridional winds across the entire Pacific are unique findings from this paper. The large-scale equatorial divergence west of the dateline and northeasterly-to-northwesterly cross-equatorial flow converging on the equator east of the dateline in the Pacific are generated from interannual IO SST coupling. In addition, off-equatorial downwelling curl impacts large-scale oceanic waves (i.e., Rossby waves reflect as western boundary Kelvin waves). After 3 months, these downwelling equatorial Kelvin waves propagate across the Pacific and strengthen the NINO3 SST. Eventually Bjerknes feedbacks take hold in the eastern Pacific which allows this warm anomaly to grow. Coupled forecasts for NINO3 SST anomalies for 1993-2014 demonstrate that including interannual IO forcing significantly improves predictions for 3-9 month lead times.

  12. The role of the Indian Ocean sector for prediction of the coupled Indo-Pacific system: Impact of atmospheric coupling

    Science.gov (United States)

    Hackert, E. C.; Busalacchi, A. J.; Carton, J.; Murtugudde, R.; Arkin, P.; Evans, M. N.

    2017-04-01

    Indian Ocean (IO) dynamics impact ENSO predictability by influencing wind and precipitation anomalies in the Pacific. To test if the upstream influence of the IO improves ENSO validation statistics, a combination of forced ocean, atmosphere, and coupled models are utilized. In one experiment, the full tropical Indo-Pacific region atmosphere is forced by observed interannual SST anomalies. In the other, the IO is forced by climatological SST. Differences between these two forced atmospheric model experiments spotlight a much richer wind response pattern in the Pacific than previous studies that used idealized forcing and simple linear atmospheric models. Weak westerlies are found near the equator similar to earlier literature. However, at initialization strong easterlies between 30°S-10°S and 0°N-25°N and equatorial convergence of the meridional winds across the entire Pacific are unique findings from this paper. The large-scale equatorial divergence west of the dateline and northeasterly-to-northwesterly cross-equatorial flow converging on the equator east of the dateline in the Pacific are generated from interannual IO SST coupling. In addition, off-equatorial downwelling curl impacts large-scale oceanic waves (i.e., Rossby waves reflect as western boundary Kelvin waves). After 3 months, these downwelling equatorial Kelvin waves propagate across the Pacific and strengthen the NINO3 SST. Eventually Bjerknes feedbacks take hold in the eastern Pacific which allows this warm anomaly to grow. Coupled forecasts for NINO3 SST anomalies for 1993-2014 demonstrate that including interannual IO forcing significantly improves predictions for 3-9 month lead times.

  13. Predictability of soil moisture and streamflow on subseasonal timescales: A case study

    Science.gov (United States)

    Orth, Rene; Seneviratne, Sonia I.

    2013-10-01

    Hydrological forecasts constitute an important tool in water resource management, especially in the case of impending extreme events. This study investigates the potential predictability of soil moisture and streamflow in Switzerland using a conceptual model including a simple water balance representation and a snow module. Our results show that simulated soil moisture and streamflow are more predictable (as indicated by significantly improved performance compared to climatology) until lead times of approximately 1 week and 2-3 days, respectively, when using initial soil moisture information and climatological atmospheric forcing. Using also initial snow information and seasonal weather forecasts as forcing, the predictable lead time doubles in case of soil moisture and triples for streamflow. The skill contributions of the additional information vary with altitude; at low altitudes the precipitation forecast is most important, whereas in mountainous areas the temperature forecast and the initial snow information are the most valuable contributors. We find furthermore that the soil moisture and streamflow forecast skills increase with increasing initial soil moisture anomalies. Comparing the respective value of realistic initial conditions and state-of-the-art forcing forecasts, we show that the former are generally more important for soil moisture forecasts, whereas the latter are more valuable for streamflow forecasts. To relate the derived predictabilities to respective soil moisture and streamflow memories investigated in other publications, we additionally illustrate the similarity between the concepts of memory and predictability as measures of persistence in the last part of this study.

  14. Emphysema predicts hospitalisation and incident airflow obstruction among older smokers: a prospective cohort study.

    Directory of Open Access Journals (Sweden)

    David A McAllister

    Full Text Available Emphysema on CT is common in older smokers. We hypothesised that emphysema on CT predicts acute episodes of care for chronic lower respiratory disease among older smokers.Participants in a lung cancer screening study age ≥ 60 years were recruited into a prospective cohort study in 2001-02. Two radiologists independently visually assessed the severity of emphysema as absent, mild, moderate or severe. Percent emphysema was defined as the proportion of voxels ≤ -910 Hounsfield Units. Participants completed a median of 5 visits over a median of 6 years of follow-up. The primary outcome was hospitalization, emergency room or urgent office visit for chronic lower respiratory disease. Spirometry was performed following ATS/ERS guidelines. Airflow obstruction was defined as FEV1/FVC ratio <0.70 and FEV1<80% predicted.Of 521 participants, 4% had moderate or severe emphysema, which was associated with acute episodes of care (rate ratio 1.89; 95% CI: 1.01-3.52 adjusting for age, sex and race/ethnicity, as was percent emphysema, with similar associations for hospitalisation. Emphysema on visual assessment also predicted incident airflow obstruction (HR 5.14; 95% CI 2.19-21.1.Visually assessed emphysema and percent emphysema on CT predicted acute episodes of care for chronic lower respiratory disease, with the former predicting incident airflow obstruction among older smokers.

  15. Volunteering predicts health among those who value others: two national studies.

    Science.gov (United States)

    Poulin, Michael J

    2014-02-01

    The purpose of these studies was to examine the role of positive views of other people in predicting stress-buffering effects of volunteering on mortality and psychological distress. In Study 1, stressful life events, volunteering, and hostile cynicism assessed in a baseline Detroit-area survey (N = 846) predicted survival over a 5-year period, adjusting for relevant covariates. In Study 2, stressful life events, volunteering, and world benevolence beliefs assessed in a baseline national survey (N = 1,157) predicted psychological distress over a 1-year period, adjusting for distress at baseline. In Study 1, a Cox proportional hazard model indicated that for individuals low in cynicism, stress predicted mortality at low levels of volunteering but not at high levels of volunteering. This effect was not present among those high in cynicism. In Study 2, multiple regression analysis revealed that among individuals high in world benevolence beliefs, stress predicted elevated distress at low levels of volunteering but not at high levels of volunteering. This effect was absent for those lower in world benevolence beliefs. Consistent with prior research on helping behavior, these studies indicate that helping behavior can buffer the effects of stress on health. However, the results of these studies indicate that stress-buffering effects of volunteering are limited to individuals with positive views of other people. Not all individuals may benefit from volunteering, and health-promotion efforts seeking to draw on health benefits of helping behavior may need to target their approach accordingly. PsycINFO Database Record (c) 2014 APA, all rights reserved.

  16. Instabilities in the coupled equatorial ocean atmosphere system

    NARCIS (Netherlands)

    Dijkstra, H.A.; Vaart, P.C.F. van der

    1999-01-01

    The large-scale interaction between the ocean and atmosphere is one of the impor- tant factors of natural climate variability.The El-Niño/Southern Oscillation (ENSO) phenomenon in the Tropical Pacific is one of the most prominent examples of climate variability on interannual time scales.ENSO has

  17. Ocean-atmosphere interaction and synoptic weather conditions in ...

    Indian Academy of Sciences (India)

    R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22

    turbances over oceans. On the other hand, these disturbances have an impact on the oceanic mixed layer, causing changes in the SST. This complex feed back process between the sea surface and the atmospheric disturbances is important in deter- mining the life span of the synoptic scale events. (Paul et al 1992). In view ...

  18. Quantifying the drivers of ocean-atmosphere CO2 fluxes

    Science.gov (United States)

    Lauderdale, Jonathan M.; Dutkiewicz, Stephanie; Williams, Richard G.; Follows, Michael J.

    2016-07-01

    A mechanistic framework for quantitatively mapping the regional drivers of air-sea CO2 fluxes at a global scale is developed. The framework evaluates the interplay between (1) surface heat and freshwater fluxes that influence the potential saturated carbon concentration, which depends on changes in sea surface temperature, salinity and alkalinity, (2) a residual, disequilibrium flux influenced by upwelling and entrainment of remineralized carbon- and nutrient-rich waters from the ocean interior, as well as rapid subduction of surface waters, (3) carbon uptake and export by biological activity as both soft tissue and carbonate, and (4) the effect on surface carbon concentrations due to freshwater precipitation or evaporation. In a steady state simulation of a coarse-resolution ocean circulation and biogeochemistry model, the sum of the individually determined components is close to the known total flux of the simulation. The leading order balance, identified in different dynamical regimes, is between the CO2 fluxes driven by surface heat fluxes and a combination of biologically driven carbon uptake and disequilibrium-driven carbon outgassing. The framework is still able to reconstruct simulated fluxes when evaluated using monthly averaged data and takes a form that can be applied consistently in models of different complexity and observations of the ocean. In this way, the framework may reveal differences in the balance of drivers acting across an ensemble of climate model simulations or be applied to an analysis and interpretation of the observed, real-world air-sea flux of CO2.

  19. International Comprehensive Ocean-Atmosphere Data Set (ICOADS)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Surface marine observational records from ships, buoys, and other platform types are processed and binned creating monthly global and regional grids of the...

  20. Biogeochemical ocean-atmosphere transfers in the Arabian Sea

    Digital Repository Service at National Institute of Oceanography (India)

    Naqvi, S.W.A.; Bange, H.W.; Gibb, S.W.; Goyet, C.; Hatton, A.D.; Upstill-Goddard, R.C.

    Transfers of some important biogenic atmospheric constituents, carbon dioxide (CO sub (2)), methane (CH Sub (4)), molecular nitrogen (N sub (2)), nitrous oxide (N sub (2) O), nitrate NO super(-) sub (3) , ammonia (NH sub(3)), methylamines (MAs...

  1. A clinical prediction rule for detecting major depressive disorder in primary care : the PREDICT-NL study

    NARCIS (Netherlands)

    Zuithoff, Nicolaas P A; Vergouwe, Yvonne; King, Michael; Nazareth, Irwin; Hak, Eelko; Moons, Karel G M; Geerlings, Mirjam I

    BACKGROUND: Major depressive disorder often remains unrecognized in primary care. OBJECTIVE: Development of a clinical prediction rule using easily obtainable predictors for major depressive disorder in primary care patients. METHODS: A total of 1046 subjects, aged 18-65 years, were included from

  2. A second study of the prediction of cognitive errors using the 'CREAM' technique

    International Nuclear Information System (INIS)

    Collier, Steve; Andresen, Gisle

    2000-03-01

    Some human errors, such as errors of commission and knowledge-based errors, are not adequately modelled in probabilistic safety assessments. Even qualitative methods for handling these sorts of errors are comparatively underdeveloped. The 'Cognitive Reliability and Error Analysis Method' (CREAM) was recently developed for prediction of cognitive error modes. It has not yet been comprehensively established how reliable, valid and generally useful it could be to researchers and practitioners. A previous study of CREAM at Halden was promising, showing a relationship between errors predicted in advance and those that actually occurred in simulated fault scenarios. The present study continues this work. CREAM was used to make predictions of cognitive error modes throughout two rather difficult fault scenarios. Predictions were made of the most likely cognitive error mode, were one to occur at all, at several points throughout the expected scenarios, based upon the scenario design and description. Each scenario was then run 15 times with different operators. Error modes occurring during simulations were later scored using the task description for the scenario, videotapes of operator actions, eye-track recording, operators' verbal protocols and an expert's concurrent commentary. The scoring team had no previous substantive knowledge of the experiment or the techniques used, so as to provide a more stringent test of the data and knowledge needed for scoring. The scored error modes were then compared with the CREAM predictions to assess the degree of agreement. Some cognitive error modes were predicted successfully, but the results were generally not so encouraging as the previous study. Several problems were found with both the CREAM technique and the data needed to complete the analysis. It was felt that further development was needed before this kind of analysis can be reliable and valid, either in a research setting or as a practitioner's tool in a safety assessment

  3. Prediction-error variance in Bayesian model updating: a comparative study

    Science.gov (United States)

    Asadollahi, Parisa; Li, Jian; Huang, Yong

    2017-04-01

    In Bayesian model updating, the likelihood function is commonly formulated by stochastic embedding in which the maximum information entropy probability model of prediction error variances plays an important role and it is Gaussian distribution subject to the first two moments as constraints. The selection of prediction error variances can be formulated as a model class selection problem, which automatically involves a trade-off between the average data-fit of the model class and the information it extracts from the data. Therefore, it is critical for the robustness in the updating of the structural model especially in the presence of modeling errors. To date, three ways of considering prediction error variances have been seem in the literature: 1) setting constant values empirically, 2) estimating them based on the goodness-of-fit of the measured data, and 3) updating them as uncertain parameters by applying Bayes' Theorem at the model class level. In this paper, the effect of different strategies to deal with the prediction error variances on the model updating performance is investigated explicitly. A six-story shear building model with six uncertain stiffness parameters is employed as an illustrative example. Transitional Markov Chain Monte Carlo is used to draw samples of the posterior probability density function of the structure model parameters as well as the uncertain prediction variances. The different levels of modeling uncertainty and complexity are modeled through three FE models, including a true model, a model with more complexity, and a model with modeling error. Bayesian updating is performed for the three FE models considering the three aforementioned treatments of the prediction error variances. The effect of number of measurements on the model updating performance is also examined in the study. The results are compared based on model class assessment and indicate that updating the prediction error variances as uncertain parameters at the model

  4. General life satisfaction predicts dementia in community living older adults: a prospective cohort study.

    Science.gov (United States)

    Peitsch, Lorraine; Tyas, Suzanne L; Menec, Verena H; St John, Philip D

    2016-07-01

    Low life satisfaction predicts adverse outcomes, and may predict dementia. The objectives were: (1) to determine if life satisfaction predicts dementia over a five year period in those with normal cognition at baseline; and (2) to determine if different aspects of life satisfaction differentially predict dementia. Secondary analysis of an existing population-based cohort study with initial assessment in 1991 and follow-up five years later. Initially, 1,751 adults age 65+ living in the community were sampled from a representative sampling frame. Of these, 1,024 were alive and had complete data at time 2, of whom 96 were diagnosed with dementia. Life satisfaction was measured using the Terrible-Delightful scale, which measures overall life satisfaction on a 7-point scale, as well as various aspects of life satisfaction (e.g. friendships, finances, etc.) Dementia was diagnosed by clinical examination using DSM-IIIR criteria. Logistic regression models were constructed for the outcome of dementia at time 2, and adjusted for age, gender, education, and comorbidities. Overall life satisfaction predicted dementia five years later, at time 2. The unadjusted Odds Ratio (OR; 95% confidence interval) for dementia at time 2 was 0.72 (0.55, 0.95) per point. The adjusted OR for dementia was 0.70 (0.51, 0.96). No individual item on the life satisfaction scale predicted dementia. However, the competing risk of mortality was very high for some items. A global single-item measure of life satisfaction predicts dementia over a five year period in older adults without cognitive impairment.

  5. Classification of baseline toxicants for QSAR predictions to replace fish acute toxicity studies.

    Science.gov (United States)

    Nendza, Monika; Müller, Martin; Wenzel, Andrea

    2017-03-22

    Fish acute toxicity studies are required for environmental hazard and risk assessment of chemicals by national and international legislations such as REACH, the regulations of plant protection products and biocidal products, or the GHS (globally harmonised system) for classification and labelling of chemicals. Alternative methods like QSARs (quantitative structure-activity relationships) can replace many ecotoxicity tests. However, complete substitution of in vivo animal tests by in silico methods may not be realistic. For the so-called baseline toxicants, it is possible to predict the fish acute toxicity with sufficient accuracy from log K ow and, hence, valid QSARs can replace in vivo testing. In contrast, excess toxicants and chemicals not reliably classified as baseline toxicants require further in silico, in vitro or in vivo assessments. Thus, the critical task is to discriminate between baseline and excess toxicants. For fish acute toxicity, we derived a scheme based on structural alerts and physicochemical property thresholds to classify chemicals as either baseline toxicants (=predictable by QSARs) or as potential excess toxicants (=not predictable by baseline QSARs). The step-wise approach identifies baseline toxicants (true negatives) in a precautionary way to avoid false negative predictions. Therefore, a certain fraction of false positives can be tolerated, i.e. baseline toxicants without specific effects that may be tested instead of predicted. Application of the classification scheme to a new heterogeneous dataset for diverse fish species results in 40% baseline toxicants, 24% excess toxicants and 36% compounds not classified. Thus, we can conclude that replacing about half of the fish acute toxicity tests by QSAR predictions is realistic to be achieved in the short-term. The long-term goals are classification criteria also for further groups of toxicants and to replace as many in vivo fish acute toxicity tests as possible with valid QSAR

  6. A predictive validity study of the Learning Style Questionnaire (LSQ) using multiple, specific learning criteria

    NARCIS (Netherlands)

    Kappe, F.R.; Boekholt, L.; den Rooyen, C.; van der Flier, H.

    2009-01-01

    Multiple and specific learning criteria were used to examine the predictive validity of the Learning Style Questionnaire (LSQ). Ninety-nine students in a college of higher learning in The Netherlands participated in a naturally occurring field study. The students were categorized into one of four

  7. Clinical prediction models for bronchopulmonary dysplasia: a systematic review and external validation study

    NARCIS (Netherlands)

    Onland, Wes; Debray, Thomas P.; Laughon, Matthew M.; Miedema, Martijn; Cools, Filip; Askie, Lisa M.; Asselin, Jeanette M.; Calvert, Sandra A.; Courtney, Sherry E.; Dani, Carlo; Durand, David J.; Marlow, Neil; Peacock, Janet L.; Pillow, J. Jane; Soll, Roger F.; Thome, Ulrich H.; Truffert, Patrick; Schreiber, Michael D.; van Reempts, Patrick; Vendettuoli, Valentina; Vento, Giovanni; van Kaam, Anton H.; Moons, Karel G.; Offringa, Martin

    2013-01-01

    Bronchopulmonary dysplasia (BPD) is a common complication of preterm birth. Very different models using clinical parameters at an early postnatal age to predict BPD have been developed with little extensive quantitative validation. The objective of this study is to review and validate clinical

  8. Utility of Hippocrates' prognostic aphorism to predict death in the modern era: prospective cohort study.

    Science.gov (United States)

    St John, Philip D; Montgomery, Patrick R

    2014-12-15

    To determine if one of Hippocrates' aphorisms, identifying good cognition and good appetite as two prognostic factors, predicts death in community living older adults in the modern era. Secondary analysis of an existing population based cohort study. Manitoba Study of Health and Aging. 1751 community living adults aged more than 65 enrolled in the Manitoba Study of Health and Aging in 1991 and followed over five years. Time to death. We recreated the hippocratic prognosticator using an item that measures appetite drawn from the Center for Epidemiologic Studies-depression subscale, and the mini-mental state examination, with a score of >25 being considered as normal. People with normal cognition and appetite were compared with those with either poor cognition or poor appetite. We constructed Cox regression models, adjusted for age, sex, education, and functional status. The prognostic aphorism predicted death, with an unadjusted hazard ratio of 2.37 (95% confidence interval 1.93 to 2.88) and a hazard ratio of 1.71 (1.37 to 2.12) adjusted for age, sex, and education. Both poor appetite and poor cognition predicted death. The sensitivity and specificity were not, however, sufficient for the measure to be used alone. An aphorism devised by Hippocrates millennia ago can predict death in the modern era. © St John et al 2014.

  9. Utility of Hippocrates’ prognostic aphorism to predict death in the modern era: prospective cohort study

    Science.gov (United States)

    Montgomery, Patrick R

    2014-01-01

    Objective To determine if one of Hippocrates’ aphorisms, identifying good cognition and good appetite as two prognostic factors, predicts death in community living older adults in the modern era. Design Secondary analysis of an existing population based cohort study. Setting Manitoba Study of Health and Aging. Participants 1751 community living adults aged more than 65 enrolled in the Manitoba Study of Health and Aging in 1991 and followed over five years. Main outcome measure Time to death. Methods We recreated the hippocratic prognosticator using an item that measures appetite drawn from the Center for Epidemiologic Studies-depression subscale, and the mini-mental state examination, with a score of >25 being considered as normal. People with normal cognition and appetite were compared with those with either poor cognition or poor appetite. We constructed Cox regression models, adjusted for age, sex, education, and functional status. Results The prognostic aphorism predicted death, with an unadjusted hazard ratio of 2.37 (95% confidence interval 1.93 to 2.88) and a hazard ratio of 1.71 (1.37 to 2.12) adjusted for age, sex, and education. Both poor appetite and poor cognition predicted death. The sensitivity and specificity were not, however, sufficient for the measure to be used alone. Conclusion An aphorism devised by Hippocrates millennia ago can predict death in the modern era. PMID:25512328

  10. RAMAN SPECTROSCOPIC STUDY ON PREDICTION OF TREATMENT RESPONSE IN CERVICAL CANCERS

    Directory of Open Access Journals (Sweden)

    S. RUBINA

    2013-04-01

    Full Text Available Concurrent chemoradiotherapy (CCRT is the choice of treatment for locally advanced cervical cancers; however, tumors exhibit diverse response to treatment. Early prediction of tumor response leads to individualizing treatment regimen. Response evaluation criteria in solid tumors (RECIST, the current modality of tumor response assessment, is often subjective and carried out at the first visit after treatment, which is about four months. Hence, there is a need for better predictive tool for radioresponse. Optical spectroscopic techniques, sensitive to molecular alteration, are being pursued as potential diagnostic tools. Present pilot study aims to explore the fiber-optic-based Raman spectroscopy approach in prediction of tumor response to CCRT, before taking up extensive in vivo studies. Ex vivo Raman spectra were acquired from biopsies collected from 11 normal (148 spectra, 16 tumor (201 spectra and 13 complete response (151 CR spectra, one partial response (8 PR spectra and one nonresponder (8 NR spectra subjects. Data was analyzed using principal component linear discriminant analysis (PC-LDA followed by leave-one-out cross-validation (LOO-CV. Findings suggest that normal tissues can be efficiently classified from both pre- and post-treated tumor biopsies, while there is an overlap between pre- and post-CCRT tumor tissues. Spectra of CR, PR and NR tissues were subjected to principal component analysis (PCA and a tendency of classification was observed, corroborating previous studies. Thus, this study further supports the feasibility of Raman spectroscopy in prediction of tumor radioresponse and prospective noninvasive in vivo applications.

  11. A Study on the Prediction of the Teaching Profession Attitudes by Communication Skills and Professional Motivation

    Science.gov (United States)

    Çimen, Latife Kabakli

    2016-01-01

    This study aims to investigate the prediction of the attitudes regarding teaching profession by the communication skills and professional motivation of pedagogical formation students. 261 pre-service teachers receiving pedagogical formation training Istanbul at a private university in the 2014-2015 academic year were included in the research as…

  12. A Cross-Validation Study of Police Recruit Performance as Predicted by the IPI and MMPI.

    Science.gov (United States)

    Shusman, Elizabeth J.; And Others

    Validation and cross-validation studies were conducted using the Minnesota Multiphasic Personality Inventory (MMPI) and Inwald Personality Inventory (IPI) to predict job performance for 698 urban male police officers who completed a six-month training academy. Job performance criteria evaluated included absence, lateness, derelictions, negative…

  13. Data pre-processing: a case study in predicting student's retention in ...

    African Journals Online (AJOL)

    dataset with features that are ready for data mining task. The study also proposed a process model and suggestions, which can be applied to support more comprehensible tools for educational domain who is the end user. Subsequently, the data pre-processing become more efficient for predicting student's retention in ...

  14. Developing a Model and Applications for Probabilities of Student Success: A Case Study of Predictive Analytics

    Science.gov (United States)

    Calvert, Carol Elaine

    2014-01-01

    This case study relates to distance learning students on open access courses. It demonstrates the use of predictive analytics to generate a model of the probabilities of success and retention at different points, or milestones, in a student journey. A core set of explanatory variables has been established and their varying relative importance at…

  15. Predicting Dyslexia in a Transparent Orthography from Grade 1 Literacy Skills: A Prospective Cohort Study

    Science.gov (United States)

    Bigozzi, Lucia; Tarchi, Christian; Pinto, Giuliana; Accorti Gamannossi, Beatrice

    2016-01-01

    We conducted this prospective cohort study to explore the predictability of dyslexia from 1st-grade literacy skills in Italian students. We followed 407 Italian students in primary school from the 1st through the 3rd grades. Students were diagnosed with dyslexia in the 3rd grade. We retrospectively tested participants' 1st-grade performance in…

  16. Overview of data-synthesis in systematic reviews of studies on outcome prediction models

    NARCIS (Netherlands)

    T. van den Berg (Tobias); M.W. Heymans (Martijn); O. Leone; D. Vergouw (David); J. Hayden (Jill); A.P. Verhagen (Arianne); H.C. de Vet (Henrica C)

    2013-01-01

    textabstractBackground: Many prognostic models have been developed. Different types of models, i.e. prognostic factor and outcome prediction studies, serve different purposes, which should be reflected in how the results are summarized in reviews. Therefore we set out to investigate how authors of

  17. Early Findings of Preventive Child Healthcare Professionals Predict Psychosocial Problems in Preadolescence : The TRAILS Study

    NARCIS (Netherlands)

    Jaspers, M.; de Winter, A.F.; de Meer, G.; Stewart, R.E.; Verhulst, F.C.; Ormel, J.; Reijneveld, S.A.

    Objective To develop and validate a prediction model for psychosocial problems in preadolescence using data on early developmental factors from routine Preventive Child Healthcare (PCH). Study design The data come from the 1692 participants who take part in the TRacking Adolescents' Individual Lives

  18. Early Findings of Preventive Child Healthcare Professionals Predict Psychosocial Problems in Preadolescence: The TRAILS Study

    NARCIS (Netherlands)

    Jaspers, M.; De Winter, A.F.; de Meer, G.; Stewart, R.E; Verhulst, F.C.; Ormel, J.; Reijneveld, S.A.

    2010-01-01

    Objective To develop and validate a prediction model for psychosocial problems in preadolescence using data on early developmental factors from routine Preventive Child Healthcare (PCH). Study design The data come from the 1692 participants who take part in the TRacking Adolescents' Individual Lives

  19. Individualized prediction of illness course at the first psychotic episode: a support vector machine MRI study.

    LENUS (Irish Health Repository)

    Mourao-Miranda, J

    2012-05-01

    To date, magnetic resonance imaging (MRI) has made little impact on the diagnosis and monitoring of psychoses in individual patients. In this study, we used a support vector machine (SVM) whole-brain classification approach to predict future illness course at the individual level from MRI data obtained at the first psychotic episode.

  20. Nomograms for predicting survival and recurrence in patients with adenoid cystic carcinoma. An international collaborative study

    DEFF Research Database (Denmark)

    Ganly, Ian; Amit, Moran; Kou, Lei

    2015-01-01

    BACKGROUND: Due to the rarity of adenoid cystic carcinoma (ACC), information on outcome is based upon small retrospective case series. The aim of our study was to create a large multiinstitutional international dataset of patients with ACC in order to design predictive nomograms for outcome. METH...

  1. Predicting Early School Achievement with the EDI: A Longitudinal Population-Based Study

    Science.gov (United States)

    Forget-Dubois, Nadine; Lemelin, Jean-Pascal; Boivin, Michel; Dionne, Ginette; Seguin, Jean R.; Vitaro, Frank; Tremblay, Richard E.

    2007-01-01

    School readiness tests are significant predictors of early school achievement. Measuring school readiness on a large scale would be necessary for the implementation of intervention programs at the community level. However, assessment of school readiness is costly and time consuming. This study assesses the predictive value of a school readiness…

  2. Mentoring Support and Power: A Three Year Predictive Field Study on Protege Networking and Career Success

    Science.gov (United States)

    Blickle, Gerhard; Witzki, Alexander H.; Schneider, Paula B.

    2009-01-01

    Career success of early employees was analyzed from a power perspective and a developmental network perspective. In a predictive field study with 112 employees mentoring support and mentors' power were assessed in the first wave, employees' networking was assessed after two years, and career success (i.e. income and hierarchical position) and…

  3. What Factors are Predictive of Patient-reported Outcomes? A Prospective Study of 337 Shoulder Arthroplasties.

    Science.gov (United States)

    Matsen, Frederick A; Russ, Stacy M; Vu, Phuong T; Hsu, Jason E; Lucas, Robert M; Comstock, Bryan A

    2016-11-01

    Although shoulder arthroplasties generally are effective in improving patients' comfort and function, the results are variable for reasons that are not well understood. We posed two questions: (1) What factors are associated with better 2-year outcomes after shoulder arthroplasty? (2) What are the sensitivities, specificities, and positive and negative predictive values of a multivariate predictive model for better outcome? Three hundred thirty-nine patients having a shoulder arthroplasty (hemiarthroplasty, arthroplasty for cuff tear arthropathy, ream and run arthroplasty, total shoulder or reverse total shoulder arthroplasty) between August 24, 2010 and December 31, 2012 consented to participate in this prospective study. Two patients were excluded because they were missing baseline variables. Forty-three patients were missing 2-year data. Univariate and multivariate analyses determined the relationship of baseline patient, shoulder, and surgical characteristics to a "better" outcome, defined as an improvement of at least 30% of the maximal possible improvement in the Simple Shoulder Test. The results were used to develop a predictive model, the accuracy of which was tested using a 10-fold cross-validation. After controlling for potentially relevant confounding variables, the multivariate analysis showed that the factors significantly associated with better outcomes were American Society of Anesthesiologists Class I (odds ratio [OR], 1.94; 95% CI, 1.03-3.65; p = 0.041), shoulder problem not related to work (OR, 5.36; 95% CI, 2.15-13.37; p factors listed above. The area under the receiver operating characteristic curve generated from the cross-validated enhanced predictive model was 0.79 (generally values of 0.7 to 0.8 are considered fair and values of 0.8 to 0.9 are considered good). The false-positive fraction and the true-positive fraction depended on the cutoff probability selected (ie, the selected probability above which the prediction would be classified as

  4. A study of fatigue life prediction for automotive spot weldment using local strain approach

    International Nuclear Information System (INIS)

    Lee, Song In; Yu, Hyo Sun; Na, Sung Hun; Na, Eui Gyun

    2000-01-01

    The fatigue crack initiation life is studied on automotive spot weldment made from cold rolled carbon steel(SPC) sheet by using DCPDM and local strain approach. It can be found that the fatigue crack initiation behavior in spot weldment can be definitely detected by DCPDM system. The local stresses and strains are estimated by elastic-plastic FEM analysis and the alternative approximate method based on Neuber's rule were applied to predict the fatigue life of spot weldment. A satisfactory correlation between the predicted life and experimental life can be found in spot weldment within a factor of 4

  5. Prediction of clearance, volume of distribution and half-life by allometric scaling and by use of plasma concentrations predicted from pharmacokinetic constants: a comparative study.

    Science.gov (United States)

    Mahmood, I

    1999-08-01

    Pharmacokinetic parameters (clearance, CL, volume of distribution in the central compartment, VdC, and elimination half-life, t1/2beta) predicted by an empirical allometric approach have been compared with parameters predicted from plasma concentrations calculated by use of the pharmacokinetic constants A, B, alpha and beta, where A and B are the intercepts on the Y axis of the plot of plasma concentration against time and alpha and beta are the rate constants, both pairs of constants being for the distribution and elimination phases, respectively. The pharmacokinetic parameters of cefpiramide, actisomide, troglitazone, procaterol, moxalactam and ciprofloxacin were scaled from animal data obtained from the literature. Three methods were used to generate plots for the prediction of clearance in man: dependence of clearance on body weight (simple allometric equation); dependence of the product of clearance and maximum life-span potential (MLP) on body weight; and dependence of the product of clearance and brain weight on body weight. Plasma concentrations of the drugs were predicted in man by use of A, B, alpha and beta obtained from animal data. The predicted plasma concentrations were then used to calculate CL, VdC and t1/2beta. The pharmacokinetic parameters predicted by use of both approaches were compared with measured values. The results indicate that simple allometry did not predict clearance satisfactorily for actisomide, troglitazone, procaterol and ciprofloxacin. Use of MLP or the product of clearance and brain weight improved the prediction of clearance for these four drugs. Except for troglitazone, VdC and t1/2beta predicted for man by use of the allometric approach were comparable with measured values for the drugs studied. CL, VdC and t1/2beta predicted by use of pharmacokinetic constants were comparable with values predicted by simple allometry. Thus, if simple allometry failed to predict clearance of a drug, so did the pharmacokinetic constant

  6. STUDY OF SOLUTION REPRESENTATION LANGUAGE INFLUENCE ON EFFICIENCY OF INTEGER SEQUENCES PREDICTION

    Directory of Open Access Journals (Sweden)

    A. S. Potapov

    2015-01-01

    Full Text Available Methods based on genetic programming for the problem solution of integer sequences extrapolation are the subjects for study in the paper. In order to check the hypothesis about the influence of language expression of program representation on the prediction effectiveness, the genetic programming method based on several limited languages for recurrent sequences has been developed. On the single sequence sample the implemented method with the use of more complete language has shown results, significantly better than the results of one of the current methods represented in literature based on artificial neural networks. Analysis of experimental comparison results for the realized method with the usage of different languages has shown that language extension increases the difficulty of consistent patterns search in languages, available for prediction in a simpler language though it makes new sequence classes accessible for prediction. This effect can be reduced but not eliminated completely at language extension by the constructions, which make solutions more compact. Carried out researches have drawn to the conclusion that alone the choice of an adequate language for solution representation is not enough for the full problem solution of integer sequences prediction (and, all the more, universal prediction problem. However, practically applied methods can be received by the usage of genetic programming.

  7. A comparative study on prediction methods for China's medium- and long-term coal demand

    International Nuclear Information System (INIS)

    Li, Bing-Bing; Liang, Qiao-Mei; Wang, Jin-Cheng

    2015-01-01

    Given the dominant position of coal in China's energy structure and in order to ensure a safe and stable energy supply, it is essential to perform a scientific and effective prediction of China's medium- and long-term coal demand. Based on the historical data of coal consumption and related factors such as GDP (Gross domestic product), coal price, industrial structure, total population, energy structure, energy efficiency, coal production and urbanization rate from 1987 to 2012, this study compared the prediction effects of five types of models. These models include the VAR (vector autoregressive model), RBF (radial basis function) neural network model, GA-DEM (genetic algorithm demand estimation model), PSO-DEM (particle swarm optimization demand estimation model) and IO (input–output model). By comparing the results of different models with the corresponding actual coal consumption, it is concluded that with a testing period from 2006 to 2012, the PSO-DEM model has a relatively optimal predicted effect on China's total coal demand, where the MAPE (mean absolute percentage error) is close to or below 2%. - Highlights: • The prediction effects of five methods for China's coal demand were compared. • Each model has acceptable prediction results, with MAPE below 5%. • Particle swarm optimization demand estimation model has better forecast efficacy.

  8. A Comparative Study to Predict Student’s Performance Using Educational Data Mining Techniques

    Science.gov (United States)

    Uswatun Khasanah, Annisa; Harwati

    2017-06-01

    Student’s performance prediction is essential to be conducted for a university to prevent student fail. Number of student drop out is one of parameter that can be used to measure student performance and one important point that must be evaluated in Indonesia university accreditation. Data Mining has been widely used to predict student’s performance, and data mining that applied in this field usually called as Educational Data Mining. This study conducted Feature Selection to select high influence attributes with student performance in Department of Industrial Engineering Universitas Islam Indonesia. Then, two popular classification algorithm, Bayesian Network and Decision Tree, were implemented and compared to know the best prediction result. The outcome showed that student’s attendance and GPA in the first semester were in the top rank from all Feature Selection methods, and Bayesian Network is outperforming Decision Tree since it has higher accuracy rate.

  9. Natural gas consumption prediction in Slovenian industry – a case study

    Directory of Open Access Journals (Sweden)

    Kovačič Miha

    2016-09-01

    Full Text Available In accordance with the regulations of the Energy Agency of the Republic of Slovenia, each natural gas supplier regulates and determines the charges for the differences between the ordered (predicted and the actually supplied quantities of natural gas. Yearly charges for these differences represent up to 2% of supplied natural gas costs. All the natural gas users, especially industry, have huge problems finding the proper method for efficient natural gas consumption prediction and, consequently, the decreasing of mentioned costs. In this study, prediction of the natural gas consumption in Štore Steel Ltd. (steel plant is presented. On the basis of production data, several models for natural gas consumption have been developed using linear regression, genetic programming and artificial neural network methods. The genetic programming approach outperformed linear regression and artificial neural networks.

  10. Field studies of submerged-diffuser thermal plumes with comparisons to predictive model results

    International Nuclear Information System (INIS)

    Frigo, A.A.; Paddock, R.A.; Ditmars, J.D.

    1976-01-01

    Thermal plumes from submerged discharges of cooling water from two power plants on Lake Michigan were studied. The system for the acquisition of water temperatures and ambient conditions permitted the three-dimensional structure of the plumes to be determined. The Zion Nuclear Power Station has two submerged discharge structures separated by only 94 m. Under conditions of flow from both structures, interaction between the two plumes resulted in larger thermal fields than would be predicted by the superposition of single non-interacting plumes. Maximum temperatures in the near-field region of the plume compared favorably with mathematical model predictions. A comparison of physical-model predictions for the plume at the D. C. Cook Nuclear Plant with prototype measurements indicated good agreement in the near-field region, but differences in the far-field occurred as similitude was not preserved there

  11. Predictive validity of the Biomedical Admissions Test: an evaluation and case study.

    Science.gov (United States)

    McManus, I C; Ferguson, Eamonn; Wakeford, Richard; Powis, David; James, David

    2011-01-01

    There has been an increase in the use of pre-admission selection tests for medicine. Such tests need to show good psychometric properties. Here, we use a paper by Emery and Bell [2009. The predictive validity of the Biomedical Admissions Test for pre-clinical examination performance. Med Educ 43:557-564] as a case study to evaluate and comment on the reporting of psychometric data in the field of medical student selection (and the comments apply to many papers in the field). We highlight pitfalls when reliability data are not presented, how simple zero-order associations can lead to inaccurate conclusions about the predictive validity of a test, and how biases need to be explored and reported. We show with BMAT that it is the knowledge part of the test which does all the predictive work. We show that without evidence of incremental validity it is difficult to assess the value of any selection tests for medicine.

  12. Measurement of predictive validity in violence risk assessment studies: a second-order systematic review.

    Science.gov (United States)

    Singh, Jay P; Desmarais, Sarah L; Van Dorn, Richard A

    2013-01-01

    The objective of the present review was to examine how predictive validity is analyzed and reported in studies of instruments used to assess violence risk. We reviewed 47 predictive validity studies published between 1990 and 2011 of 25 instruments that were included in two recent systematic reviews. Although all studies reported receiver operating characteristic curve analyses and the area under the curve (AUC) performance indicator, this methodology was defined inconsistently and findings often were misinterpreted. In addition, there was between-study variation in benchmarks used to determine whether AUCs were small, moderate, or large in magnitude. Though virtually all of the included instruments were designed to produce categorical estimates of risk - through the use of either actuarial risk bins or structured professional judgments - only a minority of studies calculated performance indicators for these categorical estimates. In addition to AUCs, other performance indicators, such as correlation coefficients, were reported in 60% of studies, but were infrequently defined or interpreted. An investigation of sources of heterogeneity did not reveal significant variation in reporting practices as a function of risk assessment approach (actuarial vs. structured professional judgment), study authorship, geographic location, type of journal (general vs. specialized audience), sample size, or year of publication. Findings suggest a need for standardization of predictive validity reporting to improve comparison across studies and instruments. Copyright © 2013 John Wiley & Sons, Ltd.

  13. Comparisons of prediction models of quality of life after laparoscopic cholecystectomy: a longitudinal prospective study.

    Directory of Open Access Journals (Sweden)

    Hon-Yi Shi

    Full Text Available BACKGROUND: Few studies of laparoscopic cholecystectomy (LC outcome have used longitudinal data for more than two years. Moreover, no studies have considered group differences in factors other than outcome such as age and nonsurgical treatment. Additionally, almost all published articles agree that the essential issue of the internal validity (reproducibility of the artificial neural network (ANN, support vector machine (SVM, Gaussian process regression (GPR and multiple linear regression (MLR models has not been adequately addressed. This study proposed to validate the use of these models for predicting quality of life (QOL after LC and to compare the predictive capability of ANNs with that of SVM, GPR and MLR. METHODOLOGY/PRINCIPAL FINDINGS: A total of 400 LC patients completed the SF-36 and the Gastrointestinal Quality of Life Index at baseline and at 2 years postoperatively. The criteria for evaluating the accuracy of the system models were mean square error (MSE and mean absolute percentage error (MAPE. A global sensitivity analysis was also performed to assess the relative significance of input parameters in the system model and to rank the variables in order of importance. Compared to SVM, GPR and MLR models, the ANN model generally had smaller MSE and MAPE values in the training data set and test data set. Most ANN models had MAPE values ranging from 4.20% to 8.60%, and most had high prediction accuracy. The global sensitivity analysis also showed that preoperative functional status was the best parameter for predicting QOL after LC. CONCLUSIONS/SIGNIFICANCE: Compared with SVM, GPR and MLR models, the ANN model in this study was more accurate in predicting patient-reported QOL and had higher overall performance indices. Further studies of this model may consider the effect of a more detailed database that includes complications and clinical examination findings as well as more detailed outcome data.

  14. A Study on Re-entry Predictions of Uncontrolled Space Objects for Space Situational Awareness

    Science.gov (United States)

    Choi, Eun-Jung; Cho, Sungki; Lee, Deok-Jin; Kim, Siwoo; Jo, Jung Hyun

    2017-12-01

    The key risk analysis technologies for the re-entry of space objects into Earth’s atmosphere are divided into four categories: cataloguing and databases of the re-entry of space objects, lifetime and re-entry trajectory predictions, break-up models after re-entry and multiple debris distribution predictions, and ground impact probability models. In this study, we focused on re- entry prediction, including orbital lifetime assessments, for space situational awareness systems. Re-entry predictions are very difficult and are affected by various sources of uncertainty. In particular, during uncontrolled re-entry, large spacecraft may break into several pieces of debris, and the surviving fragments can be a significant hazard for persons and properties on the ground. In recent years, specific methods and procedures have been developed to provide clear information for predicting and analyzing the re-entry of space objects and for ground-risk assessments. Representative tools include object reentry survival analysis tool (ORSAT) and debris assessment software (DAS) developed by National Aeronautics and Space Administration (NASA), spacecraft atmospheric re-entry and aerothermal break-up (SCARAB) and debris risk assessment and mitigation analysis (DRAMA) developed by European Space Agency (ESA), and semi-analytic tool for end of life analysis (STELA) developed by Centre National d’Etudes Spatiales (CNES). In this study, various surveys of existing re-entry space objects are reviewed, and an efficient re-entry prediction technique is suggested based on STELA, the life-cycle analysis tool for satellites, and DRAMA, a re-entry analysis tool. To verify the proposed method, the re-entry of the Tiangong-1 Space Lab, which is expected to re-enter Earth’s atmosphere shortly, was simulated. Eventually, these results will provide a basis for space situational awareness risk analyses of the re-entry of space objects.

  15. Predicting severe dengue using quantified warning signs. A retrospective cohort study

    Directory of Open Access Journals (Sweden)

    Gary Low Kim Kuan

    2015-09-01

    Full Text Available Objective: To develop and evaluate predictive models by quantifying warning signs prior to the development of severe dengue. Methods: A retrospective cohort study was conducted in which the total number of warning signs each day was compared between dengue with warning signs and severe dengue. Multivariate logistic regression with forward likelihood ratio method was employed to achieve the best fit models for the prediction of severe dengue. The models were also being explored by adding diarrhoea and removing lethargy. Receiver operating characteristics were then used in these best fit models to identify suitable cut-off probability values derived from the equation of the models. Results: Median age of patients was 26 years old (interquartile range was 15 years and 65.3% (1 110 were males. Age with total number of warning signs at day one of illness (model T1 and age with total number of warning signs at day two of illness (model T2 were identified as the best fit models. The best probability cut-offs for model T1 was 0.050 6 with 10.1% positive predictive value, 96.4% negative predictive value, 99.4% sensitivity, 1.8% specificity; for model T2 was 0.050 3 with 10.2% positive predictive value, 96.4% negative predictive value, 99.4% sensitivity, 1.8% specificity. Conclusions: The models developed in this study might not reduce the burden effectively. Clinicians may use the models but the models must be re-validated in their clinical settings as the effect size might vary. Furthermore, the risk and benefit in selecting the cut-off values should be evaluated before implementing such models.

  16. An integrative approach to ortholog prediction for disease-focused and other functional studies

    Directory of Open Access Journals (Sweden)

    Perrimon Norbert

    2011-08-01

    Full Text Available Abstract Background Mapping of orthologous genes among species serves an important role in functional genomics by allowing researchers to develop hypotheses about gene function in one species based on what is known about the functions of orthologs in other species. Several tools for predicting orthologous gene relationships are available. However, these tools can give different results and identification of predicted orthologs is not always straightforward. Results We report a simple but effective tool, the Drosophila RNAi Screening Center Integrative Ortholog Prediction Tool (DIOPT; http://www.flyrnai.org/diopt, for rapid identification of orthologs. DIOPT integrates existing approaches, facilitating rapid identification of orthologs among human, mouse, zebrafish, C. elegans, Drosophila, and S. cerevisiae. As compared to individual tools, DIOPT shows increased sensitivity with only a modest decrease in specificity. Moreover, the flexibility built into the DIOPT graphical user interface allows researchers with different goals to appropriately 'cast a wide net' or limit results to highest confidence predictions. DIOPT also displays protein and domain alignments, including percent amino acid identity, for predicted ortholog pairs. This helps users identify the most appropriate matches among multiple possible orthologs. To facilitate using model organisms for functional analysis of human disease-associated genes, we used DIOPT to predict high-confidence orthologs of disease genes in Online Mendelian Inheritance in Man (OMIM and genes in genome-wide association study (GWAS data sets. The results are accessible through the DIOPT diseases and traits query tool (DIOPT-DIST; http://www.flyrnai.org/diopt-dist. Conclusions DIOPT and DIOPT-DIST are useful resources for researchers working with model organisms, especially those who are interested in exploiting model organisms such as Drosophila to study the functions of human disease genes.

  17. An integrative approach to ortholog prediction for disease-focused and other functional studies.

    Science.gov (United States)

    Hu, Yanhui; Flockhart, Ian; Vinayagam, Arunachalam; Bergwitz, Clemens; Berger, Bonnie; Perrimon, Norbert; Mohr, Stephanie E

    2011-08-31

    Mapping of orthologous genes among species serves an important role in functional genomics by allowing researchers to develop hypotheses about gene function in one species based on what is known about the functions of orthologs in other species. Several tools for predicting orthologous gene relationships are available. However, these tools can give different results and identification of predicted orthologs is not always straightforward. We report a simple but effective tool, the Drosophila RNAi Screening Center Integrative Ortholog Prediction Tool (DIOPT; http://www.flyrnai.org/diopt), for rapid identification of orthologs. DIOPT integrates existing approaches, facilitating rapid identification of orthologs among human, mouse, zebrafish, C. elegans, Drosophila, and S. cerevisiae. As compared to individual tools, DIOPT shows increased sensitivity with only a modest decrease in specificity. Moreover, the flexibility built into the DIOPT graphical user interface allows researchers with different goals to appropriately 'cast a wide net' or limit results to highest confidence predictions. DIOPT also displays protein and domain alignments, including percent amino acid identity, for predicted ortholog pairs. This helps users identify the most appropriate matches among multiple possible orthologs. To facilitate using model organisms for functional analysis of human disease-associated genes, we used DIOPT to predict high-confidence orthologs of disease genes in Online Mendelian Inheritance in Man (OMIM) and genes in genome-wide association study (GWAS) data sets. The results are accessible through the DIOPT diseases and traits query tool (DIOPT-DIST; http://www.flyrnai.org/diopt-dist). DIOPT and DIOPT-DIST are useful resources for researchers working with model organisms, especially those who are interested in exploiting model organisms such as Drosophila to study the functions of human disease genes.

  18. Vaginal birth after caesarean section prediction models: a UK comparative observational study.

    Science.gov (United States)

    Mone, Fionnuala; Harrity, Conor; Mackie, Adam; Segurado, Ricardo; Toner, Brenda; McCormick, Timothy R; Currie, Aoife; McAuliffe, Fionnuala M

    2015-10-01

    Primarily, to assess the performance of three statistical models in predicting successful vaginal birth in patients attempting a trial of labour after one previous lower segment caesarean section (TOLAC). The statistically most reliable models were subsequently subjected to validation testing in a local antenatal population. A retrospective observational study was performed with study data collected from the Northern Ireland Maternity Service Database (NIMATs). The study population included all women that underwent a TOLAC (n=385) from 2010 to 2012 in a regional UK obstetric unit. Data was collected from the Northern Ireland Maternity Service Database (NIMATs). Area under the curve (AUC) and correlation analysis was performed. Of the three prediction models evaluated, AUC calculations for the Smith et al., Grobman et al. and Troyer and Parisi Models were 0.74, 0.72 and 0.65, respectively. Using the Smith et al. model, 52% of women had a low risk of caesarean section (CS) (predicted VBAC >72%) and 20% had a high risk of CS (predicted VBAC <60%), of whom 20% and 63% had delivery by CS. The fit between observed and predicted outcome in this study cohort using the Smith et al. and Grobman et al. models were greatest (Chi-square test, p=0.228 and 0.904), validating both within the population. The Smith et al. and Grobman et al. models could potentially be utilized within the UK to provide women with an informed choice when deciding on mode of delivery after a previous CS. Crown Copyright © 2015. Published by Elsevier Ireland Ltd. All rights reserved.

  19. Genome Wide Association Study to predict severe asthma exacerbations in children using random forests classifiers

    Directory of Open Access Journals (Sweden)

    Litonjua Augusto A

    2011-06-01

    Full Text Available Abstract Background Personalized health-care promises tailored health-care solutions to individual patients based on their genetic background and/or environmental exposure history. To date, disease prediction has been based on a few environmental factors and/or single nucleotide polymorphisms (SNPs, while complex diseases are usually affected by many genetic and environmental factors with each factor contributing a small portion to the outcome. We hypothesized that the use of random forests classifiers to select SNPs would result in an improved predictive model of asthma exacerbations. We tested this hypothesis in a population of childhood asthmatics. Methods In this study, using emergency room visits or hospitalizations as the definition of a severe asthma exacerbation, we first identified a list of top Genome Wide Association Study (GWAS SNPs ranked by Random Forests (RF importance score for the CAMP (Childhood Asthma Management Program population of 127 exacerbation cases and 290 non-exacerbation controls. We predict severe asthma exacerbations using the top 10 to 320 SNPs together with age, sex, pre-bronchodilator FEV1 percentage predicted, and treatment group. Results Testing in an independent set of the CAMP population shows that severe asthma exacerbations can be predicted with an Area Under the Curve (AUC = 0.66 with 160-320 SNPs in comparison to an AUC score of 0.57 with 10 SNPs. Using the clinical traits alone yielded AUC score of 0.54, suggesting the phenotype is affected by genetic as well as environmental factors. Conclusions Our study shows that a random forests algorithm can effectively extract and use the information contained in a small number of samples. Random forests, and other machine learning tools, can be used with GWAS studies to integrate large numbers of predictors simultaneously.

  20. Hopefulness predicts resilience after hereditary colorectal cancer genetic testing: a prospective outcome trajectories study

    OpenAIRE

    Chu Annie TW; Bonanno George A; Ho Judy WC; Ho Samuel MY; Chan Emily MS

    2010-01-01

    Abstract Background - Genetic testing for hereditary colorectal cancer (HCRC) had significant psychological consequences for test recipients. This prospective longitudinal study investigated the factors that predict psychological resilience in adults undergoing genetic testing for HCRC. Methods - A longitudinal study was carried out from April 2003 to August 2006 on Hong Kong Chinese HCRC family members who were recruited and offered genetic testing by the Hereditary Gastrointestinal Cancer R...

  1. A study of the importance of occupancy to building cooling load in prediction by intelligent approach

    International Nuclear Information System (INIS)

    Kwok, Simon S.K.; Lee, Eric W.M.

    2011-01-01

    Research highlights: → The building occupancy affecting the cooling load prediction is studied. → PENN model is adopted in this study for predicting the building cooling load. → Statistical approach is adopted to result a less prejudice prediction performance. → Results show that occupancy data can significantly improve the prediction. -- Abstract: Building cooling load prediction is one of the key factors in the success of energy-saving measures. Many computational models available in the industry today have been developed from either forward or inverse modeling approaches. However, most of these models require extensive computer resources and involve lengthy computation. This paper discusses the use of data-driven intelligent approaches, a probabilistic entropy-based neural (PENN) model to predict the cooling load of a building. Although it is common knowledge that the presence and activity of building occupants have a significant impact on the required cooling load of buildings, practices currently adopted in modeling the presence and activity of people in buildings do not reflect the complexity of the impact occupants have on building cooling load. In contrast to previous artificial neural network (ANN) models, most of which employ a fixed schedule or historic load data to represent building occupancy in simulating building cooling load, this paper introduces two input parameters, dynamic occupancy area and rate and uses it to mimic building cooling load. The training samples used include weather data obtained from the Hong Kong Observatory and building-related data acquired from an existing grade A mega office buildings in Hong Kong with tenants including many multi-national financial companies that require 24-h air conditioning seven days a week. The dynamic changes that occur in the occupancy of these buildings therefore make it very difficult to forecast building cooling load by means of a fixed time schedule. The performance of simulation results

  2. A study of the importance of occupancy to building cooling load in prediction by intelligent approach

    Energy Technology Data Exchange (ETDEWEB)

    Kwok, Simon S.K. [Department of Building and Construction, City University of Hong Kong, Tat Chee Avenue, Kowloon Tong (Hong Kong); Lee, Eric W.M., E-mail: ericlee@cityu.edu.h [Department of Building and Construction, City University of Hong Kong, Tat Chee Avenue, Kowloon Tong (Hong Kong)

    2011-07-15

    Research highlights: {yields} The building occupancy affecting the cooling load prediction is studied. {yields} PENN model is adopted in this study for predicting the building cooling load. {yields} Statistical approach is adopted to result a less prejudice prediction performance. {yields} Results show that occupancy data can significantly improve the prediction. -- Abstract: Building cooling load prediction is one of the key factors in the success of energy-saving measures. Many computational models available in the industry today have been developed from either forward or inverse modeling approaches. However, most of these models require extensive computer resources and involve lengthy computation. This paper discusses the use of data-driven intelligent approaches, a probabilistic entropy-based neural (PENN) model to predict the cooling load of a building. Although it is common knowledge that the presence and activity of building occupants have a significant impact on the required cooling load of buildings, practices currently adopted in modeling the presence and activity of people in buildings do not reflect the complexity of the impact occupants have on building cooling load. In contrast to previous artificial neural network (ANN) models, most of which employ a fixed schedule or historic load data to represent building occupancy in simulating building cooling load, this paper introduces two input parameters, dynamic occupancy area and rate and uses it to mimic building cooling load. The training samples used include weather data obtained from the Hong Kong Observatory and building-related data acquired from an existing grade A mega office buildings in Hong Kong with tenants including many multi-national financial companies that require 24-h air conditioning seven days a week. The dynamic changes that occur in the occupancy of these buildings therefore make it very difficult to forecast building cooling load by means of a fixed time schedule. The performance of

  3. Predicting survival time in noncurative patients with advanced cancer: a prospective study in China.

    Science.gov (United States)

    Cui, Jing; Zhou, Lingjun; Wee, B; Shen, Fengping; Ma, Xiuqiang; Zhao, Jijun

    2014-05-01

    Accurate prediction of prognosis for cancer patients is important for good clinical decision making in therapeutic and care strategies. The application of prognostic tools and indicators could improve prediction accuracy. This study aimed to develop a new prognostic scale to predict survival time of advanced cancer patients in China. We prospectively collected items that we anticipated might influence survival time of advanced cancer patients. Participants were recruited from 12 hospitals in Shanghai, China. We collected data including demographic information, clinical symptoms and signs, and biochemical test results. Log-rank tests, Cox regression, and linear regression were performed to develop a prognostic scale. Three hundred twenty patients with advanced cancer were recruited. Fourteen prognostic factors were included in the prognostic scale: Karnofsky Performance Scale (KPS) score, pain, ascites, hydrothorax, edema, delirium, cachexia, white blood cell (WBC) count, hemoglobin, sodium, total bilirubin, direct bilirubin, aspartate aminotransferase (AST), and alkaline phosphatase (ALP) values. The score was calculated by summing the partial scores, ranging from 0 to 30. When using the cutoff points of 7-day, 30-day, 90-day, and 180-day survival time, the scores were calculated as 12, 10, 8, and 6, respectively. We propose a new prognostic scale including KPS, pain, ascites, hydrothorax, edema, delirium, cachexia, WBC count, hemoglobin, sodium, total bilirubin, direct bilirubin, AST, and ALP values, which may help guide physicians in predicting the likely survival time of cancer patients more accurately. More studies are needed to validate this scale in the future.

  4. Predictive values of thermal and electrical dental pulp tests: a clinical study.

    Science.gov (United States)

    Villa-Chávez, Carlos E; Patiño-Marín, Nuria; Loyola-Rodríguez, Juan P; Zavala-Alonso, Norma V; Martínez-Castañón, Gabriel A; Medina-Solís, Carlo E

    2013-08-01

    For a diagnostic test to be useful, it is necessary to determine the probability that the test will provide the correct diagnosis. Therefore, it is necessary to calculate the predictive value of diagnostics. The aim of the present study was to identify the sensitivity, specificity, positive and negative predictive values, accuracy, and reproducibility of thermal and electrical tests of pulp sensitivity. The thermal tests studied were the 1, 1, 1, 2-tetrafluoroethane (cold) and hot gutta-percha (hot) tests. For the electrical test, the Analytic Technology Pulp Tester (Analytic Technology, Redmond, WA) was used. A total of 110 teeth were tested: 60 teeth with vital pulp and 50 teeth with necrotic pulps (disease prevalence of 45%). The ideal standard was established by direct pulp inspection. The sensitivities of the diagnostic tests were 0.88 for the cold test, 0.86 for the heat test, and 0.76 for the electrical test, and the specificity was 1.0 for all 3 tests. The negative predictive value was 0.90 for the cold test, 0.89 for the heat test, and 0.83 for the electrical test, and the positive predictive value was 1.0 for all 3 tests. The highest accuracy (0.94) and reproducibility (0.88) were observed for the cold test. The cold test was the most accurate method for diagnostic testing. Copyright © 2013 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

  5. Predicting smear negative pulmonary tuberculosis with classification trees and logistic regression: a cross-sectional study

    Directory of Open Access Journals (Sweden)

    Kritski Afrânio

    2006-02-01

    Full Text Available Abstract Background Smear negative pulmonary tuberculosis (SNPT accounts for 30% of pulmonary tuberculosis cases reported yearly in Brazil. This study aimed to develop a prediction model for SNPT for outpatients in areas with scarce resources. Methods The study enrolled 551 patients with clinical-radiological suspicion of SNPT, in Rio de Janeiro, Brazil. The original data was divided into two equivalent samples for generation and validation of the prediction models. Symptoms, physical signs and chest X-rays were used for constructing logistic regression and classification and regression tree models. From the logistic regression, we generated a clinical and radiological prediction score. The area under the receiver operator characteristic curve, sensitivity, and specificity were used to evaluate the model's performance in both generation and validation samples. Results It was possible to generate predictive models for SNPT with sensitivity ranging from 64% to 71% and specificity ranging from 58% to 76%. Conclusion The results suggest that those models might be useful as screening tools for estimating the risk of SNPT, optimizing the utilization of more expensive tests, and avoiding costs of unnecessary anti-tuberculosis treatment. Those models might be cost-effective tools in a health care network with hierarchical distribution of scarce resources.

  6. Comparative study of biodegradability prediction of chemicals using decision trees, functional trees, and logistic regression.

    Science.gov (United States)

    Chen, Guangchao; Li, Xuehua; Chen, Jingwen; Zhang, Ya-Nan; Peijnenburg, Willie J G M

    2014-12-01

    Biodegradation is the principal environmental dissipation process of chemicals. As such, it is a dominant factor determining the persistence and fate of organic chemicals in the environment, and is therefore of critical importance to chemical management and regulation. In the present study, the authors developed in silico methods assessing biodegradability based on a large heterogeneous set of 825 organic compounds, using the techniques of the C4.5 decision tree, the functional inner regression tree, and logistic regression. External validation was subsequently carried out by 2 independent test sets of 777 and 27 chemicals. As a result, the functional inner regression tree exhibited the best predictability with predictive accuracies of 81.5% and 81.0%, respectively, on the training set (825 chemicals) and test set I (777 chemicals). Performance of the developed models on the 2 test sets was subsequently compared with that of the Estimation Program Interface (EPI) Suite Biowin 5 and Biowin 6 models, which also showed a better predictability of the functional inner regression tree model. The model built in the present study exhibits a reasonable predictability compared with existing models while possessing a transparent algorithm. Interpretation of the mechanisms of biodegradation was also carried out based on the models developed. © 2014 SETAC.

  7. [Study on predicting firmness of watermelon by Vis/NIR diffuse transmittance technique].

    Science.gov (United States)

    Tian, Hai-Qing; Ying, Yi-Bin; Lu, Hui-Shan; Xu, Hui-Rong; Xie, Li-Juan; Fu, Xia-Ping; Yu, Hai-Yan

    2007-06-01

    Watermelon is a popular fruit in the world and firmness (FM) is one of the major characteristics used for assessing watermelon quality. The objective of the present research was to study the potential of visible/near Infrared (Vis/NIR) diffuse transmittance spectroscopy as a way for the nondestructive measurement of FM of watermelon. Statistical models between the spectra and FM were developed using partial least square (PLS) and principle component regression (PCR) methods. Performance of different models was assessed in terms of correlation coefficients (r) of validation set of samples and root mean square errors of prediction (RMSEP). Models for three kinds of mathematical treatments of spectra (original, first derivative and second derivative) were established. Savitsky-Goaly filter smoothing method was used for spectra data smoothing. The PLS model of the second derivative spectra gave the best prediction of FM, with a correlation coefficient (r) of 0. 974 and root mean square errors of prediction (RMSEP) of 0. 589 N using Savitsky-Goaly filter smoothing method. The results of this study indicate that NIR diffuse transmittance spectroscopy can be used to predict the FM of watermelon. The Vis/NIR diffuse transmittance technique will be valuable for the nandestructive detection large shape and thick peel fruits'.

  8. Prediction of immediate postoperative pain using the analgesia/nociception index: a prospective observational study.

    Science.gov (United States)

    Boselli, E; Bouvet, L; Bégou, G; Dabouz, R; Davidson, J; Deloste, J-Y; Rahali, N; Zadam, A; Allaouchiche, B

    2014-04-01

    The analgesia/nociception index (ANI) is derived from heart rate variability, ranging from 0 (maximal nociception) to 100 (maximal analgesia), to reflect the analgesia/nociception balance during general anaesthesia. This should be correlated with immediate postoperative pain in the post-anaesthesia care unit (PACU). The aim of this study was to evaluate the performance of ANI measured at arousal from general anaesthesia to predict immediate postoperative pain on arrival in PACU. Two hundred patients undergoing ear, nose, and throat or lower limb orthopaedic surgery with general anaesthesia using an inhalational agent and remifentanil were included in this prospective observational study. The ANI was measured immediately before tracheal extubation and pain intensity was assessed within 10 min of arrival in PACU using a 0-10 numerical rating scale (NRS). The relationship between ANI and NRS was assessed using linear regression. A receiver-operating characteristic (ROC) curve was used to evaluate the performance of ANI to predict NRS>3. A negative linear relationship was observed between ANI immediately before extubation and NRS on arrival in PACU. Using a threshold of 3 were both 86% with 92% negative predictive value, corresponding to an area under the ROC curve of 0.89. The measurement of ANI immediately before extubation after inhalation-remifentanil anaesthesia was significantly associated with pain intensity on arrival in PACU. The performance of ANI for the prediction of immediate postoperative pain is good and may assist physicians in optimizing acute pain management. ClinicalTrials.gov NCT01796249.

  9. A study of single multiplicative neuron model with nonlinear filters for hourly wind speed prediction

    International Nuclear Information System (INIS)

    Wu, Xuedong; Zhu, Zhiyu; Su, Xunliang; Fan, Shaosheng; Du, Zhaoping; Chang, Yanchao; Zeng, Qingjun

    2015-01-01

    Wind speed prediction is one important methods to guarantee the wind energy integrated into the whole power system smoothly. However, wind power has a non–schedulable nature due to the strong stochastic nature and dynamic uncertainty nature of wind speed. Therefore, wind speed prediction is an indispensable requirement for power system operators. Two new approaches for hourly wind speed prediction are developed in this study by integrating the single multiplicative neuron model and the iterated nonlinear filters for updating the wind speed sequence accurately. In the presented methods, a nonlinear state–space model is first formed based on the single multiplicative neuron model and then the iterated nonlinear filters are employed to perform dynamic state estimation on wind speed sequence with stochastic uncertainty. The suggested approaches are demonstrated using three cases wind speed data and are compared with autoregressive moving average, artificial neural network, kernel ridge regression based residual active learning and single multiplicative neuron model methods. Three types of prediction errors, mean absolute error improvement ratio and running time are employed for different models’ performance comparison. Comparison results from Tables 1–3 indicate that the presented strategies have much better performance for hourly wind speed prediction than other technologies. - Highlights: • Developed two novel hybrid modeling methods for hourly wind speed prediction. • Uncertainty and fluctuations of wind speed can be better explained by novel methods. • Proposed strategies have online adaptive learning ability. • Proposed approaches have shown better performance compared with existed approaches. • Comparison and analysis of two proposed novel models for three cases are provided

  10. Early EEG for outcome prediction of postanoxic coma: prospective cohort study with cost-minimization analysis.

    Science.gov (United States)

    Sondag, Lotte; Ruijter, Barry J; Tjepkema-Cloostermans, Marleen C; Beishuizen, Albertus; Bosch, Frank H; van Til, Janine A; van Putten, Michel J A M; Hofmeijer, Jeannette

    2017-05-15

    We recently showed that electroencephalography (EEG) patterns within the first 24 hours robustly contribute to multimodal prediction of poor or good neurological outcome of comatose patients after cardiac arrest. Here, we confirm these results and present a cost-minimization analysis. Early prognosis contributes to communication between doctors and family, and may prevent inappropriate treatment. A prospective cohort study including 430 subsequent comatose patients after cardiac arrest was conducted at intensive care units of two teaching hospitals. Continuous EEG was started within 12 hours after cardiac arrest and continued up to 3 days. EEG patterns were visually classified as unfavorable (isoelectric, low-voltage, or burst suppression with identical bursts) or favorable (continuous patterns) at 12 and 24 hours after cardiac arrest. Outcome at 6 months was classified as good (cerebral performance category (CPC) 1 or 2) or poor (CPC 3, 4, or 5). Predictive values of EEG measures and cost-consequences from a hospital perspective were investigated, assuming EEG-based decision- making about withdrawal of life-sustaining treatment in the case of a poor predicted outcome. Poor outcome occurred in 197 patients (51% of those included in the analyses). Unfavorable EEG patterns at 24 hours predicted a poor outcome with specificity of 100% (95% CI 98-100%) and sensitivity of 29% (95% CI 22-36%). Favorable patterns at 12 hours predicted good outcome with specificity of 88% (95% CI 81-93%) and sensitivity of 51% (95% CI 42-60%). Treatment withdrawal based on an unfavorable EEG pattern at 24 hours resulted in a reduced mean ICU length of stay without increased mortality in the long term. This gave small cost reductions, depending on the timing of withdrawal. Early EEG contributes to reliable prediction of good or poor outcome of postanoxic coma and may lead to reduced length of ICU stay. In turn, this may bring small cost reductions.

  11. Intention Understanding over T: A neuroimaging study on shared representations and tennis return predictions

    Directory of Open Access Journals (Sweden)

    Stephanie eCacioppo

    2014-10-01

    Full Text Available Studying the way athletes predict actions of their peers during fast-ball sports, such as a tennis, has proved to be a valuable tool for increasing our knowledge of intention understanding. The working model in this area is that the anticipatory representations of others’ behaviors require internal predictive models of actions formed from pre-established and shared representations between the observer and the actor. This model also predicts that observers would not be able to read accurately the intentions of a competitor if the competitor were to perform the action without prior knowledge of their intention until moments before the action. To test this hypothesis, we recorded brain activity from 25 male tennis players while they performed a novel behavioral tennis intention inference task, which included two conditions: i one condition in which they viewed video clips of a tennis athlete who knew in advance where he was about to act/serve (initially intended serves and ii one condition in which they viewed video clips of that same athlete when he did not know where he was to act/serve until the target was specified after he had tossed the ball into the air to complete his serve (non-initially intended serves. Our results demonstrated that i tennis expertise is related to the accuracy in predicting where another server intends to serve when that server knows where he intends to serve before (but not after he tosses the ball in the air; and ii accurate predictions are characterized by the recruitment of both cortical areas within the human mirror neuron system (that is known to be involved in higher-order (top-down processes of embodied cognition and shared representation and subcortical areas within brain regions involved in procedural memory (caudate nucleus. Interestingly, inaccurate predictions instead recruit areas known to be involved in low-level (bottom-up computational processes associated with the sense of agency and self

  12. Defending Tor from Network Adversaries: A Case Study of Network Path Prediction

    Directory of Open Access Journals (Sweden)

    Juen Joshua

    2015-06-01

    Full Text Available The Tor anonymity network has been shown vulnerable to traffic analysis attacks by autonomous systems (ASes and Internet exchanges (IXes, which can observe different overlay hops belonging to the same circuit. We evaluate whether network path prediction techniques provide an accurate picture of the threat from such adversaries, and whether they can be used to avoid this threat. We perform a measurement study by collecting 17.2 million traceroutes from Tor relays to destinations around the Internet. We compare the collected traceroute paths to predicted paths using state-of-the-art path inference techniques. We find that traceroutes present a very different picture, with the set of ASes seen in the traceroute path differing from the predicted path 80% of the time. We also consider the impact that prediction errors have on Tor security. Using a simulator to choose paths over a week, our traceroutes indicate a user has nearly a 100% chance of at least one compromise in a week with 11% of total paths containing an AS compromise and less than 1% containing an IX compromise when using default Tor selection. We find modifying the path selection to choose paths predicted to be safe lowers total paths with an AS compromise to 0.14% but still presents a 5–11% chance of at least one compromise in a week while making 5% of paths fail, with 96% of failures due to false positives in path inferences. Our results demonstrate more measurement and better path prediction is necessary to mitigate the risk of AS and IX adversaries to Tor.

  13. Predicting survival of de novo metastatic breast cancer in Asian women: systematic review and validation study.

    Directory of Open Access Journals (Sweden)

    Hui Miao

    Full Text Available BACKGROUND: In Asia, up to 25% of breast cancer patients present with distant metastases at diagnosis. Given the heterogeneous survival probabilities of de novo metastatic breast cancer, individual outcome prediction is challenging. The aim of the study is to identify existing prognostic models for patients with de novo metastatic breast cancer and validate them in Asia. MATERIALS AND METHODS: We performed a systematic review to identify prediction models for metastatic breast cancer. Models were validated in 642 women with de novo metastatic breast cancer registered between 2000 and 2010 in the Singapore Malaysia Hospital Based Breast Cancer Registry. Survival curves for low, intermediate and high-risk groups according to each prognostic score were compared by log-rank test and discrimination of the models was assessed by concordance statistic (C-statistic. RESULTS: We identified 16 prediction models, seven of which were for patients with brain metastases only. Performance status, estrogen receptor status, metastatic site(s and disease-free interval were the most common predictors. We were able to validate nine prediction models. The capacity of the models to discriminate between poor and good survivors varied from poor to fair with C-statistics ranging from 0.50 (95% CI, 0.48-0.53 to 0.63 (95% CI, 0.60-0.66. CONCLUSION: The discriminatory performance of existing prediction models for de novo metastatic breast cancer in Asia is modest. Development of an Asian-specific prediction model is needed to improve prognostication and guide decision making.

  14. Predicting Risk of Motor Vehicle Collisions in Patients with Glaucoma: A Longitudinal Study.

    Science.gov (United States)

    Gracitelli, Carolina P B; Tatham, Andrew J; Boer, Erwin R; Abe, Ricardo Y; Diniz-Filho, Alberto; Rosen, Peter N; Medeiros, Felipe A

    2015-01-01

    To evaluate the ability of longitudinal Useful Field of View (UFOV) and simulated driving measurements to predict future occurrence of motor vehicle collision (MVC) in drivers with glaucoma. Prospective observational cohort study. 117 drivers with glaucoma followed for an average of 2.1 ± 0.5 years. All subjects had standard automated perimetry (SAP), UFOV, driving simulator, and cognitive assessment obtained at baseline and every 6 months during follow-up. The driving simulator evaluated reaction times to high and low contrast peripheral divided attention stimuli presented while negotiating a winding country road, with central driving task performance assessed as "curve coherence". Drivers with MVC during follow-up were identified from Department of Motor Vehicle records. Survival models were used to evaluate the ability of driving simulator and UFOV to predict MVC over time, adjusting for potential confounding factors. Mean age at baseline was 64.5 ± 12.6 years. 11 of 117 (9.4%) drivers had a MVC during follow-up. In the multivariable models, low contrast reaction time was significantly predictive of MVC, with a hazard ratio (HR) of 2.19 per 1 SD slower reaction time (95% CI, 1.30 to 3.69; P = 0.003). UFOV divided attention was also significantly predictive of MVC with a HR of 1.98 per 1 SD worse (95% CI, 1.10 to 3.57; P = 0.022). Global SAP visual field indices in the better or worse eye were not predictive of MVC. The longitudinal model including driving simulator performance was a better predictor of MVC compared to UFOV (R2 = 0.41 vs R2 = 0.18). Longitudinal divided attention metrics on the UFOV test and during simulated driving were significantly predictive of risk of MVC in glaucoma patients. These findings may help improve the understanding of factors associated with driving impairment related to glaucoma.

  15. Predicting Risk of Motor Vehicle Collisions in Patients with Glaucoma: A Longitudinal Study.

    Directory of Open Access Journals (Sweden)

    Carolina P B Gracitelli

    Full Text Available To evaluate the ability of longitudinal Useful Field of View (UFOV and simulated driving measurements to predict future occurrence of motor vehicle collision (MVC in drivers with glaucoma.Prospective observational cohort study.117 drivers with glaucoma followed for an average of 2.1 ± 0.5 years.All subjects had standard automated perimetry (SAP, UFOV, driving simulator, and cognitive assessment obtained at baseline and every 6 months during follow-up. The driving simulator evaluated reaction times to high and low contrast peripheral divided attention stimuli presented while negotiating a winding country road, with central driving task performance assessed as "curve coherence". Drivers with MVC during follow-up were identified from Department of Motor Vehicle records.Survival models were used to evaluate the ability of driving simulator and UFOV to predict MVC over time, adjusting for potential confounding factors.Mean age at baseline was 64.5 ± 12.6 years. 11 of 117 (9.4% drivers had a MVC during follow-up. In the multivariable models, low contrast reaction time was significantly predictive of MVC, with a hazard ratio (HR of 2.19 per 1 SD slower reaction time (95% CI, 1.30 to 3.69; P = 0.003. UFOV divided attention was also significantly predictive of MVC with a HR of 1.98 per 1 SD worse (95% CI, 1.10 to 3.57; P = 0.022. Global SAP visual field indices in the better or worse eye were not predictive of MVC. The longitudinal model including driving simulator performance was a better predictor of MVC compared to UFOV (R2 = 0.41 vs R2 = 0.18.Longitudinal divided attention metrics on the UFOV test and during simulated driving were significantly predictive of risk of MVC in glaucoma patients. These findings may help improve the understanding of factors associated with driving impairment related to glaucoma.

  16. Prediction of Thorough QT study results using action potential simulations based on ion channel screens.

    Science.gov (United States)

    Mirams, Gary R; Davies, Mark R; Brough, Stephen J; Bridgland-Taylor, Matthew H; Cui, Yi; Gavaghan, David J; Abi-Gerges, Najah

    2014-01-01

    Detection of drug-induced pro-arrhythmic risk is a primary concern for pharmaceutical companies and regulators. Increased risk is linked to prolongation of the QT interval on the body surface ECG. Recent studies have shown that multiple ion channel interactions can be required to predict changes in ventricular repolarisation and therefore QT intervals. In this study we attempt to predict the result of the human clinical Thorough QT (TQT) study, using multiple ion channel screening which is available early in drug development. Ion current reduction was measured, in the presence of marketed drugs which have had a TQT study, for channels encoded by hERG, CaV1.2, NaV1.5, KCNQ1/MinK, and Kv4.3/KChIP2.2. The screen was performed on two platforms - IonWorks Quattro (all 5 channels, 34 compounds), and IonWorks Barracuda (hERG & CaV1.2, 26 compounds). Concentration-effect curves were fitted to the resulting data, and used to calculate a percentage reduction in each current at a given concentration. Action potential simulations were then performed using the ten Tusscher and Panfilov (2006), Grandi et al. (2010) and O'Hara et al. (2011) human ventricular action potential models, pacing at 1Hz and running to steady state, for a range of concentrations. We compared simulated action potential duration predictions with the QT prolongation observed in the TQT studies. At the estimated concentrations, simulations tended to underestimate any observed QT prolongation. When considering a wider range of concentrations, and conventional patch clamp rather than screening data for hERG, prolongation of ≥5ms was predicted with up to 79% sensitivity and 100% specificity. This study provides a proof-of-principle for the prediction of human TQT study results using data available early in drug development. We highlight a number of areas that need refinement to improve the method's predictive power, but the results suggest that such approaches will provide a useful tool in cardiac safety

  17. Risk prediction of major complications in individuals with diabetes: the Atherosclerosis Risk in Communities Study.

    Science.gov (United States)

    Parrinello, C M; Matsushita, K; Woodward, M; Wagenknecht, L E; Coresh, J; Selvin, E

    2016-09-01

    To develop a prediction equation for 10-year risk of a combined endpoint (incident coronary heart disease, stroke, heart failure, chronic kidney disease, lower extremity hospitalizations) in people with diabetes, using demographic and clinical information, and a panel of traditional and non-traditional biomarkers. We included in the study 654 participants in the Atherosclerosis Risk in Communities (ARIC) study, a prospective cohort study, with diagnosed diabetes (visit 2; 1990-1992). Models included self-reported variables (Model 1), clinical measurements (Model 2), and glycated haemoglobin (Model 3). Model 4 tested the addition of 12 blood-based biomarkers. We compared models using prediction and discrimination statistics. Successive stages of model development improved risk prediction. The C-statistics (95% confidence intervals) of models 1, 2, and 3 were 0.667 (0.64, 0.70), 0.683 (0.65, 0.71), and 0.694 (0.66, 0.72), respectively (p < 0.05 for differences). The addition of three traditional and non-traditional biomarkers [β-2 microglobulin, creatinine-based estimated glomerular filtration rate (eGFR), and cystatin C-based eGFR] to Model 3 significantly improved discrimination (C-statistic = 0.716; p = 0.003) and accuracy of 10-year risk prediction for major complications in people with diabetes (midpoint percentiles of lowest and highest deciles of predicted risk changed from 18-68% to 12-87%). These biomarkers, particularly those of kidney filtration, may help distinguish between people at low versus high risk of long-term major complications. © 2016 John Wiley & Sons Ltd.

  18. Development of a predictive energy equation for maintenance hemodialysis patients: a pilot study.

    Science.gov (United States)

    Byham-Gray, Laura; Parrott, J Scott; Ho, Wai Yin; Sundell, Mary B; Ikizler, T Alp

    2014-01-01

    The study objectives were to explore the predictors of measured resting energy expenditure (mREE) among a sample of maintenance hemodialysis (MHD) patients, to generate a predictive energy equation (MHDE), and to compare such models to another commonly used predictive energy equation in nutritional care, the Mifflin-St. Jeor equation (MSJE). The study was a retrospective, cross-sectional cohort design conducted at the Vanderbilt University Medical Center. Study subjects were adult MHD patients (N = 67). Data collected from several clinical trials were analyzed using Pearson's correlation and multivariate linear regression procedures. Demographic, anthropometric, clinical, and laboratory data were examined as potential predictors of mREE. Limits of agreement between the MHDE and the MSJE were evaluated using Bland-Altman plots. The a priori α was set at P lean body mass [LBM]) of mREE included (R(2) = 0.489) FFM, ALB, age, and CRP. Two additional models (MHDE-CRP and MHDE-CR) with acceptable predictability (R(2) = 0.460 and R(2) = 0.451) were derived to improve the clinical utility of the developed energy equation (MHDE-LBM). Using Bland-Altman plots, the MHDE over- and underpredicted mREE less often than the MSJE. Predictive models (MHDE) including selective demographic, clinical, and anthropometric data explained less than 50% variance of mREE but had better precision in determining energy requirements for MHD patients when compared with MSJE. Further research is necessary to improve predictive models of mREE in the MHD population and to test its validity and clinical application. Copyright © 2014 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.

  19. External Validation Study of First Trimester Obstetric Prediction Models (Expect Study I): Research Protocol and Population Characteristics.

    Science.gov (United States)

    Meertens, Linda Jacqueline Elisabeth; Scheepers, Hubertina Cj; De Vries, Raymond G; Dirksen, Carmen D; Korstjens, Irene; Mulder, Antonius Lm; Nieuwenhuijze, Marianne J; Nijhuis, Jan G; Spaanderman, Marc Ea; Smits, Luc Jm

    2017-10-26

    A number of first-trimester prediction models addressing important obstetric outcomes have been published. However, most models have not been externally validated. External validation is essential before implementing a prediction model in clinical practice. The objective of this paper is to describe the design of a study to externally validate existing first trimester obstetric prediction models, based upon maternal characteristics and standard measurements (eg, blood pressure), for the risk of pre-eclampsia (PE), gestational diabetes mellitus (GDM), spontaneous preterm birth (PTB), small-for-gestational-age (SGA) infants, and large-for-gestational-age (LGA) infants among Dutch pregnant women (Expect Study I). The results of a pilot study on the feasibility and acceptability of the recruitment process and the comprehensibility of the Pregnancy Questionnaire 1 are also reported. A multicenter prospective cohort study was performed in The Netherlands between July 1, 2013 and December 31, 2015. First trimester obstetric prediction models were systematically selected from the literature. Predictor variables were measured by the Web-based Pregnancy Questionnaire 1 and pregnancy outcomes were established using the Postpartum Questionnaire 1 and medical records. Information about maternal health-related quality of life, costs, and satisfaction with Dutch obstetric care was collected from a subsample of women. A pilot study was carried out before the official start of inclusion. External validity of the models will be evaluated by assessing discrimination and calibration. Based on the pilot study, minor improvements were made to the recruitment process and online Pregnancy Questionnaire 1. The validation cohort consists of 2614 women. Data analysis of the external validation study is in progress. This study will offer insight into the generalizability of existing, non-invasive first trimester prediction models for various obstetric outcomes in a Dutch obstetric population

  20. Enhancing Accuracy of Sediment Total Load Prediction Using Evolutionary Algorithms (Case Study: Gotoorchay River

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    K. Roshangar

    2016-09-01

    Full Text Available Introduction: Exact prediction of transported sediment rate by rivers in water resources projects is of utmost importance. Basically erosion and sediment transport process is one of the most complexes hydrodynamic. Although different studies have been developed on the application of intelligent models based on neural, they are not widely used because of lacking explicitness and complexity governing on choosing and architecting of proper network. In this study, a Genetic expression programming model (as an important branches of evolutionary algorithems for predicting of sediment load is selected and investigated as an intelligent approach along with other known classical and imperical methods such as Larsen´s equation, Engelund-Hansen´s equation and Bagnold´s equation. Materials and Methods: In this study, in order to improve explicit prediction of sediment load of Gotoorchay, located in Aras catchment, Northwestern Iran latitude: 38°24´33.3˝ and longitude: 44°46´13.2˝, genetic programming (GP and Genetic Algorithm (GA were applied. Moreover, the semi-empirical models for predicting of total sediment load and rating curve have been used. Finally all the methods were compared and the best ones were introduced. Two statistical measures were used to compare the performance of the different models, namely root mean square error (RMSE and determination coefficient (DC. RMSE and DC indicate the discrepancy between the observed and computed values. Results and Discussions: The statistical characteristics results obtained from the analysis of genetic programming method for both selected model groups indicated that the model 4 including the only discharge of the river, relative to other studied models had the highest DC and the least RMSE in the testing stage (DC= 0.907, RMSE= 0.067. Although there were several parameters applied in other models, these models were complicated and had weak results of prediction. Our results showed that the model 9

  1. Concurrent and predictive evaluation of malnutrition diagnostic measures in hip fracture inpatients: a diagnostic accuracy study.

    Science.gov (United States)

    Bell, J J; Bauer, J D; Capra, S; Pulle, R C

    2014-03-01

    Differences in malnutrition diagnostic measures impact malnutrition prevalence and outcomes data in hip fracture. This study investigated the concurrent and predictive validity of commonly reported malnutrition diagnostic measures in patients admitted to a metropolitan hospital acute hip fracture unit. A prospective, consecutive level II diagnostic accuracy study (n=142; 8 exclusions) including the International Classification of Disease, 10th Revision, Australian Modification (ICD10-AM) protein-energy malnutrition criteria, a body mass index (BMI) Patients were predominantly elderly (median age 83.5, range 50-100 years), female (68%), multimorbid (median five comorbidities), with 15% 4-month mortality. Malnutrition prevalence was lowest when assessed by BMI (13%), followed by MNA-SF (27%), ICD10-AM (48%), albumin (53%) and geriatrician assessment (55%). Agreement between measures was highest between ICD10-AM and geriatrician assessment (κ=0.61) followed by ICD10-AM and MNA-SF measures (κ=0.34). ICD10-AM diagnosed malnutrition was the only measure associated with 48-h mobilisation (35.0 vs 55.3%; P=0.018). Reduced likelihood of home discharge was predicted by ICD-10-AM (20.6 vs 57.1%; P=0.001) and MNA-SF (18.8 vs 47.8%; P=0.035). Bivariate analysis demonstrated ICD10-AM (relative risk (RR)1.2; 1.05-1.42) and MNA-SF (RR1.2; 1.0-1.5) predicted 4-month mortality. When adjusted for age, usual place of residency, comorbidities and time to surgery only ICD-10AM criteria predicted mortality (odds ratio 3.59; 1.10-11.77). Albumin, BMI and geriatrician assessment demonstrated limited concurrent and predictive validity. Malnutrition prevalence in hip fracture varies substantially depending on the diagnostic measure applied. ICD-10AM criteria or the MNA-SF should be considered for the diagnosis of protein-energy malnutrition in frail, multi-morbid hip fracture inpatients.

  2. Studying Musical and Linguistic Prediction in Comparable Ways: The Melodic Cloze Probability Method.

    Science.gov (United States)

    Fogel, Allison R; Rosenberg, Jason C; Lehman, Frank M; Kuperberg, Gina R; Patel, Aniruddh D

    2015-01-01

    Prediction or expectancy is thought to play an important role in both music and language processing. However, prediction is currently studied independently in the two domains, limiting research on relations between predictive mechanisms in music and language. One limitation is a difference in how expectancy is quantified. In language, expectancy is typically measured using the cloze probability task, in which listeners are asked to complete a sentence fragment with the first word that comes to mind. In contrast, previous production-based studies of melodic expectancy have asked participants to sing continuations following only one to two notes. We have developed a melodic cloze probability task in which listeners are presented with the beginning of a novel tonal melody (5-9 notes) and are asked to sing the note they expect to come next. Half of the melodies had an underlying harmonic structure designed to constrain expectations for the next note, based on an implied authentic cadence (AC) within the melody. Each such 'authentic cadence' melody was matched to a 'non-cadential' (NC) melody matched in terms of length, rhythm and melodic contour, but differing in implied harmonic structure. Participants showed much greater consistency in the notes sung following AC vs. NC melodies on average. However, significant variation in degree of consistency was observed within both AC and NC melodies. Analysis of individual melodies suggests that pitch prediction in tonal melodies depends on the interplay of local factors just prior to the target note (e.g., local pitch interval patterns) and larger-scale structural relationships (e.g., melodic patterns and implied harmonic structure). We illustrate how the melodic cloze method can be used to test a computational model of melodic expectation. Future uses for the method include exploring the interplay of different factors shaping melodic expectation, and designing experiments that compare the cognitive mechanisms of prediction in

  3. An Automated Defect Prediction Framework using Genetic Algorithms: A Validation of Empirical Studies

    Directory of Open Access Journals (Sweden)

    Juan Murillo-Morera

    2016-05-01

    Full Text Available Today, it is common for software projects to collect measurement data through development processes. With these data, defect prediction software can try to estimate the defect proneness of a software module, with the objective of assisting and guiding software practitioners. With timely and accurate defect predictions, practitioners can focus their limited testing resources on higher risk areas. This paper reports the results of three empirical studies that uses an automated genetic defect prediction framework. This framework generates and compares different learning schemes (preprocessing + attribute selection + learning algorithms and selects the best one using a genetic algorithm, with the objective to estimate the defect proneness of a software module. The first empirical study is a performance comparison of our framework with the most important framework of the literature. The second empirical study is a performance and runtime comparison between our framework and an exhaustive framework. The third empirical study is a sensitivity analysis. The last empirical study, is our main contribution in this paper. Performance of the software development defect prediction models (using AUC, Area Under the Curve was validated using NASA-MDP and PROMISE data sets. Seventeen data sets from NASA-MDP (13 and PROMISE (4 projects were analyzed running a NxM-fold cross-validation. A genetic algorithm was used to select the components of the learning schemes automatically, and to assess and report the results. Our results reported similar performance between frameworks. Our framework reported better runtime than exhaustive framework. Finally, we reported the best configuration according to sensitivity analysis.

  4. Study on discriminant analysis by military mental disorder prediction scale for mental disorder of new recruits

    Directory of Open Access Journals (Sweden)

    Li-yi ZHANG

    2011-11-01

    Full Text Available Objective To examine the predictive role of the Military Mental Disorder Prediction Scale on the mental disorder of new recruits.Methods The present study examined 115 new recruits diagnosed with mental disorder and 115 healthy new recruits.The recruits were tested using the Military Mental Disorder Prediction Scale.The discriminant function was built by discriminant analysis method.The current study analyzed the predictive value of 11 factors(family medical record and past medical record(X1,growth experience(X2,introversion(X3,stressor(X4,poor mental defense(X5,social support(X6,psychosis(X7,depression(X8,mania(X9,neurosis(X10,and personality disorder(X11 aside from lie factor on the mental disorder of new recruits.Results The mental disorder group has higher total score and factor score in family medical record and past medical record,introversion,stressor,poor mental defense,social support,psychosis,depression,mania,neurosis,personality disorder,and lie than those of the contrast group(P < 0.01.For the score of growth experience factor,that of the mental disorder group is higher than the score of the contrast group(P < 0.05.All 11 factors except the lie factor in the Mental Disorder Prediction Scale are taken as independent variables by enforced introduction to obtain the Fisher linear discriminant function as follows: The mental disorder group=-7.014-0.278X1+1.556X2+1.563X3+0.878X4+0.183X5-0.845X6-0.562X7-0.353X8+1.246X9-0.505X10+1.029X11.The contrast group=-2.971+0.056X1+2.194X2+0.707X3+0.592X4-0.086X5-0.888X6-0.133X7-0.360X8+0.654X9-0.467X10+0.308X11.The discriminant function has an accuracy rate of 76.5% on the new recruits with mental disorders and 100% on the healthy new recruits.The total accurate discrimination rate is 88.3% and the total inaccurate discrimination rate is 11.7%.Conclusion The Military Mental Disorder Prediction Scale has a high accuracy rate on the prediction of mental disorder of new recruits and is worthy of

  5. Study of Model Predictive Control for Path-Following Autonomous Ground Vehicle Control under Crosswind Effect

    Directory of Open Access Journals (Sweden)

    Fitri Yakub

    2016-01-01

    Full Text Available We present a comparative study of model predictive control approaches of two-wheel steering, four-wheel steering, and a combination of two-wheel steering with direct yaw moment control manoeuvres for path-following control in autonomous car vehicle dynamics systems. Single-track mode, based on a linearized vehicle and tire model, is used. Based on a given trajectory, we drove the vehicle at low and high forward speeds and on low and high road friction surfaces for a double-lane change scenario in order to follow the desired trajectory as close as possible while rejecting the effects of wind gusts. We compared the controller based on both simple and complex bicycle models without and with the roll vehicle dynamics for different types of model predictive control manoeuvres. The simulation result showed that the model predictive control gave a better performance in terms of robustness for both forward speeds and road surface variation in autonomous path-following control. It also demonstrated that model predictive control is useful to maintain vehicle stability along the desired path and has an ability to eliminate the crosswind effect.

  6. Multi-Robot Interfaces and Operator Situational Awareness: Study of the Impact of Immersion and Prediction

    Science.gov (United States)

    Peña-Tapia, Elena; Martín-Barrio, Andrés; Olivares-Méndez, Miguel A.

    2017-01-01

    Multi-robot missions are a challenge for operators in terms of workload and situational awareness. These operators have to receive data from the robots, extract information, understand the situation properly, make decisions, generate the adequate commands, and send them to the robots. The consequences of excessive workload and lack of awareness can vary from inefficiencies to accidents. This work focuses on the study of future operator interfaces of multi-robot systems, taking into account relevant issues such as multimodal interactions, immersive devices, predictive capabilities and adaptive displays. Specifically, four interfaces have been designed and developed: a conventional, a predictive conventional, a virtual reality and a predictive virtual reality interface. The four interfaces have been validated by the performance of twenty-four operators that supervised eight multi-robot missions of fire surveillance and extinguishing. The results of the workload and situational awareness tests show that virtual reality improves the situational awareness without increasing the workload of operators, whereas the effects of predictive components are not significant and depend on their implementation. PMID:28749407

  7. Study (Prediction of Main Pipes Break Rates in Water Distribution Systems Using Intelligent and Regression Methods

    Directory of Open Access Journals (Sweden)

    Massoud Tabesh

    2011-07-01

    Full Text Available Optimum operation of water distribution networks is one of the priorities of sustainable development of water resources, considering the issues of increasing efficiency and decreasing the water losses. One of the key subjects in optimum operational management of water distribution systems is preparing rehabilitation and replacement schemes, prediction of pipes break rate and evaluation of their reliability. Several approaches have been presented in recent years regarding prediction of pipe failure rates which each one requires especial data sets. Deterministic models based on age and deterministic multi variables and stochastic group modeling are examples of the solutions which relate pipe break rates to parameters like age, material and diameters. In this paper besides the mentioned parameters, more factors such as pipe depth and hydraulic pressures are considered as well. Then using multi variable regression method, intelligent approaches (Artificial neural network and neuro fuzzy models and Evolutionary polynomial Regression method (EPR pipe burst rate are predicted. To evaluate the results of different approaches, a case study is carried out in a part ofMashhadwater distribution network. The results show the capability and advantages of ANN and EPR methods to predict pipe break rates, in comparison with neuro fuzzy and multi-variable regression methods.

  8. Multi-Robot Interfaces and Operator Situational Awareness: Study of the Impact of Immersion and Prediction.

    Science.gov (United States)

    Roldán, Juan Jesús; Peña-Tapia, Elena; Martín-Barrio, Andrés; Olivares-Méndez, Miguel A; Del Cerro, Jaime; Barrientos, Antonio

    2017-07-27

    Multi-robot missions are a challenge for operators in terms of workload and situational awareness. These operators have to receive data from the robots, extract information, understand the situation properly, make decisions, generate the adequate commands, and send them to the robots. The consequences of excessive workload and lack of awareness can vary from inefficiencies to accidents. This work focuses on the study of future operator interfaces of multi-robot systems, taking into account relevant issues such as multimodal interactions, immersive devices, predictive capabilities and adaptive displays. Specifically, four interfaces have been designed and developed: a conventional, a predictive conventional, a virtual reality and a predictive virtual reality interface. The four interfaces have been validated by the performance of twenty-four operators that supervised eight multi-robot missions of fire surveillance and extinguishing. The results of the workload and situational awareness tests show that virtual reality improves the situational awareness without increasing the workload of operators, whereas the effects of predictive components are not significant and depend on their implementation.

  9. Multi-Robot Interfaces and Operator Situational Awareness: Study of the Impact of Immersion and Prediction

    Directory of Open Access Journals (Sweden)

    Juan Jesús Roldán

    2017-07-01

    Full Text Available Multi-robot missions are a challenge for operators in terms of workload and situational awareness. These operators have to receive data from the robots, extract information, understand the situation properly, make decisions, generate the adequate commands, and send them to the robots. The consequences of excessive workload and lack of awareness can vary from inefficiencies to accidents. This work focuses on the study of future operator interfaces of multi-robot systems, taking into account relevant issues such as multimodal interactions, immersive devices, predictive capabilities and adaptive displays. Specifically, four interfaces have been designed and developed: a conventional, a predictive conventional, a virtual reality and a predictive virtual reality interface. The four interfaces have been validated by the performance of twenty-four operators that supervised eight multi-robot missions of fire surveillance and extinguishing. The results of the workload and situational awareness tests show that virtual reality improves the situational awareness without increasing the workload of operators, whereas the effects of predictive components are not significant and depend on their implementation.

  10. Predictive modeling studies for the ecotoxicity of ionic liquids towards the green algae Scenedesmus vacuolatus.

    Science.gov (United States)

    Das, Rudra Narayan; Roy, Kunal

    2014-06-01

    Hazardous potential of ionic liquids is becoming an issue of high concern with increasing application of these compounds in various industrial processes. Predictive toxicological modeling on ionic liquids provides a rational assessment strategy and aids in developing suitable guidance for designing novel analogues. The present study attempts to explore the chemical features of ionic liquids responsible for their ecotoxicity towards the green algae Scenedesmus vacuolatus by developing mathematical models using extended topochemical atom (ETA) indices along with other categories of chemical descriptors. The entire study has been conducted with reference to the OECD guidelines for QSAR model development using predictive classification and regression modeling strategies. The best models from both the analyses showed that ecotoxicity of ionic liquids can be decreased by reducing chain length of cationic substituents and increasing hydrogen bond donor feature in cations, and replacing bulky unsaturated anions with simple saturated moiety having less lipophilic heteroatoms. Copyright © 2013 Elsevier Ltd. All rights reserved.

  11. Serial-order short-term memory predicts vocabulary development: evidence from a longitudinal study.

    Science.gov (United States)

    Leclercq, Anne-Lise; Majerus, Steve

    2010-03-01

    Serial-order short-term memory (STM), as opposed to item STM, has been shown to be very consistently associated with lexical learning abilities in cross-sectional study designs. This study investigated longitudinal predictions between serial-order STM and vocabulary development. Tasks maximizing the temporary retention of either serial-order or item information were administered to kindergarten children aged 4 and 5. At age 4, age 5, and from age 4 to age 5, serial-order STM capacities, but not item STM capacities, were specifically associated with vocabulary development. Moreover, the increase of serial-order STM capacity from age 4 to age 5 predicted the increase of vocabulary knowledge over the same time period. These results support a theoretical position that assumes an important role for serial-order STM capacities in vocabulary acquisition.

  12. Prediction of paddy drying kinetics: A comparative study between mathematical and artificial neural network modelling

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    Beigi Mohsen

    2017-01-01

    Full Text Available The present study aimed at investigation of deep bed drying of rough rice kernels at various thin layers at different drying air temperatures and flow rates. A comparative study was performed between mathematical thin layer models and artificial neural networks to estimate the drying curves of rough rice. The suitability of nine mathematical models in simulating the drying kinetics was examined and the Midilli model was determined as the best approach for describing drying curves. Different feed forward-back propagation artificial neural networks were examined to predict the moisture content variations of the grains. The ANN with 4-18-18-1 topology, transfer function of hyperbolic tangent sigmoid and a Levenberg-Marquardt back propagation training algorithm provided the best results with the maximum correlation coefficient and the minimum mean square error values. Furthermore, it was revealed that ANN modeling had better performance in prediction of drying curves with lower root mean square error values.

  13. Cardiovascular reactivity to video game predicts subsequent blood pressure increases in young men: The CARDIA study.

    Science.gov (United States)

    Markovitz, J H; Raczynski, J M; Wallace, D; Chettur, V; Chesney, M A

    1998-01-01

    This study was undertaken to determine the relationship between heightened reactivity of blood pressure (BP) during stress and 5-year changes in blood pressure and hypertensive status, using the CARDIA study. A total of 3364 participants (910 white men, 909 white women, 678 black men, and 867 black women), initially 20 to 32 years old and normotensive, were included. Cardiovascular reactivity to psychological stressors (video game and star-tracing tasks for 3 minutes, cold pressor test for 1 minute) was measured in 1987-1988. We then examined reactivity as a predictor of significant BP change (> or = 8 mm Hg, thought to represent a clinically significant increase) over the next 5 years. Logistic regression models were used to control for potential covariates. Significant BP change and the development of hypertension (BP greater than 140/90 or taking medication for hypertension) over the 5-year follow-up were examined in separate analyses. Increased systolic blood pressure (SBP) reactivity to the video game was associated with a significant 5-year SBP increase among the entire cohort, independent of resting SBP (p men but not for women. Reactivity to the star-tracing task or the cold pressor test did not predict significant BP change. Among black men only, new hypertensives (N = 36) had greater diastolic blood pressure (DBP) reactivity to the video game (p = .01). Although BP reactivity to all physical and mental stressors used in this study did not consistently predict 5-year change in BP in this young cohort, the results indicate that reactivity to a video game stressor predicts 5-year change in BP and early hypertension among young adult men. These findings are consistent with other studies showing the usefulness of stressors producing a primarily beta-adrenergic response in predicting BP change and hypertension. The results may be limited by the shortened initial rest and recovery periods used in the CARDIA protocol.

  14. An elevated respiratory quotient predicts complications after cardiac surgery under extracorporeal circulation: an observational pilot study.

    Science.gov (United States)

    Piot, J; Hébrard, A; Durand, M; Payen, J F; Albaladejo, P

    2018-04-17

    Following cardiac surgery, hyperlactatemia due to anaerobic metabolism is associated with an increase in both morbidity and mortality. We previously found that an elevated respiratory quotient (RQ) predicts anaerobic metabolism. In the present study we aimed to demonstrate that it is also associated with poor outcome following cardiac surgery. This single institution, prospective, observational study includes all those patients that were consecutively admitted to the intensive care unit (ICU) after cardiac surgery with cardiopulmonary bypass, that had also been monitored using pulmonary artery catheter. Data were recorded at admission (H0) and after one hour (H1) including: oxygen consumption ([Formula: see text]), carbon dioxide production ([Formula: see text]), RQ ([Formula: see text]), lactate levels and mixed venous oxygen saturation ([Formula: see text]). The primary endpoint was defined as mortality at 30 days. Comparison of the area under the curve (AUC) for receiver operating characteristic curves was used to analyze the prognostic predictive value of RQ, lactate levels and [Formula: see text], in terms of patient outcome. We studied 151 patients admitted to the ICU between May 2015 and February 2016. Seventy eight patients experienced a worse than expected outcome in the post-operative period, and among those seven died. RQ at H1 in non-survivors ([Formula: see text]) was higher than in survivors ([Formula: see text]; p = 0.02). The AUC for RQ to predict mortality was 0.77 (IC 95% [0.70-0.84]), with a threshold value of 0.76 (sensitivity 64%, specificity 100%). By comparison, the AUC for lactate levels was significantly superior (AUClact 0.89, IC 95% [0.83-0.93], p = 0.02). In this study, elevated RQ appeared to be predictive of mortality after cardiac surgery with CPB.

  15. Can the cerebroplacental ratio (CPR) predict intrapartum fetal compromise? : a prospective observational study

    OpenAIRE

    Page, Ann-Sophie; Page, Geert; Dehaene, Isabelle; Roets, Ellen; Roelens, Kristien

    2017-01-01

    Objective: To investigate the potential clinical use of serial fetal CPR measurements during the last month of pregnancy for the prediction of adverse perinatal outcome in unselected low-risk pregnancies. Methods: A multicenter prospective observational cohort study in 315 consecutively recruited low-risk pregnancies. All eligible pregnancies underwent serial sonographic evaluation of fetal weight and Doppler indices at two week intervals, from 36 weeks gestation until delivery. Data were ...

  16. Emotion regulation and Residual Depression Predict Psychosocial Functioning in Bipolar Disorder: Preliminary Study

    OpenAIRE

    Becerra, Rodrigo; Cruise, Kate; Harms, Craig; Allan, Alfred; Bassett, Darryl; Hood, Sean; Murray, Greg

    2015-01-01

    This study explores the predictive value of various clinical, neuropsychological, functional, and emotion regulation processes for recovery in Bipolar Disorder. Clinical and demographic information was collected for 27 euthymic or residually depressed BD participants. Seventy one percent of the sample reported some degree of impairment in psychosocial functioning. Both residual depression and problems with emotion regulation were identified as significant predictors of poor psychosocial funct...

  17. Predicting asthma in preschool children with asthma symptoms: study rationale and design

    Directory of Open Access Journals (Sweden)

    Hafkamp-de Groen Esther

    2012-10-01

    Full Text Available Abstract Background In well-child care it is difficult to determine whether preschool children with asthma symptoms actually have or will develop asthma at school age. The PIAMA (Prevention and Incidence of Asthma and Mite Allergy Risk Score has been proposed as an instrument that predicts asthma at school age, using eight easy obtainable parameters, assessed at the time of first asthma symptoms at preschool age. The aim of this study is to present the rationale and design of a study 1 to externally validate and update the PIAMA Risk Score, 2 to develop an Asthma Risk Appraisal Tool to predict asthma at school age in (specific subgroups of preschool children with asthma symptoms and 3 to test implementation of the Asthma Risk Appraisal Tool in well-child care. Methods and design The study will be performed within the framework of Generation R, a prospective multi-ethnic cohort study. In total, consent for postnatal follow-up was obtained from 7893 children, born between 2002 and 2006. At preschool age the PIAMA Risk Score will be assessed and used to predict asthma at school age. Discrimination (C-index and calibration will be assessed for the external validation. We will study whether the predictive ability of the PIAMA Risk Score can be improved by removing or adding predictors (e.g. preterm birth. The (updated PIAMA Risk Score will be converted to the Asthma Risk Appraisal Tool- to predict asthma at school age in preschool children with asthma symptoms. Additionally, we will conduct a pilot study to test implementation of the Asthma Risk Appraisal Tool in well-child care. Discussion Application of the Asthma Risk Appraisal Tool in well-child care will help to distinguish preschool children at high- and low-risk of developing asthma at school age when asthma symptoms appear. This study will increase knowledge about the validity of the PIAMA risk score and might improve risk assessment of developing asthma at school age in (specific subgroups

  18. Strategies to design clinical studies to identify predictive biomarkers in cancer research.

    Science.gov (United States)

    Perez-Gracia, Jose Luis; Sanmamed, Miguel F; Bosch, Ana; Patiño-Garcia, Ana; Schalper, Kurt A; Segura, Victor; Bellmunt, Joaquim; Tabernero, Josep; Sweeney, Christopher J; Choueiri, Toni K; Martín, Miguel; Fusco, Juan Pablo; Rodriguez-Ruiz, Maria Esperanza; Calvo, Alfonso; Prior, Celia; Paz-Ares, Luis; Pio, Ruben; Gonzalez-Billalabeitia, Enrique; Gonzalez Hernandez, Alvaro; Páez, David; Piulats, Jose María; Gurpide, Alfonso; Andueza, Mapi; de Velasco, Guillermo; Pazo, Roberto; Grande, Enrique; Nicolas, Pilar; Abad-Santos, Francisco; Garcia-Donas, Jesus; Castellano, Daniel; Pajares, María J; Suarez, Cristina; Colomer, Ramon; Montuenga, Luis M; Melero, Ignacio

    2017-02-01

    The discovery of reliable biomarkers to predict efficacy and toxicity of anticancer drugs remains one of the key challenges in cancer research. Despite its relevance, no efficient study designs to identify promising candidate biomarkers have been established. This has led to the proliferation of a myriad of exploratory studies using dissimilar strategies, most of which fail to identify any promising targets and are seldom validated. The lack of a proper methodology also determines that many anti-cancer drugs are developed below their potential, due to failure to identify predictive biomarkers. While some drugs will be systematically administered to many patients who will not benefit from them, leading to unnecessary toxicities and costs, others will never reach registration due to our inability to identify the specific patient population in which they are active. Despite these drawbacks, a limited number of outstanding predictive biomarkers have been successfully identified and validated, and have changed the standard practice of oncology. In this manuscript, a multidisciplinary panel reviews how those key biomarkers were identified and, based on those experiences, proposes a methodological framework-the DESIGN guidelines-to standardize the clinical design of biomarker identification studies and to develop future research in this pivotal field. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  19. Predicting methylphenidate response in attention deficit hyperactivity disorder: a preliminary study.

    Science.gov (United States)

    Johnston, Blair A; Coghill, David; Matthews, Keith; Steele, J Douglas

    2015-01-01

    Methylphenidate (MPH) is established as the main pharmacological treatment for patients with attention deficit hyperactivity disorder (ADHD). Whilst MPH is generally a highly effective treatment, not all patients respond, and some experience adverse reactions. Currently, there is no reliable method to predict how patients will respond, other than by exposure to a trial of medication. In this preliminary study, we sought to investigate whether an accurate predictor of clinical response to methylphenidate could be developed for individual patients, using sociodemographic, clinical and neuropsychological measures. Of the 43 boys with ADHD included in this proof-of-concept study, 30 were classed as responders and 13 as non-responders to MPH, with no significant differences in age nor verbal intelligence quotient (IQ) between the groups. Here we report the application of a multivariate analysis approach to the prediction of clinical response to MPH, which achieved an accuracy of 77% (p = 0.005). The most important variables to the classifier were performance on a 'go/no go' task and comorbid conduct disorder. This preliminary study suggested that further investigation is merited. Achieving a highly significant accuracy of 77% for the prediction of MPH response is an encouraging step towards finding a reliable and clinically useful method that could minimise the number of children needlessly being exposed to MPH. © The Author(s) 2014.

  20. Improving rational thermal comfort prediction by using subpopulation characteristics: A case study at Hermitage Amsterdam.

    Science.gov (United States)

    Kramer, Rick; Schellen, Lisje; Schellen, Henk; Kingma, Boris

    2017-01-01

    This study aims to improve the prediction accuracy of the rational standard thermal comfort model, known as the Predicted Mean Vote (PMV) model, by (1) calibrating one of its input variables "metabolic rate," and (2) extending it by explicitly incorporating the variable running mean outdoor temperature (RMOT) that relates to adaptive thermal comfort. The analysis was performed with survey data ( n = 1121) and climate measurements of the indoor and outdoor environment from a one year-long case study undertaken at Hermitage Amsterdam museum in the Netherlands. The PMVs were calculated for 35 survey days using (1) an a priori assumed metabolic rate, (2) a calibrated metabolic rate found by fitting the PMVs to the thermal sensation votes (TSVs) of each respondent using an optimization routine, and (3) extending the PMV model by including the RMOT. The results show that the calibrated metabolic rate is estimated to be 1.5 Met for this case study that was predominantly visited by elderly females. However, significant differences in metabolic rates have been revealed between adults and elderly showing the importance of differentiating between subpopulations. Hence, the standard tabular values, which only differentiate between various activities, may be oversimplified for many cases. Moreover, extending the PMV model with the RMOT substantially improves the thermal sensation prediction, but thermal sensation toward extreme cool and warm sensations remains partly underestimated.

  1. Parametric study on the advantages of weather-predicted control algorithm of free cooling ventilation system

    International Nuclear Information System (INIS)

    Medved, Sašo; Babnik, Miha; Vidrih, Boris; Arkar, Ciril

    2014-01-01

    Predicted climate changes and the increased intensity of urban heat islands, as well as population aging, will increase the energy demand for the cooling of buildings in the future. However, the energy demand for cooling can be efficiently reduced by low-exergy free-cooling systems, which use natural processes, like evaporative cooling or the environmental cold of ambient air during night-time ventilation for the cooling of buildings. Unlike mechanical cooling systems, the energy for the operation of free-cooling system is needed only for the transport of the cold from the environment into the building. Because the natural cold potential is time dependent, the efficiency of free-cooling systems could be improved by introducing a weather forecast into the algorithm for the controlling. In the article, a numerical algorithm for the optimization of the operation of free-cooling systems with night-time ventilation is presented and validated on a test cell with different thermal storage capacities and during different ambient conditions. As a case study, the advantage of weather-predicted controlling is presented for a summer week for typical office room. The results show the necessity of the weather-predicted controlling of free-cooling ventilation systems for achieving the highest overall energy efficiency of such systems in comparison to mechanical cooling, better indoor comfort conditions and a decrease in the primary energy needed for cooling of the buildings. - Highlights: • Energy demand for cooling will increase due to climate changes and urban heat island • Free cooling could significantly reduce energy demand for cooling of the buildings. • Free cooling is more effective if weather prediction is included in operation control. • Weather predicted free cooling operation algorithm was validated on test cell. • Advantages of free-cooling on mechanical cooling is shown with different indicators

  2. Risk assessment models to predict caries recurrence after oral rehabilitation under general anaesthesia: a pilot study.

    Science.gov (United States)

    Lin, Yai-Tin; Kalhan, Ashish Chetan; Lin, Yng-Tzer Joseph; Kalhan, Tosha Ashish; Chou, Chein-Chin; Gao, Xiao Li; Hsu, Chin-Ying Stephen

    2018-05-08

    Oral rehabilitation under general anaesthesia (GA), commonly employed to treat high caries-risk children, has been associated with high economic and individual/family burden, besides high post-GA caries recurrence rates. As there is no caries prediction model available for paediatric GA patients, this study was performed to build caries risk assessment/prediction models using pre-GA data and to explore mid-term prognostic factors for early identification of high-risk children prone to caries relapse post-GA oral rehabilitation. Ninety-two children were identified and recruited with parental consent before oral rehabilitation under GA. Biopsychosocial data collection at baseline and the 6-month follow-up were conducted using questionnaire (Q), microbiological assessment (M) and clinical examination (C). The prediction models constructed using data collected from Q, Q + M and Q + M + C demonstrated an accuracy of 72%, 78% and 82%, respectively. Furthermore, of the 83 (90.2%) patients recalled 6 months after GA intervention, recurrent caries was identified in 54.2%, together with reduced bacterial counts, lower plaque index and increased percentage of children toothbrushing for themselves (all P < 0.05). Additionally, meal-time and toothbrushing duration were shown, through bivariate analyses, to be significant prognostic determinants for caries recurrence (both P < 0.05). Risk assessment/prediction models built using pre-GA data may be promising in identifying high-risk children prone to post-GA caries recurrence, although future internal and external validation of predictive models is warranted. © 2018 FDI World Dental Federation.

  3. Survival prediction of trauma patients: a study on US National Trauma Data Bank.

    Science.gov (United States)

    Sefrioui, I; Amadini, R; Mauro, J; El Fallahi, A; Gabbrielli, M

    2017-12-01

    Exceptional circumstances like major incidents or natural disasters may cause a huge number of victims that might not be immediately and simultaneously saved. In these cases it is important to define priorities avoiding to waste time and resources for not savable victims. Trauma and Injury Severity Score (TRISS) methodology is the well-known and standard system usually used by practitioners to predict the survival probability of trauma patients. However, practitioners have noted that the accuracy of TRISS predictions is unacceptable especially for severely injured patients. Thus, alternative methods should be proposed. In this work we evaluate different approaches for predicting whether a patient will survive or not according to simple and easily measurable observations. We conducted a rigorous, comparative study based on the most important prediction techniques using real clinical data of the US National Trauma Data Bank. Empirical results show that well-known Machine Learning classifiers can outperform the TRISS methodology. Based on our findings, we can say that the best approach we evaluated is Random Forest: it has the best accuracy, the best area under the curve, and k-statistic, as well as the second-best sensitivity and specificity. It has also a good calibration curve. Furthermore, its performance monotonically increases as the dataset size grows, meaning that it can be very effective to exploit incoming knowledge. Considering the whole dataset, it is always better than TRISS. Finally, we implemented a new tool to compute the survival of victims. This will help medical practitioners to obtain a better accuracy than the TRISS tools. Random Forests may be a good candidate solution for improving the predictions on survival upon the standard TRISS methodology.

  4. Machine learning techniques in disease forecasting: a case study on rice blast prediction

    Directory of Open Access Journals (Sweden)

    Kapoor Amar S

    2006-11-01

    Full Text Available Abstract Background Diverse modeling approaches viz. neural networks and multiple regression have been followed to date for disease prediction in plant populations. However, due to their inability to predict value of unknown data points and longer training times, there is need for exploiting new prediction softwares for better understanding of plant-pathogen-environment relationships. Further, there is no online tool available which can help the plant researchers or farmers in timely application of control measures. This paper introduces a new prediction approach based on support vector machines for developing weather-based prediction models of plant diseases. Results Six significant weather variables were selected as predictor variables. Two series of models (cross-location and cross-year were developed and validated using a five-fold cross validation procedure. For cross-year models, the conventional multiple regression (REG approach achieved an average correlation coefficient (r of 0.50, which increased to 0.60 and percent mean absolute error (%MAE decreased from 65.42 to 52.24 when back-propagation neural network (BPNN was used. With generalized regression neural network (GRNN, the r increased to 0.70 and %MAE also improved to 46.30, which further increased to r = 0.77 and %MAE = 36.66 when support vector machine (SVM based method was used. Similarly, cross-location validation achieved r = 0.48, 0.56 and 0.66 using REG, BPNN and GRNN respectively, with their corresponding %MAE as 77.54, 66.11 and 58.26. The SVM-based method outperformed all the three approaches by further increasing r to 0.74 with improvement in %MAE to 44.12. Overall, this SVM-based prediction approach will open new vistas in the area of forecasting plant diseases of various crops. Conclusion Our case study demonstrated that SVM is better than existing machine learning techniques and conventional REG approaches in forecasting plant diseases. In this direction, we have also

  5. Numerical model experiments on the variation of the ocean-atmosphere carbon cycle during the last 2100 years: The impact of variations of the thermahaline oceanic circulation; Numerische Modellexperimente zur Veraenderung des Ozean-Atmosphaere-Kohlenstoffkreislaufes waehrend der letzten 21000 Jahre: Der Einfluss von Variationen der thermohalinen Ozeanzirkulation

    Energy Technology Data Exchange (ETDEWEB)

    Schulz, M

    1998-03-01

    In order to quantify the variability of the ocean-atmosphere carbon-cycle on glacial-interglacial time scales numerical biogeochemical models are required. In this work, a modeling approach consisting of a coupling between a newly developed biogeochemical box model (16 oceanic boxes) and a three-dimensional (3D) ocean general circulation model (OGCM) was pursued. The simulation of biogeochemical processes by the box model is almost identical to state of the art 3D-models. The global OGCM (4 x 6 , 12 layers) is forced by temperature and salinity fields obtained from paleoceanographic time-slice reconstructions, and model-derived wind fields. This model setup offers several advantages: (1) The box model is driven by waterfluxes that are diagnosed from the OGCM-fields. This approach results in hydrodynamically consistent water-fluxes for the box model. (2) The OGCM results guide the selection of appropriate box-configurations for time-slices having water-mass distributions that differ from the present-day situation. (3) The high numerical efficiency of the biogeochemical model component allows for a sufficient number of sensitivity experiments. (4) Based on paleoceanographic information, the boundary conditions of the box model can be combined as a function of time in order to conduct time-dependent experiments with the box model. (orig.) [Deutsch] Die globale Quantifizierung von Veraenderungen des Ozean-Atmosphaere-Kohlenstoffkreislaufes auf glazial-interglazialen Zeitskalen erfordert den Einsatz numerischer biogeochemischer Modelle. Im Rahmen dieser Arbeit wurde hierzu ein Modellansatz gewaehlt, der aus der Kopplung eines neu entwickelten biogeochemischen Boxmodells (16 ozeanische Boxen) an ein dreidimensionales (3D) allgemeines Ozean-Zirkulationsmodell (OGCM) besteht. Die Simulation biogeochemischer Prozesse erfolgt im Boxmodell analog zu hochentwickelten 3D-Modellen. Das globale (4 x 6 , 12 Schichten) Ozeanmodell wird mit Temperatur- und Salzgehaltsfeldern, die

  6. A clinical prediction rule for detecting major depressive disorder in primary care: the PREDICT-NL study.

    Science.gov (United States)

    Zuithoff, Nicolaas P A; Vergouwe, Yvonne; King, Michael; Nazareth, Irwin; Hak, Eelko; Moons, Karel G M; Geerlings, Mirjam I

    2009-08-01

    Major depressive disorder often remains unrecognized in primary care. Development of a clinical prediction rule using easily obtainable predictors for major depressive disorder in primary care patients. A total of 1046 subjects, aged 18-65 years, were included from seven large general practices in the center of The Netherlands. All subjects were recruited in the general practice waiting room, irrespective of their presenting complaint. Major depressive disorder according to Diagnostic and Statistical Manual of Mental Disorders, Fourth Text Revision edition criteria was assessed with the Composite International Diagnostic Interview. Candidate predictors were gender, age, educational level, being single, number of presented complaints, presence of non-somatic complaints, whether a diagnosis was assigned, consultation rate in past 12 months, presentation of depressive complaints or prescription of antidepressants in past 12 months, number of life events in past 6 months and any history of depression. The first multivariable logistic regression model including only predictors that require no confronting depression-related questions had a reasonable degree of discrimination (area under the receiver operating characteristic curve or concordance-statistic (c-statistic) = 0.71; 95% Confidence Interval (CI): 0.67-0.76). Addition of three simple though more depression-related predictors, number of life events and history of depression, significantly increased the c-statistic to 0.80 (95% CI: 0.76-0.83). After transforming this second model to an easily to use risk score, the lowest risk category (sum score depression, which increased to 49% in the highest category (sum score > or = 30). A clinical prediction rule allows GPs to identify patients-irrespective of their complaints-in whom diagnostic workup for major depressive disorder is indicated.

  7. Screening Characteristics of TIMI Score in Predicting Acute Coronary Syndrome Outcome; a Diagnostic Accuracy Study

    Directory of Open Access Journals (Sweden)

    Mostafa Alavi-Moghaddam

    2017-01-01

    Full Text Available Introduction: In cases with potential diagnosis of ischemic chest pain, screening high risk patients for adverse outcomes would be very helpful. The present study was designed aiming to determine the diagnostic accuracy of thrombolysis in myocardial infarction (TIMI score in Patients with potential diagnosis of ischemic chest pain.Method: This diagnostic accuracy study was designed to evaluate the screening performance characteristics of TIMI score in predicting 30-day outcomes of mortality, myocardial infarction (MI, and need for revascularization in patients presenting to ED with complaint of typical chest pain and diagnosis of unstable angina or Non-ST elevation MI.Results: 901 patients with the mean age of 58.17 ± 15.00 years (19-90 were studied (52.9% male. Mean TIMI score of the studied patients was 0.97 ± 0.93 (0-5 and the highest frequency of the score belonged to 0 to 2 with 37.2%, 35.3%, and 21.4%, respectively. In total, 170 (18.8% patients experienced the outcomes evaluated in this study. Total sensitivity, specificity, positive and negative predictive value, and positive and negative likelihood ratio of TIMI score were 20 (95% CI: 17 – 24, 99 (95% CI: 97 – 100, 98 (95% CI: 93 – 100, 42 (95% CI: 39 – 46, 58 (95% CI: 14 – 229, and 1.3 (95% CI: 1.2 – 1.4, respectively. Area under the ROC curve of this system for prediction of 30-day mortality, MI, and need for revascularization were 0.51 (95% CI: 0.47 – 0.55, 0.58 (95% CI: 0.54 – 0.62 and 0.56 (95% CI: 0.52 – 0.60, respectively.Conclusion: Based on the findings of the present study, it seems that TIMI score has a high specificity in predicting 30-day adverse outcomes of mortality, MI, and need for revascularization following acute coronary syndrome. However, since its sensitivity, negative predictive value, and negative likelihood ratio are low, it cannot be used as a proper screening tool for ruling out low risk patients in ED.

  8. Continuous electroencephalography predicts delayed cerebral ischemia after subarachnoid hemorrhage: A prospective study of diagnostic accuracy.

    Science.gov (United States)

    Rosenthal, Eric S; Biswal, Siddharth; Zafar, Sahar F; O'Connor, Kathryn L; Bechek, Sophia; Shenoy, Apeksha V; Boyle, Emily J; Shafi, Mouhsin M; Gilmore, Emily J; Foreman, Brandon P; Gaspard, Nicolas; Leslie-Mazwi, Thabele M; Rosand, Jonathan; Hoch, Daniel B; Ayata, Cenk; Cash, Sydney S; Cole, Andrew J; Patel, Aman B; Westover, M Brandon

    2018-04-16

    Delayed cerebral ischemia (DCI) is a common, disabling complication of subarachnoid hemorrhage (SAH). Preventing DCI is a key focus of neurocritical care, but interventions carry risk and cannot be applied indiscriminately. Although retrospective studies have identified continuous electroencephalographic (cEEG) measures associated with DCI, no study has characterized the accuracy of cEEG with sufficient rigor to justify using it to triage patients to interventions or clinical trials. We therefore prospectively assessed the accuracy of cEEG for predicting DCI, following the Standards for Reporting Diagnostic Accuracy Studies. We prospectively performed cEEG in nontraumatic, high-grade SAH patients at a single institution. The index test consisted of clinical neurophysiologists prospectively reporting prespecified EEG alarms: (1) decreasing relative alpha variability, (2) decreasing alpha-delta ratio, (3) worsening focal slowing, or (4) late appearing epileptiform abnormalities. The diagnostic reference standard was DCI determined by blinded, adjudicated review. Primary outcome measures were sensitivity and specificity of cEEG for subsequent DCI, determined by multistate survival analysis, adjusted for baseline risk. One hundred three of 227 consecutive patients were eligible and underwent cEEG monitoring (7.7-day mean duration). EEG alarms occurred in 96.2% of patients with and 19.6% without subsequent DCI (1.9-day median latency, interquartile range = 0.9-4.1). Among alarm subtypes, late onset epileptiform abnormalities had the highest predictive value. Prespecified EEG findings predicted DCI among patients with low (91% sensitivity, 83% specificity) and high (95% sensitivity, 77% specificity) baseline risk. cEEG accurately predicts DCI following SAH and may help target therapies to patients at highest risk of secondary brain injury. Ann Neurol 2018. © 2018 American Neurological Association.

  9. Analysis, prediction, and case studies of early-age cracking in bridge decks

    Science.gov (United States)

    ElSafty, Adel; Graeff, Matthew K.; El-Gharib, Georges; Abdel-Mohti, Ahmed; Mike Jackson, N.

    2016-06-01

    Early-age cracking can adversely affect strength, serviceability, and durability of concrete bridge decks. Early age is defined as the period after final setting, during which concrete properties change rapidly. Many factors can cause early-age bridge deck cracking including temperature change, hydration, plastic shrinkage, autogenous shrinkage, and drying shrinkage. The cracking may also increase the effect of freeze and thaw cycles and may lead to corrosion of reinforcement. This research paper presents an analysis of causes and factors affecting early-age cracking. It also provides a tool developed to predict the likelihood and initiation of early-age cracking of concrete bridge decks. Understanding the concrete properties is essential so that the developed tool can accurately model the mechanisms contributing to the cracking of concrete bridge decks. The user interface of the implemented computer Excel program enables the user to input the properties of the concrete being monitored. The research study and the developed spreadsheet were used to comprehensively investigate the issue of concrete deck cracking. The spreadsheet is designed to be a user-friendly calculation tool for concrete mixture proportioning, temperature prediction, thermal analysis, and tensile cracking prediction. The study also provides review and makes recommendations on the deck cracking based mainly on the Florida Department of Transportation specifications and Structures Design Guidelines, and Bridge Design Manuals of other states. The results were also compared with that of other commercially available software programs that predict early-age cracking in concrete slabs, concrete pavement, and reinforced concrete bridge decks. The outcome of this study can identify a set of recommendations to limit the deck cracking problem and maintain a longer service life of bridges.

  10. Diagnostic test of predicted height model in Indonesian elderly: a study in an urban area

    Directory of Open Access Journals (Sweden)

    Fatmah Fatmah

    2010-08-01

    Full Text Available Aim In an anthropometric assessment, elderly are frequently unable to measure their height due to mobility and skeletal deformities. An alternative is to use a surrogate value of stature from arm span, knee height, and sitting height. The equations developed for predicting height in Indonesian elderly using these three predictors. The equations put in the nutritional assessment card (NSA of older people. Before the card which is the first new technology in Indonesia will be applied in the community, it should be tested. The study aimed was to conduct diagnostic test of predicted height model in the card compared to actual height.Methods Model validation towards 400 healthy elderly conducted in Jakarta City with cross-sectional design. The study was the second validation test of the model besides Depok City representing semi urban area which was undertaken as the first study.Result Male elderly had higher mean age, height, weight, arm span, knee height, and sitting height as compared to female elderly. The highest correlation between knee height and standing height was similar in women (r = 0.80; P < 0.001 and men (r = 0.78; P < 0.001, and followed by arm span and sitting height. Knee height had the lowest difference with standing height in men (3.13 cm and women (2.79 cm. Knee height had the biggest sensitivity (92.2%, and the highest specificity on sitting height (91.2%.Conclusion Stature prediction equation based on knee-height, arm span, and sitting height are applicable for nutritional status assessment in Indonesian elderly. (Med J Indones 2010;19:199-204Key words: diagnostic test, elderly, predicted height model

  11. SU-D-BRA-01: Feasibility Study for Swallowing Prediction Using Pressure Sensors

    International Nuclear Information System (INIS)

    Cho, M; Kim, T; Kim, D; Kang, S; Kim, K; Shin, D; Noh, Y; Suh, T; Kim, S

    2016-01-01

    Purpose: To develop a swallowing prediction system (SPS) using force sensing sensors and evaluate its feasibility. Methods: The SPS developed consists of force sensing sensor units, a thermoplastic mask, a signal transport device and a control PC installed with an in-house software. The SPS is designed to predict the pharyngeal stage of swallowing because it is known that internal organ movement occurs in pharyngeal stage. To detect prediction signal in the SPS, the force sensing sensor units were attached on both the submental muscle region and thyroid cartilage region of the thermoplastic mask. While the signal from the thyroid cartilage region informs the action of swallowing, the signal from the submental muscle region is utilized as a precursor for swallowing. Since the duration of swallowing is relatively short, using such precursor (or warning) signals for machine control is considered more beneficial. A volunteer study was conducted to evaluate the feasibility of the system. In this volunteer study, we intended to verify that the system could predict the pharyngeal stage of the swallowing. We measured time gaps between obtaining the warning signals in the SPS and starting points of the pharyngeal stage of swallowing. Results: The measured data was examined whether the time gaps were in reasonable order to be easily utilized. The mean and standard deviation values of these time gaps were 0.550 s ± 0.183 s. in 8 volunteers. Conclusion: The proposed method was able to predict the on-set of swallowing of human subjects inside the thermoplastic mask, which has never been possible with other monitoring systems such as camera-based monitoring system. With the prediction ability of swallowing incorporated into the machine control mechanism (in the future), beam delivery can be controlled to skip swallowing periods and significant dosimetric gain is expected in head & neck cancer treatments. This work was supported by the Radiation Technology R&D program (No. 2015M

  12. Shear wave elastography of thyroid nodules for the prediction of malignancy in a large scale study

    Energy Technology Data Exchange (ETDEWEB)

    Park, Ah Young; Son, Eun Ju [Department of Radiology, Yonsei University College of Medicine, Gangnam Severance Hospital, Seoul (Korea, Republic of); Han, Kyunghwa [Biostatistics Collaboration Unit, Gangnam Medical Research Center, Yonsei University College of Medicine, Gangnam Severance Hospital, Seoul (Korea, Republic of); Youk, Ji Hyun [Department of Radiology, Yonsei University College of Medicine, Gangnam Severance Hospital, Seoul (Korea, Republic of); Kim, Jeong-Ah, E-mail: chrismd@hanmail.net [Department of Radiology, Yonsei University College of Medicine, Gangnam Severance Hospital, Seoul (Korea, Republic of); Park, Cheong Soo [Department of Surgery, Yonsei University College of Medicine, Gangnam Severance Hospital, Seoul (Korea, Republic of)

    2015-03-15

    Highlights: •Elasticity indices of malignant thyroid nodules were higher than those of benign. •High elasticity indices were the independent predictors of thyroid malignancy. •SWE evaluation could be useful as adjunctive tool for thyroid cancer diagnosis. -- Abstract: Objectives: The purpose of this study is to validate the usefulness of shear wave elastography (SWE) in predicting thyroid malignancy with a large-scale quantitative SWE data. Methods: This restrospective study included 476 thyroid nodules in 453 patients who underwent gray-scale US and SWE before US-guided fine-needle aspiration biopsy (US-FNA) or surgical excision were included. Gray-scale findings and SWE elasticity indices (EIs) were retrospectively reviewed and compared between benign and malignant thyroid nodules. The optimal cut-off values of EIs for predicting malignancy were determined. The diagnostic performances of gray-scale US and SWE for predicting malignancy were analyzed. The diagnostic performance was compared between the gray-scale US findings only and the combined use of gray-scale US findings with SWEs. Results: All EIs of malignant thyroid nodules were significantly higher than those of benign nodules (p ≤ .001). The optimal cut-off value of each EI for predicting malignancy was 85.2 kPa of E{sub mean}, 94.0 kPa of E{sub max}, 54.0 kPa of E{sub min}. E{sub mean} (OR 3.071, p = .005) and E{sub max} (OR 3.015, p = .003) were the independent predictors of thyroid malignancy. Combined use of gray-scale US findings and each EI showed elevated sensitivity (95.0–95.5% vs 92.9%, p ≤ .005) and AUC (0.820–0.834 vs 0.769, p ≤ .005) for predicting malignancy, compared with the use of only gray-scale US findings. Conclusions: Quantitative parameters of SWE were the independent predictors of thyroid malignancy and SWE evaluation combined with gray-scale US was adjunctive to the diagnostic performance of gray-scale US for predicting thyroid malignancy.

  13. SU-D-BRA-01: Feasibility Study for Swallowing Prediction Using Pressure Sensors

    Energy Technology Data Exchange (ETDEWEB)

    Cho, M; Kim, T; Kim, D; Kang, S; Kim, K; Shin, D; Noh, Y; Suh, T [The Catholic University of Korea College of Medicine, Department of Biomedical Engineering, Research Institute of Biomedical Engineering, Seoul (Korea, Republic of); Kim, S [Virginia Commonwealth University, Richmond, VA (United States)

    2016-06-15

    Purpose: To develop a swallowing prediction system (SPS) using force sensing sensors and evaluate its feasibility. Methods: The SPS developed consists of force sensing sensor units, a thermoplastic mask, a signal transport device and a control PC installed with an in-house software. The SPS is designed to predict the pharyngeal stage of swallowing because it is known that internal organ movement occurs in pharyngeal stage. To detect prediction signal in the SPS, the force sensing sensor units were attached on both the submental muscle region and thyroid cartilage region of the thermoplastic mask. While the signal from the thyroid cartilage region informs the action of swallowing, the signal from the submental muscle region is utilized as a precursor for swallowing. Since the duration of swallowing is relatively short, using such precursor (or warning) signals for machine control is considered more beneficial. A volunteer study was conducted to evaluate the feasibility of the system. In this volunteer study, we intended to verify that the system could predict the pharyngeal stage of the swallowing. We measured time gaps between obtaining the warning signals in the SPS and starting points of the pharyngeal stage of swallowing. Results: The measured data was examined whether the time gaps were in reasonable order to be easily utilized. The mean and standard deviation values of these time gaps were 0.550 s ± 0.183 s. in 8 volunteers. Conclusion: The proposed method was able to predict the on-set of swallowing of human subjects inside the thermoplastic mask, which has never been possible with other monitoring systems such as camera-based monitoring system. With the prediction ability of swallowing incorporated into the machine control mechanism (in the future), beam delivery can be controlled to skip swallowing periods and significant dosimetric gain is expected in head & neck cancer treatments. This work was supported by the Radiation Technology R&D program (No. 2015M

  14. Shear wave elastography of thyroid nodules for the prediction of malignancy in a large scale study

    International Nuclear Information System (INIS)

    Park, Ah Young; Son, Eun Ju; Han, Kyunghwa; Youk, Ji Hyun; Kim, Jeong-Ah; Park, Cheong Soo

    2015-01-01

    Highlights: •Elasticity indices of malignant thyroid nodules were higher than those of benign. •High elasticity indices were the independent predictors of thyroid malignancy. •SWE evaluation could be useful as adjunctive tool for thyroid cancer diagnosis. -- Abstract: Objectives: The purpose of this study is to validate the usefulness of shear wave elastography (SWE) in predicting thyroid malignancy with a large-scale quantitative SWE data. Methods: This restrospective study included 476 thyroid nodules in 453 patients who underwent gray-scale US and SWE before US-guided fine-needle aspiration biopsy (US-FNA) or surgical excision were included. Gray-scale findings and SWE elasticity indices (EIs) were retrospectively reviewed and compared between benign and malignant thyroid nodules. The optimal cut-off values of EIs for predicting malignancy were determined. The diagnostic performances of gray-scale US and SWE for predicting malignancy were analyzed. The diagnostic performance was compared between the gray-scale US findings only and the combined use of gray-scale US findings with SWEs. Results: All EIs of malignant thyroid nodules were significantly higher than those of benign nodules (p ≤ .001). The optimal cut-off value of each EI for predicting malignancy was 85.2 kPa of E mean , 94.0 kPa of E max , 54.0 kPa of E min . E mean (OR 3.071, p = .005) and E max (OR 3.015, p = .003) were the independent predictors of thyroid malignancy. Combined use of gray-scale US findings and each EI showed elevated sensitivity (95.0–95.5% vs 92.9%, p ≤ .005) and AUC (0.820–0.834 vs 0.769, p ≤ .005) for predicting malignancy, compared with the use of only gray-scale US findings. Conclusions: Quantitative parameters of SWE were the independent predictors of thyroid malignancy and SWE evaluation combined with gray-scale US was adjunctive to the diagnostic performance of gray-scale US for predicting thyroid malignancy

  15. Prediction of BMI at age 11 in a longitudinal sample of the Ulm Birth Cohort Study.

    Directory of Open Access Journals (Sweden)

    Hanna Christiansen

    Full Text Available Obesity is one of the greatest public health challenges in the world with childhood prevalence rates between 20-26% and numerous associated health risks. The aim of the current study was to analyze the 11-year follow-up data of the Ulm Birth Cohort Study (UBCS, to identify whether abnormal eating behavior patterns, especially restrained eating, predict body mass index (BMI at 11 years of age and to explore other factors known to be longitudinally associated with it. Of the original UBCS, n = 422 children (~ 40% of the original sample and their parents participated in the 11-year follow-up. BMI at age 8 and 11 as well as information on restrained eating, psychological problems, depressive symptoms, lifestyle, and IQ at age 8 were assessed. Partial Least Squares Structural Equation Modeling (PLS-SEM was used to predict children's BMI scores at age 11. PLS-SEM explained 68% of the variance of BMI at age 11, with BMI at age 8 being the most important predictor. Restrained eating, via BMI at age 8 as well as parental BMI, had further weak associations with BMI at age 11; no other predictor was statistically significant. Since established overweight at age 8 already predicts BMI scores at age 11 longitudinally, obesity interventions should be implemented in early childhood.

  16. A predictability study of Lorenz's 28-variable model as a dynamical system

    Science.gov (United States)

    Krishnamurthy, V.

    1993-01-01

    The dynamics of error growth in a two-layer nonlinear quasi-geostrophic model has been studied to gain an understanding of the mathematical theory of atmospheric predictability. The growth of random errors of varying initial magnitudes has been studied, and the relation between this classical approach and the concepts of the nonlinear dynamical systems theory has been explored. The local and global growths of random errors have been expressed partly in terms of the properties of an error ellipsoid and the Liapunov exponents determined by linear error dynamics. The local growth of small errors is initially governed by several modes of the evolving error ellipsoid but soon becomes dominated by the longest axis. The average global growth of small errors is exponential with a growth rate consistent with the largest Liapunov exponent. The duration of the exponential growth phase depends on the initial magnitude of the errors. The subsequent large errors undergo a nonlinear growth with a steadily decreasing growth rate and attain saturation that defines the limit of predictability. The degree of chaos and the largest Liapunov exponent show considerable variation with change in the forcing, which implies that the time variation in the external forcing can introduce variable character to the predictability.

  17. Nomogram to Predict Graft Thickness in Descemet Stripping Automated Endothelial Keratoplasty: An Eye Bank Study.

    Science.gov (United States)

    Bae, Steven S; Menninga, Isaac; Hoshino, Richard; Humphreys, Christine; Chan, Clara C

    2018-06-01

    The purpose of this study was to develop a nomogram to predict postcut thickness of corneal grafts prepared at an eye bank for Descemet stripping automated endothelial keratoplasty (DSAEK). Retrospective chart review was performed of DSAEK graft preparations by 3 experienced technicians from April 2012 to May 2017 at the Eye Bank of Canada-Ontario Division. Variables collected included the following: donor demographics, death-to-preservation time, death-to-processing time, precut tissue thickness, postcut tissue thickness, microkeratome head size, endothelial cell count, cut technician, and rate of perforation. Linear regression models were generated for each microkeratome head size (300 and 350 μm). A total of 780 grafts were processed during the study period. Twelve preparation attempts resulted in perforation (1.5%) and were excluded. Mean precut tissue thickness was 510 ± 49 μm (range: 363-670 μm). Mean postcut tissue thickness was 114 ± 22 μm (range: 57-193 μm). Seventy-nine percent (608/768) of grafts were ≤130 μm. The linear regression models included precut thickness and donor age, which were able to predict the thickness to within 25 μm 80% of the time. We report a nomogram to predict thickness of DSAEK corneal grafts prepared in an eye bank setting, which was accurate to within 25 μm 80% of the time. Other eye banks could consider performing similar analyses.

  18. A correlational and predictive study of creativity and personality of college students.

    Science.gov (United States)

    Sanz de Acedo Baquedano, María Teresa; Sanz de Acedo Lizarraga, María Luisa

    2012-11-01

    The goals of this study were to examine the relationship between creativity and personality, to identify what personality variables better predict creativity, and to determine whether significant differences exist among them in relation to gender. The research was conducted with a sample of 87 students at the Universidad Pública de Navarra, Spain. We administered the Creative Intelligence Test (CREA), which provides a cognitive measure for creativity and the Situational Personality Questionnaire (SPQ), which is composed of 15 personality features. Positive and significant correlations between creativity and independence, cognitive control, and tolerance personality scales were found. Negative and significant correlations between creativity and anxious, dominant, and aggressive personalities were also found. Moreover, four personality variables that positively predicted creativity (efficacy, independence, cognitive control, and integrity-honesty) and another four that negatively predicted creativity (emotional stability, anxiety, dominance, and leadership) were identified. The results did not show significant differences in creativity and personality in relation to gender, except in self-concept and in social adjustment. In conclusion, the results from this study can potentially be used to expand the types of features that support creative personalities.

  19. Hemoglobin and hematocrit levels in the prediction of complicated Crohn's disease behavior--a cohort study.

    Science.gov (United States)

    Rieder, Florian; Paul, Gisela; Schnoy, Elisabeth; Schleder, Stephan; Wolf, Alexandra; Kamm, Florian; Dirmeier, Andrea; Strauch, Ulrike; Obermeier, Florian; Lopez, Rocio; Achkar, Jean-Paul; Rogler, Gerhard; Klebl, Frank

    2014-01-01

    Markers that predict the occurrence of a complicated disease behavior in patients with Crohn's disease (CD) can permit a more aggressive therapeutic regimen for patients at risk. The aim of this cohort study was to test the blood levels of hemoglobin (Hgb) and hematocrit (Hct) for the prediction of complicated CD behavior and CD related surgery in an adult patient population. Blood samples of 62 CD patients of the German Inflammatory Bowel Disease-network "Kompetenznetz CED" were tested for the levels of Hgb and Hct prior to the occurrence of complicated disease behavior or CD related surgery. The relation of these markers and clinical events was studied using Kaplan-Meier survival analysis and adjusted COX-proportional hazard regression models. The median follow-up time was 55.8 months. Of the 62 CD patients without any previous complication or surgery 34% developed a complication and/or underwent CD related surgery. Low Hgb or Hct levels were independent predictors of a shorter time to occurrence of the first complication or CD related surgery. This was true for early as well as late occurring complications. Stable low Hgb or Hct during serial follow-up measurements had a higher frequency of complications compared to patients with a stable normal Hgb or Hct, respectively. Determination of Hgb or Hct in complication and surgery naïve CD patients might serve as an additional tool for the prediction of complicated disease behavior.

  20. Basic study on dynamic reactive-power control method with PV output prediction for solar inverter

    Directory of Open Access Journals (Sweden)

    Ryunosuke Miyoshi

    2016-01-01

    Full Text Available To effectively utilize a photovoltaic (PV system, reactive-power control methods for solar inverters have been considered. Among the various methods, the constant-voltage control outputs less reactive power compared with the other methods. We have developed a constant-voltage control to reduce the reactive-power output. However, the developed constant-voltage control still outputs unnecessary reactive power because the control parameter is constant in every waveform of the PV output. To reduce the reactive-power output, we propose a dynamic reactive-power control method with a PV output prediction. In the proposed method, the control parameter is varied according to the properties of the predicted PV waveform. In this study, we performed numerical simulations using a distribution system model, and we confirmed that the proposed method reduces the reactive-power output within the voltage constraint.

  1. Dose Prediction for surface nuclear explosions: case studies for Semipalatinsk and Lop Nur tests

    International Nuclear Information System (INIS)

    Takada, Jun

    2008-01-01

    Dose prediction method RAPS after surface nuclear explosion has been developed by using the empirical dose function of USA nuclear test. This method which provides us external total dose, dose rate at any distant, at any time for any yield of nuclear explosion, is useful for radiation protection in case of nuclear events such as terrorism and nuclear war. The validity of RAPS has been confirmed by application to historical surface nuclear test explosions. The first test case study which was done for the first test explosion of the former USSR at the Semipalatinsk Nuclear Test Site on August 29th 1949, shows a good agreement with luminescence dosimetry on a brick. This dose prediction method was applied nuclear tests in Lop Nur. The results indicate dangerous nuclear radiation influences including fatal risk in the wide Uygur area. (author)

  2. Predictive time-series modeling using artificial neural networks for Linac beam symmetry: an empirical study.

    Science.gov (United States)

    Li, Qiongge; Chan, Maria F

    2017-01-01

    Over half of cancer patients receive radiotherapy (RT) as partial or full cancer treatment. Daily quality assurance (QA) of RT in cancer treatment closely monitors the performance of the medical linear accelerator (Linac) and is critical for continuous improvement of patient safety and quality of care. Cumulative longitudinal QA measurements are valuable for understanding the behavior of the Linac and allow physicists to identify trends in the output and take preventive actions. In this study, artificial neural networks (ANNs) and autoregressive moving average (ARMA) time-series prediction modeling techniques were both applied to 5-year daily Linac QA data. Verification tests and other evaluations were then performed for all models. Preliminary results showed that ANN time-series predictive modeling has more advantages over ARMA techniques for accurate and effective applicability in the dosimetry and QA field. © 2016 New York Academy of Sciences.

  3. Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT: A Multicenter Study.

    Science.gov (United States)

    Betancur, Julian; Commandeur, Frederic; Motlagh, Mahsaw; Sharir, Tali; Einstein, Andrew J; Bokhari, Sabahat; Fish, Mathews B; Ruddy, Terrence D; Kaufmann, Philipp; Sinusas, Albert J; Miller, Edward J; Bateman, Timothy M; Dorbala, Sharmila; Di Carli, Marcelo; Germano, Guido; Otaki, Yuka; Tamarappoo, Balaji K; Dey, Damini; Berman, Daniel S; Slomka, Piotr J

    2018-03-12

    The study evaluated the automatic prediction of obstructive disease from myocardial perfusion imaging (MPI) by deep learning as compared with total perfusion deficit (TPD). Deep convolutional neural networks trained with a large multicenter population may provide improved prediction of per-patient and per-vessel coronary artery disease from single-photon emission computed tomography MPI. A total of 1,638 patients (67% men) without known coronary artery disease, undergoing stress 99m Tc-sestamibi or tetrofosmin MPI with new generation solid-state scanners in 9 different sites, with invasive coronary angiography performed within 6 months of MPI, were studied. Obstructive disease was defined as ≥70% narrowing of coronary arteries (≥50% for left main artery). Left ventricular myocardium was segmented using clinical nuclear cardiology software and verified by an expert reader. Stress TPD was computed using sex- and camera-specific normal limits. Deep learning was trained using raw and quantitative polar maps and evaluated for prediction of obstructive stenosis in a stratified 10-fold cross-validation procedure. A total of 1,018 (62%) patients and 1,797 of 4,914 (37%) arteries had obstructive disease. Area under the receiver-operating characteristic curve for disease prediction by deep learning was higher than for TPD (per patient: 0.80 vs. 0.78; per vessel: 0.76 vs. 0.73: p deep learning threshold set to the same specificity as TPD, per-patient sensitivity improved from 79.8% (TPD) to 82.3% (deep learning) (p deep learning) (p Deep learning has the potential to improve automatic interpretation of MPI as compared with current clinical methods. Copyright © 2018 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

  4. A predictive data-driven framework for endocrine prioritization: a triazole fungicide case study

    Science.gov (United States)

    Paul Friedman, Katie; Papineni, Sabitha; Marty, M. Sue; Yi, Kun Don; Goetz, Amber K.; Rasoulpour, Reza J.; Kwiatkowski, Pat; Wolf, Douglas C.; Blacker, Ann M.; Peffer, Richard C.

    2016-01-01

    Abstract The US Environmental Protection Agency Endocrine Disruptor Screening Program (EDSP) is a tiered screening approach to determine the potential for a chemical to interact with estrogen, androgen, or thyroid hormone systems and/or perturb steroidogenesis. Use of high-throughput screening (HTS) to predict hazard and exposure is shifting the EDSP approach to (1) prioritization of chemicals for further screening; and (2) targeted use of EDSP Tier 1 assays to inform specific data needs. In this work, toxicology data for three triazole fungicides (triadimefon, propiconazole, and myclobutanil) were evaluated, including HTS results, EDSP Tier 1 screening (and other scientifically relevant information), and EPA guideline mammalian toxicology study data. The endocrine-related bioactivity predictions from HTS and information that satisfied the EDSP Tier 1 requirements were qualitatively concordant. Current limitations in the available HTS battery for thyroid and steroidogenesis pathways were mitigated by inclusion of guideline toxicology studies in this analysis. Similar margins (3–5 orders of magnitude) were observed between HTS-predicted human bioactivity and exposure values and between in vivo mammalian bioactivity and EPA chronic human exposure estimates for these products’ registered uses. Combined HTS hazard and human exposure predictions suggest low priority for higher-tiered endocrine testing of these triazoles. Comparison with the mammalian toxicology database indicated that this HTS-based prioritization would have been protective for any potential in vivo effects that form the basis of current risk assessment for these chemicals. This example demonstrates an effective, human health protective roadmap for EDSP evaluation of pesticide active ingredients via prioritization using HTS and guideline toxicology information. PMID:27347635

  5. Prediction of Brittle Failure for TBM Tunnels in Anisotropic Rock: A Case Study from Northern Norway

    Science.gov (United States)

    Dammyr, Øyvind

    2016-06-01

    Prediction of spalling and rock burst is especially important for hard rock TBM tunneling, because failure can have larger impact than in a drill and blast tunnel and ultimately threaten excavation feasibility. The majority of research on brittle failure has focused on rock types with isotropic behavior. This paper gives a review of existing theory and its application before a 3.5-m-diameter TBM tunnel in foliated granitic gneiss is used as a case to study brittle failure characteristics of anisotropic rock. Important aspects that should be considered in order to predict brittle failure in anisotropic rock are highlighted. Foliation is responsible for considerable strength anisotropy and is believed to influence the preferred side of v-shaped notch development in the investigated tunnel. Prediction methods such as the semi- empirical criterion, the Hoek- Brown brittle parameters, and the non-linear damage initiation and spalling limit method give reliable results; but only as long as the angle between compression axis and foliation in uniaxial compressive tests is relevant, dependent on the relation between tunnel trend/plunge, strike/dip of foliation, and tunnel boundary stresses. It is further demonstrated that local in situ stress variations, for example, due to the presence of discontinuities, can have profound impact on failure predictions. Other carefully documented case studies into the brittle failure nature of rock, in particular anisotropic rock, are encouraged in order to expand the existing and relatively small database. This will be valuable for future TBM planning and construction stages in highly stressed brittle anisotropic rock.

  6. Predicting the probability of slip in gait: methodology and distribution study.

    Science.gov (United States)

    Gragg, Jared; Yang, James

    2016-01-01

    The likelihood of a slip is related to the available and required friction for a certain activity, here gait. Classical slip and fall analysis presumed that a walking surface was safe if the difference between the mean available and required friction coefficients exceeded a certain threshold. Previous research was dedicated to reformulating the classical slip and fall theory to include the stochastic variation of the available and required friction when predicting the probability of slip in gait. However, when predicting the probability of a slip, previous researchers have either ignored the variation in the required friction or assumed the available and required friction to be normally distributed. Also, there are no published results that actually give the probability of slip for various combinations of required and available frictions. This study proposes a modification to the equation for predicting the probability of slip, reducing the previous equation from a double-integral to a more convenient single-integral form. Also, a simple numerical integration technique is provided to predict the probability of slip in gait: the trapezoidal method. The effect of the random variable distributions on the probability of slip is also studied. It is shown that both the required and available friction distributions cannot automatically be assumed as being normally distributed. The proposed methods allow for any combination of distributions for the available and required friction, and numerical results are compared to analytical solutions for an error analysis. The trapezoidal method is shown to be highly accurate and efficient. The probability of slip is also shown to be sensitive to the input distributions of the required and available friction. Lastly, a critical value for the probability of slip is proposed based on the number of steps taken by an average person in a single day.

  7. Analysis of MRI by fractals for prediction of sensory attributes: A case study in loin

    DEFF Research Database (Denmark)

    Caballero, Daniel; Antequera, Teresa; Caro, Andrés

    2018-01-01

    This study investigates the use of fractal algorithms to analyse MRI of meat products, specifically loin, in order to determine sensory parameters of loin. For that, the capability of different fractal algorithms was evaluated (Classical Fractal Algorithm, CFA; Fractal Texture Algorithm, FTA...... was analysed. Results on this study firstly demonstrate the capability of fractal algorithms to analyse MRI from meat product. Different combinations of the analysed techniques can be applied for predicting most sensory attributes of loins adequately (R > 0.5). However, the combination of SE, OPFTA and MLR...... offered the most appropriate results. Thus, it could be proposed as an alternative to the traditional food technology methods....

  8. The Prediction of Consumer Buying Intentions: A Comparative Study of the Predictive Efficacy of Two Attitudinal Models. Faculty Working Paper No. 234.

    Science.gov (United States)

    Bhagat, Rabi S.; And Others

    The role of attitudes in the conduct of buyer behavior is examined in the context of two competitive models of attitude structure and attitude-behavior relationship. Specifically, the objectives of the study were to compare the Fishbein and Sheth models on the criteria of predictive as well as cross validities. Data on both the models were…

  9. Research and development studies for predicting the thermal fatigue; Etudes de R and D pour la prediction de la fatigue thermique

    Energy Technology Data Exchange (ETDEWEB)

    Moulin, D.; Garnier, J.; Fissolo, A.; Lejeail, Y. [CEA, 75 - Paris (France); Stephan, J.M.; Moinereau, D.; Masson, J. [Electricite de France, Les Renardieres, 77 - Moret sur Loing (France). Direction des Etudes et Recherches

    2001-07-01

    This paper presents some studies in development or realized in the EDF and CEA laboratories, concerning the thermal fatigue damage in nuclear reactor components. The first part presents the basic principles and the methods of lifetime prediction. The second part gives some examples on sodium loop, water loop, welded junctions resistance to thermal fatigue and tests on fatigue specimen. (A.L.B.)

  10. In silico predictive studies of mAHR congener binding using homology modelling and molecular docking.

    Science.gov (United States)

    Panda, Roshni; Cleave, A Suneetha Susan; Suresh, P K

    2014-09-01

    The aryl hydrocarbon receptor (AHR) is one of the principal xenobiotic, nuclear receptor that is responsible for the early events involved in the transcription of a complex set of genes comprising the CYP450 gene family. In the present computational study, homology modelling and molecular docking were carried out with the objective of predicting the relationship between the binding efficiency and the lipophilicity of different polychlorinated biphenyl (PCB) congeners and the AHR in silico. Homology model of the murine AHR was constructed by several automated servers and assessed by PROCHECK, ERRAT, VERIFY3D and WHAT IF. The resulting model of the AHR by MODWEB was used to carry out molecular docking of 36 PCB congeners using PatchDock server. The lipophilicity of the congeners was predicted using the XLOGP3 tool. The results suggest that the lipophilicity influences binding energy scores and is positively correlated with the same. Score and Log P were correlated with r = +0.506 at p = 0.01 level. In addition, the number of chlorine (Cl) atoms and Log P were highly correlated with r = +0.900 at p = 0.01 level. The number of Cl atoms and scores also showed a moderate positive correlation of r = +0.481 at p = 0.01 level. To the best of our knowledge, this is the first study employing PatchDock in the docking of AHR to the environmentally deleterious congeners and attempting to correlate structural features of the AHR with its biochemical properties with regards to PCBs. The result of this study are consistent with those of other computational studies reported in the previous literature that suggests that a combination of docking, scoring and ranking organic pollutants could be a possible predictive tool for investigating ligand-mediated toxicity, for their subsequent validation using wet lab-based studies. © The Author(s) 2012.

  11. Social Strategies during University Studies Predict Early Career Work Burnout and Engagement: 18-Year Longitudinal Study

    Science.gov (United States)

    Salmela-Aro, Katariina; Tolvanen, Asko; Nurmi, Jari-Erik

    2011-01-01

    This longitudinal study spanning 18 years examined the role of social strategies in early career adaptation. The aim was to find out whether individuals' social strategies measured during their university studies had an impact on work burnout and work engagement measured 10-18 years later. A sample of 292 university students completed the SAQ…

  12. Predicting Workplace Transfer of Learning: A Study of Adult Learners Enrolled in a Continuing Professional Education Training Program

    Science.gov (United States)

    Nafukho, Fredrick Muyia; Alfred, Mary; Chakraborty, Misha; Johnson, Michelle; Cherrstrom, Catherine A.

    2017-01-01

    Purpose: The primary purpose of this study was to predict transfer of learning to workplace among adult learners enrolled in a continuing professional education (CPE) training program, specifically training courses offered through face-to-face, blended and online instruction formats. The study examined the predictive capacity of trainee…

  13. Revisiting EOR Projects in Indonesia through Integrated Study: EOR Screening, Predictive Model, and Optimisation

    KAUST Repository

    Hartono, A. D.; Hakiki, Farizal; Syihab, Z.; Ambia, F.; Yasutra, A.; Sutopo, S.; Efendi, M.; Sitompul, V.; Primasari, I.; Apriandi, R.

    2017-01-01

    EOR preliminary analysis is pivotal to be performed at early stage of assessment in order to elucidate EOR feasibility. This study proposes an in-depth analysis toolkit for EOR preliminary evaluation. The toolkit incorporates EOR screening, predictive, economic, risk analysis and optimisation modules. The screening module introduces algorithms which assimilates statistical and engineering notions into consideration. The United States Department of Energy (U.S. DOE) predictive models were implemented in the predictive module. The economic module is available to assess project attractiveness, while Monte Carlo Simulation is applied to quantify risk and uncertainty of the evaluated project. Optimization scenario of EOR practice can be evaluated using the optimisation module, in which stochastic methods of Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Evolutionary Strategy (ES) were applied in the algorithms. The modules were combined into an integrated package of EOR preliminary assessment. Finally, we utilised the toolkit to evaluate several Indonesian oil fields for EOR evaluation (past projects) and feasibility (future projects). The attempt was able to update the previous consideration regarding EOR attractiveness and open new opportunity for EOR implementation in Indonesia.

  14. Intrinsic predictive factors for ankle sprain in active university students: a prospective study.

    Science.gov (United States)

    de Noronha, M; França, L C; Haupenthal, A; Nunes, G S

    2013-10-01

    The ankle is the joint most affected among the sports-related injuries. The current study investigated whether certain intrinsic factors could predict ankle sprains in active students. The 125 participants were submitted to a baseline assessment in a single session were then followed-up for 52 weeks regarding the occurrence of sprain. The baseline assessment were performed in both ankles and included the questionnaire Cumberland ankle instability tool - Portuguese, the foot lift test, dorsiflexion range of motion, Star Excursion Balance Test (SEBT), the side recognition task, body mass index, and history of previous sprain. Two groups were used for analysis: one with those who suffered an ankle sprain and the other with those who did not suffer an ankle sprain. After Cox regression analysis, participants with history of previous sprain were twice as likely to suffer subsequent sprains [hazard ratio (HR) 2.21 and 95% confidence interval (CI) 1.07-4.57] and people with better performance on the SEBT in the postero-lateral (PL) direction were less likely to suffer a sprain (HR 0.96 and 95% CI 0.92-0.99). History of previous sprain was the strongest predictive factor and a weak performance on SEBT PL was also considered a predictive factor for ankle sprains. © 2012 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  15. Information Mining from Heterogeneous Data Sources: A Case Study on Drought Predictions

    Directory of Open Access Journals (Sweden)

    Getachew B. Demisse

    2017-07-01

    Full Text Available The objective of this study was to develop information mining methodology for drought modeling and predictions using historical records of climate, satellite, environmental, and oceanic data. The classification and regression tree (CART approach was used for extracting drought episodes at different time-lag prediction intervals. Using the CART approach, a number of successful model trees were constructed, which can easily be interpreted and used by decision makers in their drought management decisions. The regression rules produced by CART were found to have correlation coefficients from 0.71–0.95 in rules-alone modeling. The accuracies of the models were found to be higher in the instance and rules model (0.77–0.96 compared to the rules-alone model. From the experimental analysis, it was concluded that different combinations of the nearest neighbor and committee models significantly increase the performances of CART drought models. For more robust results from the developed methodology, it is recommended that future research focus on selecting relevant attributes for slow-onset drought episode identification and prediction.

  16. Use of a multigrid technique to study effects of limited sampling of heterogeneity on transport prediction

    International Nuclear Information System (INIS)

    Cole, C.R.; Foote, H.P.

    1987-02-01

    Reliable ground water transport prediction requires accurate spatial and temporal characterization of a hydrogeologic system. However, cost constraints and the desire to maintain site integrity by minimizing drilling can restrict the amount of spatial sampling that can be obtained to resolve the flow parameter variability associated with heterogeneities. This study quantifies the errors in subsurface transport predictions resulting from incomplete characterization of hydraulic conductivity heterogeneity. A multigrid technique was used to simulate two-dimensional flow velocity fields with high resolution. To obtain these velocity fields, the finite difference code MGRID, which implements a multigrid solution technique, was applied to compute stream functions on a 256-by-256 grid for a variety of hypothetical systems having detailed distributions of hydraulic conductivity. Spatial variability in hydraulic conductivity distributions was characterized by the components in the spectrum of spatial frequencies. A low-pass spatial filtering technique was applied to the base case hydraulic conductivity distribution to produce a data set with lower spatial frequency content. Arrival time curves were then calculated for filtered hydraulic conductivity distribution and compared to base case results to judge the relative importance of the higher spatial frequency components. Results indicate a progression from multimode to single-mode arrival time curves as the number and extent of distinct flow pathways are reduced by low-pass filtering. This relationship between transport predictions and spatial frequencies was used to judge the consequences of sampling the hydraulic conductivity with reduced spatial resolution. 22 refs., 17 figs

  17. Women's gender role orientation predicts their drinking patterns: a follow-up study of Czech women.

    Science.gov (United States)

    Kubicka, Ludek; Csémy, Ladislav

    2008-06-01

    Evaluation of the hypothesis that women's non-traditional gender role orientation contributes to drinking patterns typical for men. A two-wave prospective study with data collected in 1992 and 1997. The data reflect Czech women's changing gender role orientation and their drinking patterns during a historical period of post-totalitarian societal transformation. A representative cohort of 497 Prague women aged 30-59 years in 1997. Face-to-face interview data on drinking patterns and individually collected original questionnaire on gender role orientation. An analysis of the principal components of the gender role orientation questionnaire has led to four components, designated as egalitarianism, liberalism, feminism and hedonism. Constructed role orientation scales had Cronbachs's alpha reliabilities ranging from 0.57 to 0.74. With possible confounders controlled (thanks mainly to the prospective design), non-traditional gender role orientation components assessed in 1992 predicted the usual quantities of alcohol women have consumed per occasion in 1997, as well as three hazardous drinking patterns (occasional use of > or = 96 g alcohol, usual use of > or = 48 g and daily intake of > or = 40 g). Specifically, women's usual quantity per occasion and occasional use of > or = 96 g were predicted by egalitarianism and hedonism, and hedonism predicted usual use of > or = 48 g as well as average daily intake of > or = 40 g ethanol. Women's gender role orientation can be associated with their drinking patterns with non-traditional gender role identification being associated with greater likelihood of hazardous drinking.

  18. Pelvic Incidence: A Predictive Factor for Three-Dimensional Acetabular Orientation—A Preliminary Study

    Directory of Open Access Journals (Sweden)

    Christophe Boulay

    2014-01-01

    Full Text Available Acetabular cup orientation (inclination and anteversion is a fundamental topic in orthopaedics and depends on pelvis tilt (positional parameter emphasising the notion of a safe range of pelvis tilt. The hypothesis was that pelvic incidence (morphologic parameter could yield a more accurate and reliable assessment than pelvis tilt. The aim was to find out a predictive equation of acetabular 3D orientation parameters which were determined by pelvic incidence to include in the model. The second aim was to consider the asymmetry between the right and left acetabulae. Twelve pelvic anatomic specimens were measured with an electromagnetic Fastrak system (Polhemus Society providing 3D position of anatomical landmarks to allow measurement of acetabular and pelvic parameters. Acetabulum and pelvis data were correlated by a Spearman matrix. A robust linear regression analysis provided prediction of acetabulum axes. The orientation of each acetabulum could be predicted by the incidence. The incidence is correlated with the morphology of acetabula. The asymmetry of the acetabular roof was correlated with pelvic incidence. This study allowed analysis of relationships of acetabular orientation and pelvic incidence. Pelvic incidence (morphologic parameter could determine the safe range of pelvis tilt (positional parameter for an individual and not a group.

  19. Novel equations to predict body fat percentage of Brazilian professional soccer players: A case study

    Directory of Open Access Journals (Sweden)

    Luiz Fernando Novack

    2014-12-01

    Full Text Available This study analyzed classical and developed novel mathematical models to predict body fat percentage (%BF in professional soccer players from the South Brazilian region using skinfold thicknesses measurement. Skinfolds of thirty one male professional soccer players (age of 21.48 ± 3.38 years, body mass of 79.05 ± 9.48 kg and height of 181.97 ± 8.11 cm were introduced into eight mathematical models from the literature for the prediction of %BF; these results were then compared to Dual-energy X-ray Absorptiometry (DXA. The classical equations were able to account from 65% to 79% of the variation of %BF in DXA. Statistical differences between most of the classical equations (seven of the eight classic equations and DXA were found, rendering their widespread use in this population useless. We developed three new equations for prediction of %BF with skinfolds from: axils, abdomen, thighs and calves. Theses equations accounted for 86.5% of the variation in %BF obtained with DXA.

  20. A study on improvement of analytical prediction model for spacer grid pressure loss coefficients

    International Nuclear Information System (INIS)

    Lim, Jonh Seon

    2002-02-01

    Nuclear fuel assemblies used in the nuclear power plants consist of the nuclear fuel rods, the control rod guide tubes, an instrument guide tube, spacer grids,a bottom nozzle, a top nozzle. The spacer grid is the most important component of the fuel assembly components for thermal hydraulic and mechanical design and analyses. The spacer grids fixed with the guide tubes support the fuel rods and have the very important role to activate thermal energy transfer by the coolant mixing caused to the turbulent flow and crossflow in the subchannels. In this paper, the analytical spacer grid pressure loss prediction model has been studied and improved by considering the test section wall to spacer grid gap pressure loss independently and applying the appropriate friction drag coefficient to predict pressure loss more accurately at the low Reynolds number region. The improved analytical model has been verified based on the hydraulic pressure drop test results for the spacer grids of three types with 5x5, 16x16, 17x17 arrays, respectively. The pressure loss coefficients predicted by the improved analytical model are coincident with those test results within ±12%. This result shows that the improved analytical model can be used for research and design change of the nuclear fuel assembly

  1. Resting lateralized activity predicts the cortical response and appraisal of emotions: an fNIRS study.

    Science.gov (United States)

    Balconi, Michela; Grippa, Elisabetta; Vanutelli, Maria Elide

    2015-12-01

    This study explored the effect of lateralized left-right resting brain activity on prefrontal cortical responsiveness to emotional cues and on the explicit appraisal (stimulus evaluation) of emotions based on their valence. Indeed subjective responses to different emotional stimuli should be predicted by brain resting activity and should be lateralized and valence-related (positive vs negative valence). A hemodynamic measure was considered (functional near-infrared spectroscopy). Indeed hemodynamic resting activity and brain response to emotional cues were registered when subjects (N = 19) viewed emotional positive vs negative stimuli (IAPS). Lateralized index response during resting state, LI (lateralized index) during emotional processing and self-assessment manikin rating were considered. Regression analysis showed the significant predictive effect of resting activity (more left or right lateralized) on both brain response and appraisal of emotional cues based on stimuli valence. Moreover, significant effects were found as a function of valence (more right response to negative stimuli; more left response to positive stimuli) during emotion processing. Therefore, resting state may be considered a predictive marker of the successive cortical responsiveness to emotions. The significance of resting condition for emotional behavior was discussed. © The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  2. Revisiting EOR Projects in Indonesia through Integrated Study: EOR Screening, Predictive Model, and Optimisation

    KAUST Repository

    Hartono, A. D.

    2017-10-17

    EOR preliminary analysis is pivotal to be performed at early stage of assessment in order to elucidate EOR feasibility. This study proposes an in-depth analysis toolkit for EOR preliminary evaluation. The toolkit incorporates EOR screening, predictive, economic, risk analysis and optimisation modules. The screening module introduces algorithms which assimilates statistical and engineering notions into consideration. The United States Department of Energy (U.S. DOE) predictive models were implemented in the predictive module. The economic module is available to assess project attractiveness, while Monte Carlo Simulation is applied to quantify risk and uncertainty of the evaluated project. Optimization scenario of EOR practice can be evaluated using the optimisation module, in which stochastic methods of Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Evolutionary Strategy (ES) were applied in the algorithms. The modules were combined into an integrated package of EOR preliminary assessment. Finally, we utilised the toolkit to evaluate several Indonesian oil fields for EOR evaluation (past projects) and feasibility (future projects). The attempt was able to update the previous consideration regarding EOR attractiveness and open new opportunity for EOR implementation in Indonesia.

  3. Acceptability of the Predicting Abusive Head Trauma (PredAHT) clinical prediction tool: A qualitative study with child protection professionals.

    Science.gov (United States)

    Cowley, Laura E; Maguire, Sabine; Farewell, Daniel M; Quinn-Scoggins, Harriet D; Flynn, Matthew O; Kemp, Alison M

    2018-05-09

    The validated Predicting Abusive Head Trauma (PredAHT) tool estimates the probability of abusive head trauma (AHT) based on combinations of six clinical features: head/neck bruising; apnea; seizures; rib/long-bone fractures; retinal hemorrhages. We aimed to determine the acceptability of PredAHT to child protection professionals. We conducted qualitative semi-structured interviews with 56 participants: clinicians (25), child protection social workers (10), legal practitioners (9, including 4 judges), police officers (8), and pathologists (4), purposively sampled across southwest United Kingdom. Interviews were recorded, transcribed and imported into NVivo for thematic analysis (38% double-coded). We explored participants' evaluations of PredAHT, their opinions about the optimal way to present the calculated probabilities, and their interpretation of probabilities in the context of suspected AHT. Clinicians, child protection social workers and police thought PredAHT would be beneficial as an objective adjunct to their professional judgment, to give them greater confidence in their decisions. Lawyers and pathologists appreciated its value for prompting multidisciplinary investigations, but were uncertain of its usefulness in court. Perceived disadvantages included: possible over-reliance and false reassurance from a low score. Interpretations regarding which percentages equate to 'low', 'medium' or 'high' likelihood of AHT varied; participants preferred a precise % probability over these general terms. Participants would use PredAHT with provisos: if they received multi-agency training to define accepted risk thresholds for consistent interpretation; with knowledge of its development; if it was accepted by colleagues. PredAHT may therefore increase professionals' confidence in their decision-making when investigating suspected AHT, but may be of less value in court. Copyright © 2018 Elsevier Ltd. All rights reserved.

  4. Predicting groundwater recharge for varying land cover and climate conditions - a global meta-study

    Science.gov (United States)

    Mohan, Chinchu; Western, Andrew W.; Wei, Yongping; Saft, Margarita

    2018-05-01

    Groundwater recharge is one of the important factors determining the groundwater development potential of an area. Even though recharge plays a key role in controlling groundwater system dynamics, much uncertainty remains regarding the relationships between groundwater recharge and its governing factors at a large scale. Therefore, this study aims to identify the most influential factors of groundwater recharge, and to develop an empirical model to estimate diffuse rainfall recharge at a global scale. Recharge estimates reported in the literature from various parts of the world (715 sites) were compiled and used in model building and testing exercises. Unlike conventional recharge estimates from water balance, this study used a multimodel inference approach and information theory to explain the relationship between groundwater recharge and influential factors, and to predict groundwater recharge at 0.5° resolution. The results show that meteorological factors (precipitation and potential evapotranspiration) and vegetation factors (land use and land cover) had the most predictive power for recharge. According to the model, long-term global average annual recharge (1981-2014) was 134 mm yr-1 with a prediction error ranging from -8 to 10 mm yr-1 for 97.2 % of cases. The recharge estimates presented in this study are unique and more reliable than the existing global groundwater recharge estimates because of the extensive validation carried out using both independent local estimates collated from the literature and national statistics from the Food and Agriculture Organization (FAO). In a water-scarce future driven by increased anthropogenic development, the results from this study will aid in making informed decisions about groundwater potential at a large scale.

  5. Prediction of Parkinson's disease subsequent to severe depression: a ten-year follow-up study.

    Science.gov (United States)

    Walter, Uwe; Heilmann, Robert; Kaulitz, Lara; Just, Tino; Krause, Bernd Joachim; Benecke, Reiner; Höppner, Jacqueline

    2015-06-01

    Major depressive disorder (MDD) has been associated with an increased risk of subsequent Parkinson's disease (PD) in case-control and cohort studies. However, depression alone is unlikely to be a useful marker of prodromal PD due to its low specificity. In this longitudinal observational study, we assessed whether the presence of other potential markers of prodromal PD predicts the subsequent development of PD in MDD patients. Of 57 patients with severe MDD but no diagnosis of PD who underwent a structured interview, olfactory and motor investigation and transcranial sonography at baseline, 46 (36 women; mean age 54.9 ± 11.7 years) could be followed for up to 11 (median, 10) years. Three patients (2 women; age 64, 65 and 70 years) developed definite PD after 1, 7, and 9 years, respectively. The combined finding of mild asymmetric motor slowing, idiopathic hyposmia, and substantia nigra hyperechogenicity predicted subsequent PD in all patients who could be followed for longer than 1 year. Out of the whole study cohort, only the subjects with subsequent PD presented with the triad of asymmetric motor slowing, idiopathic hyposmia, and substantia nigra hyperechogenicity in combination with at least two out of four reportable risk factors (family history of PD, current non-smoker, non-coffee drinker, constipation) at baseline investigation. Post-hoc analysis revealed that additional rating of eye and eye-lid motor abnormalities might further improve the prediction of PD in larger cohorts. Findings of this pilot-study suggest that MDD patients at risk of subsequent PD can be identified using an inexpensive non-invasive diagnostic battery.

  6. Resting Heart Rate Predicts Depression and Cognition Early after Ischemic Stroke: A Pilot Study.

    Science.gov (United States)

    Tessier, Arnaud; Sibon, Igor; Poli, Mathilde; Audiffren, Michel; Allard, Michèle; Pfeuty, Micha

    2017-10-01

    Early detection of poststroke depression (PSD) and cognitive impairment (PSCI) remains challenging. It is well documented that the function of autonomic nervous system is associated with depression and cognition. However, their relationship has never been investigated in the early poststroke phase. This pilot study aimed at determining whether resting heart rate (HR) parameters measured in early poststroke phase (1) are associated with early-phase measures of depression and cognition and (2) could be used as new tools for early objective prediction of PSD or PSCI, which could be applicable to patients unable to answer usual questionnaires. Fifty-four patients with first-ever ischemic stroke, without cardiac arrhythmia, were assessed for resting HR and heart rate variability (HRV) within the first week after stroke and for depression and cognition during the first week and at 3 months after stroke. Multiple regression analyses controlled for age, gender, and stroke severity revealed that higher HR, lower HRV, and higher sympathovagal balance (low-frequency/high-frequency ratio of HRV) were associated with higher severity of depressive symptoms within the first week after stroke. Furthermore, higher sympathovagal balance in early phase predicted higher severity of depressive symptoms at the 3-month follow-up, whereas higher HR and lower HRV in early phase predicted lower global cognitive functioning at the 3-month follow-up. Resting HR measurements obtained in early poststroke phase could serve as an objective tool, applicable to patients unable to complete questionnaires, to help in the early prediction of PSD and PSCI. Copyright © 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved.

  7. A study of modelling simplifications in ground vibration predictions for railway traffic at grade

    Science.gov (United States)

    Germonpré, M.; Degrande, G.; Lombaert, G.

    2017-10-01

    Accurate computational models are required to predict ground-borne vibration due to railway traffic. Such models generally require a substantial computational effort. Therefore, much research has focused on developing computationally efficient methods, by either exploiting the regularity of the problem geometry in the direction along the track or assuming a simplified track structure. This paper investigates the modelling errors caused by commonly made simplifications of the track geometry. A case study is presented investigating a ballasted track in an excavation. The soil underneath the ballast is stiffened by a lime treatment. First, periodic track models with different cross sections are analyzed, revealing that a prediction of the rail receptance only requires an accurate representation of the soil layering directly underneath the ballast. A much more detailed representation of the cross sectional geometry is required, however, to calculate vibration transfer from track to free field. Second, simplifications in the longitudinal track direction are investigated by comparing 2.5D and periodic track models. This comparison shows that the 2.5D model slightly overestimates the track stiffness, while the transfer functions between track and free field are well predicted. Using a 2.5D model to predict the response during a train passage leads to an overestimation of both train-track interaction forces and free field vibrations. A combined periodic/2.5D approach is therefore proposed in this paper. First, the dynamic axle loads are computed by solving the train-track interaction problem with a periodic model. Next, the vibration transfer to the free field is computed with a 2.5D model. This combined periodic/2.5D approach only introduces small modelling errors compared to an approach in which a periodic model is used in both steps, while significantly reducing the computational cost.

  8. A study on improvement of scaling factor prediction using artificial neural network

    International Nuclear Information System (INIS)

    Lee, Sang Chul; Hwang, Ki Ha; Kang, Sang Hee; Lee, Kun Jai

    2003-01-01

    Final disposal of radioactive waste generated from Nuclear Power Plant (NPP) requires the detailed knowledge of the natures and quantities of radionuclides in waste package. Many of these radionuclides are difficult to measure and expensive to assay. Thus it is suggested to the indirect method by which the concentrations of DTM (Difficult-to Measure) nuclide is decided using the relation of concentrations (Scaling Factor) between Key (Easy-to-Measure) nuclide and DTM nuclide with measured concentrations of Key nuclide. In general, scaling factor is determined by using of log mean average (LMA) and regression. These methods are adequate to apply most corrosion product nuclides. But in case of fission product nuclides and some corrosion product nuclides, the predicted values aren't well matched with the original values. In this study, the models using artificial neural network (ANN) for C-14 and Sr-90 are compared with those using LMA and regression. The assessment of models is executed in the two parts divided by a training part and a validation part. For all of two nuclides in the training part, the predicted values using ANN are well matched with the measured values compared with those using LMA and regression. In the validation part, the accuracy of the predicted values using ANN is better than that using LMA and is similar to or better than that using regression. It is concluded that the predicted values using ANN model are better than those using conventional model in some nuclides and ANN model can be used as the complement of LMA and regression model

  9. Short communication: Genetic study of methane production predicted from milk fat composition in dairy cows.

    Science.gov (United States)

    van Engelen, S; Bovenhuis, H; Dijkstra, J; van Arendonk, J A M; Visker, M H P W

    2015-11-01

    Dairy cows produce enteric methane, a greenhouse gas with 25 times the global warming potential of CO2. Breeding could make a permanent, cumulative, and long-term contribution to methane reduction. Due to a lack of accurate, repeatable, individual methane measurements needed for breeding, indicators of methane production based on milk fatty acids have been proposed. The aim of the present study was to quantify the genetic variation for predicted methane yields. The milk fat composition of 1,905 first-lactation Dutch Holstein-Friesian cows was used to investigate 3 different predicted methane yields (g/kg of DMI): Methane1, Methane2, and Methane3. Methane1 was based on the milk fat proportions of C17:0anteiso, C18:1 rans-10+11, C18:1 cis-11, and C18:1 cis-13 (R(2)=0.73). Methane2 was based on C4:0, C18:0, C18:1 trans-10+11, and C18:1 cis-11 (R(2)=0.70). Methane3 was based on C4:0, C6:0, and C18:1 trans-10+11 (R(2)=0.63). Predicted methane yields were demonstrated to be heritable traits, with heritabilities between 0.12 and 0.44. Breeding can, thus, be used to decrease methane production predicted based on milk fatty acids. Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

  10. Observational study to calculate addictive risk to opioids: a validation study of a predictive algorithm to evaluate opioid use disorder

    Directory of Open Access Journals (Sweden)

    Brenton A

    2017-05-01

    Full Text Available Ashley Brenton,1 Steven Richeimer,2,3 Maneesh Sharma,4 Chee Lee,1 Svetlana Kantorovich,1 John Blanchard,1 Brian Meshkin1 1Proove Biosciences, Irvine, CA, 2Keck school of Medicine, University of Southern California, Los Angeles, CA, 3Departments of Anesthesiology and Psychiatry, University of Southern California, Los Angeles, CA, 4Interventional Pain Institute, Baltimore, MD, USA Background: Opioid abuse in chronic pain patients is a major public health issue, with rapidly increasing addiction rates and deaths from unintentional overdose more than quadrupling since 1999. Purpose: This study seeks to determine the predictability of aberrant behavior to opioids using a comprehensive scoring algorithm incorporating phenotypic risk factors and neuroscience-associated single-nucleotide polymorphisms (SNPs. Patients and methods: The Proove Opioid Risk (POR algorithm determines the predictability of aberrant behavior to opioids using a comprehensive scoring algorithm incorporating phenotypic risk factors and neuroscience-associated SNPs. In a validation study with 258 subjects with diagnosed opioid use disorder (OUD and 650 controls who reported using opioids, the POR successfully categorized patients at high and moderate risks of opioid misuse or abuse with 95.7% sensitivity. Regardless of changes in the prevalence of opioid misuse or abuse, the sensitivity of POR remained >95%. Conclusion: The POR correctly stratifies patients into low-, moderate-, and high-risk categories to appropriately identify patients at need for additional guidance, monitoring, or treatment changes. Keywords: opioid use disorder, addiction, personalized medicine, pharmacogenetics, genetic testing, predictive algorithm

  11. Predictive modeling of human perception subjectivity: feasibility study of mammographic lesion similarity

    Science.gov (United States)

    Xu, Songhua; Hudson, Kathleen; Bradley, Yong; Daley, Brian J.; Frederick-Dyer, Katherine; Tourassi, Georgia

    2012-02-01

    The majority of clinical content-based image retrieval (CBIR) studies disregard human perception subjectivity, aiming to duplicate the consensus expert assessment of the visual similarity on example cases. The purpose of our study is twofold: i) discern better the extent of human perception subjectivity when assessing the visual similarity of two images with similar semantic content, and (ii) explore the feasibility of personalized predictive modeling of visual similarity. We conducted a human observer study in which five observers of various expertise were shown ninety-nine triplets of mammographic masses with similar BI-RADS descriptors and were asked to select the two masses with the highest visual relevance. Pairwise agreement ranged between poor and fair among the five observers, as assessed by the kappa statistic. The observers' self-consistency rate was remarkably low, based on repeated questions where either the orientation or the presentation order of a mass was changed. Various machine learning algorithms were explored to determine whether they can predict each observer's personalized selection using textural features. Many algorithms performed with accuracy that exceeded each observer's self-consistency rate, as determined using a cross-validation scheme. This accuracy was statistically significantly higher than would be expected by chance alone (two-tailed p-value ranged between 0.001 and 0.01 for all five personalized models). The study confirmed that human perception subjectivity should be taken into account when developing CBIR-based medical applications.

  12. Predicting drivers and distributions of deep-sea ecosystems: A cold-water coral case study

    DEFF Research Database (Denmark)

    Mohn, Christian; Rengstorf, Anna; Brown, Colin

    2015-01-01

    pertusa as a case study (Rengstorf et al., 2014). The study shows that predictive models incorporating hydrodynamic variables perform significantly better than models based on terrain parameters only. They are a potentially powerful tool to improve our understanding of deep-sea ecosystem functioning......, facilitating species distribution modelling with high spatial detail. In this study, we used high resolution data (250 m grid size) from a newly developed hydrodynamic model to explore linkages between key physical drivers and occurrences of the cold-water coral Lophelia pertusa in selected areas of the NE...... and to provide decision support for marine spatial planning and conservation in the deep sea. Mohn et al., 2014.Linking benthic hydrodynamics and cold water coral occurrences: A high-resolution model study at three cold-water coral provinces in the NE Atlantic. Progress in Oceanography 122, 92-104. Rengstorf et...

  13. Prediction of response to neoadjuvant chemotherapy in breast cancer: a radiomic study

    Science.gov (United States)

    Wu, Guolin; Fan, Ming; Zhang, Juan; Zheng, Bin; Li, Lihua

    2017-03-01

    Breast cancer is one of the most malignancies among women in worldwide. Neoadjuvant Chemotherapy (NACT) has gained interest and is increasingly used in treatment of breast cancer in recent years. Therefore, it is necessary to find a reliable non-invasive assessment and prediction method which can evaluate and predict the response of NACT. Recent studies have highlighted the use of MRI for predicting response to NACT. In addition, molecular subtype could also effectively identify patients who are likely have better prognosis in breast cancer. In this study, a radiomic analysis were performed, by extracting features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and immunohistochemistry (IHC) to determine subtypes. A dataset with fifty-seven breast cancer patients were included, all of them received preoperative MRI examination. Among them, 47 patients had complete response (CR) or partial response (PR) and 10 had stable disease (SD) to chemotherapy based on the RECIST criterion. A total of 216 imaging features including statistical characteristics, morphology, texture and dynamic enhancement were extracted from DCE-MRI. In multivariate analysis, the proposed imaging predictors achieved an AUC of 0.923 (P = 0.0002) in leave-one-out crossvalidation. The performance of the classifier increased to 0.960, 0.950 and 0.936 when status of HER2, Luminal A and Luminal B subtypes were added into the statistic model, respectively. The results of this study demonstrated that IHC determined molecular status combined with radiomic features from DCE-MRI could be used as clinical marker that is associated with response to NACT.

  14. The predictive role of support in the birth experience: A longitudinal cohort study.

    Science.gov (United States)

    Sigurdardottir, Valgerdur Lisa; Gamble, Jennifer; Gudmundsdottir, Berglind; Kristjansdottir, Hildur; Sveinsdottir, Herdis; Gottfredsdottir, Helga

    2017-12-01

    Several risk factors for negative birth experience have been identified, but little is known regarding the influence of social and midwifery support on the birth experience over time. The aim of this study was to describe women's birth experience up to two years after birth and to detect the predictive role of satisfaction with social and midwifery support in the birth experience. A longitudinal cohort study was conducted with a convenience sample of pregnant women from 26 community health care centres. Data was gathered using questionnaires at 11-16 weeks of pregnancy (T1, n=1111), at five to six months (T2, n=765), and at 18-24 months after birth (T3, n=657). Data about sociodemographic factors, reproductive history, birth outcomes, social and midwifery support, depressive symptoms, and birth experience were collected. The predictive role of midwifery support in the birth experience was examined using binary logistic regression. The prevalence of negative birth experience was 5% at T2 and 5.7% at T3. Women who were not satisfied with midwifery support during pregnancy and birth were more likely to have negative birth experience at T2 than women who were satisfied with midwifery support. Operative birth, perception of prolonged birth and being a student predicted negative birth experience at both T2 and T3. Perception of negative birth experience was relatively consistent during the study period and the role of support from midwives during pregnancy and birth had a significant impact on women's perception of birth experience. Copyright © 2017 Australian College of Midwives. Published by Elsevier Ltd. All rights reserved.

  15. Appropriateness guidelines and predictive rules to select patients for upper endoscopy: a nationwide multicenter study.

    Science.gov (United States)

    Buri, Luigi; Hassan, Cesare; Bersani, Gianluca; Anti, Marcello; Bianco, Maria Antonietta; Cipolletta, Livio; Di Giulio, Emilio; Di Matteo, Giovanni; Familiari, Luigi; Ficano, Leonardo; Loriga, Pietro; Morini, Sergio; Pietropaolo, Vincenzo; Zambelli, Alessandro; Grossi, Enzo; Intraligi, Marco; Buscema, Massimo

    2010-06-01

    Selecting patients appropriately for upper endoscopy (EGD) is crucial for efficient use of endoscopy. The objective of this study was to compare different clinical strategies and statistical methods to select patients for EGD, namely appropriateness guidelines, age and/or alarm features, and multivariate and artificial neural network (ANN) models. A nationwide, multicenter, prospective study was undertaken in which consecutive patients referred for EGD during a 1-month period were enrolled. Before EGD, the endoscopist assessed referral appropriateness according to the American Society for Gastrointestinal Endoscopy (ASGE) guidelines, also collecting clinical and demographic variables. Outcomes of the study were detection of relevant findings and new diagnosis of malignancy at EGD. The accuracy of the following clinical strategies and predictive rules was compared: (i) ASGE appropriateness guidelines (indicated vs. not indicated), (ii) simplified rule (>or=45 years or alarm features vs. <45 years without alarm features), (iii) logistic regression model, and (iv) ANN models. A total of 8,252 patients were enrolled in 57 centers. Overall, 3,803 (46%) relevant findings and 132 (1.6%) new malignancies were detected. Sensitivity, specificity, and area under the receiver-operating characteristic curve (AUC) of the simplified rule were similar to that of the ASGE guidelines for both relevant findings (82%/26%/0.55 vs. 88%/27%/0.52) and cancer (97%/22%/0.58 vs. 98%/20%/0.58). Both logistic regression and ANN models seemed to be substantially more accurate in predicting new cases of malignancy, with an AUC of 0.82 and 0.87, respectively. A simple predictive rule based on age and alarm features is similarly effective to the more complex ASGE guidelines in selecting patients for EGD. Regression and ANN models may be useful in identifying a relatively small subgroup of patients at higher risk of cancer.

  16. An approach to model validation and model-based prediction -- polyurethane foam case study.

    Energy Technology Data Exchange (ETDEWEB)

    Dowding, Kevin J.; Rutherford, Brian Milne

    2003-07-01

    Enhanced software methodology and improved computing hardware have advanced the state of simulation technology to a point where large physics-based codes can be a major contributor in many systems analyses. This shift toward the use of computational methods has brought with it new research challenges in a number of areas including characterization of uncertainty, model validation, and the analysis of computer output. It is these challenges that have motivated the work described in this report. Approaches to and methods for model validation and (model-based) prediction have been developed recently in the engineering, mathematics and statistical literatures. In this report we have provided a fairly detailed account of one approach to model validation and prediction applied to an analysis investigating thermal decomposition of polyurethane foam. A model simulates the evolution of the foam in a high temperature environment as it transforms from a solid to a gas phase. The available modeling and experimental results serve as data for a case study focusing our model validation and prediction developmental efforts on this specific thermal application. We discuss several elements of the ''philosophy'' behind the validation and prediction approach: (1) We view the validation process as an activity applying to the use of a specific computational model for a specific application. We do acknowledge, however, that an important part of the overall development of a computational simulation initiative is the feedback provided to model developers and analysts associated with the application. (2) We utilize information obtained for the calibration of model parameters to estimate the parameters and quantify uncertainty in the estimates. We rely, however, on validation data (or data from similar analyses) to measure the variability that contributes to the uncertainty in predictions for specific systems or units (unit-to-unit variability). (3) We perform statistical

  17. Testing by photometric measurement and camera study of theoretical prediction of microvolume universal sessile dropshape

    International Nuclear Information System (INIS)

    Smith, S R P; O'Neill, M; McMillan, N D; Arthure, K; Smith, S; Riedel, S

    2011-01-01

    The approach to the theory of sessile dropshapes held on a cylindrical drophead is discussed. It reveals an 'undifferentiable' universal micro-dropshape for volumes below 3μL. Camera studies demonstrate the veracity of this prediction exploited in the design of a new microvolume spectrometer. The mean pathlength of light injected through a microvolume sessile drop has been determined both from the model and from experiment. Drop volumes determine accurately the mean pathlength and with this Beer's law relationship is experimentally confirmed. The Transmitted Light Drop Analyser uses this universal 'natural cuvette' to deliver both high-performance UV spectra and absorbance measurements at discrete wavelengths.

  18. Study and prediction model on low temperature aging embrittlement in duplex stainless steels

    International Nuclear Information System (INIS)

    Sanchez, L.; Gutierrez-Solana, F.

    1997-01-01

    Within the framework of a general study on low temperature (280-400 degree centigree) aging embrittlement in duplex stainless steels, a relationship has been obtained between aging, measured from ferrite hardness evolution, and bulk materials embrittlement, determined from fracture toughness and fracture impact tests. The existing correlation between the increase in ferrite hardness and its percentage presence in the fracture path supports this relationship and results in the development of a prediction design model which provides the fracture resistance curves, for any aging level, based on the chemical composition and the steel's properties in an unaged state. (Author)

  19. Comparative Study of Different Methods for the Prediction of Drug-Polymer Solubility

    DEFF Research Database (Denmark)

    Knopp, Matthias Manne; Tajber, Lidia; Tian, Yiwei

    2015-01-01

    monomer weight ratios. The drug–polymer solubility at 25 °C was predicted using the Flory–Huggins model, from data obtained at elevated temperature using thermal analysis methods based on the recrystallization of a supersaturated amorphous solid dispersion and two variations of the melting point......, which suggests that this method can be used as an initial screening tool if a liquid analogue is available. The learnings of this important comparative study provided general guidance for the selection of the most suitable method(s) for the screening of drug–polymer solubility....

  20. Fluid prediction using rock modelling and reconnaissance. AVO analysis - A case study from the North Sea

    Energy Technology Data Exchange (ETDEWEB)

    Osdal, Bard; Granli, John Reidar

    1998-12-31

    Seismic lithology and fluid phase prediction (LFP) is becoming an important part of seismic interpretation, and can contribute significantly to risk reduction prior to drilling. In this presentation there is focused on quantitative interpretation of the amplitudes in a 2-D dataset, with respect to presence of hydrocarbons. Different aspect of the working producer, like data quality (well data and seismic data), rock modelling and seismic modelling will be illustrated. In the present study only one well has been used for calibration and to investigate the seismic response for different fluid and lithology scenarios. The rock modelling included evaluation of seismic parameter effect for different fluid and porosities. 1 ref., 4 figs.

  1. A comparative study of various inflow boundary conditions and turbulence models for wind turbine wake predictions

    Science.gov (United States)

    Tian, Lin-Lin; Zhao, Ning; Song, Yi-Lei; Zhu, Chun-Ling

    2018-05-01

    This work is devoted to perform systematic sensitivity analysis of different turbulence models and various inflow boundary conditions in predicting the wake flow behind a horizontal axis wind turbine represented by an actuator disc (AD). The tested turbulence models are the standard k-𝜀 model and the Reynolds Stress Model (RSM). A single wind turbine immersed in both uniform flows and in modeled atmospheric boundary layer (ABL) flows is studied. Simulation results are validated against the field experimental data in terms of wake velocity and turbulence intensity.

  2. The predictive value of microalbuminuria in IDDM. A five-year follow-up study

    DEFF Research Database (Denmark)

    Almdal, T; Nörgaard, K; Feldt-Rasmussen, B

    1994-01-01

    OBJECTIVE: To investigate the predictive value of microalbuminuria and the annual increase of albumin excretion as risk factors for diabetic nephropathy. RESEARCH DESIGN AND METHODS: A 5-year follow-up of patients with microalbuminuria (urinary albumin excretion [UAE] = 30-299 mg/24 h) and matched...... patients with normoalbuminuria (UAE classification was based on one single 24-h urine collection. The annual increase in UAE was calculated by linear regression analysis of log-transformed UAE on time. This study was conducted at the outpatient clinic of the Steno Diabetes Center...

  3. Sex-Specific Prediction Models for Sleep Apnea From the Hispanic Community Health Study/Study of Latinos.

    Science.gov (United States)

    Shah, Neomi; Hanna, David B; Teng, Yanping; Sotres-Alvarez, Daniela; Hall, Martica; Loredo, Jose S; Zee, Phyllis; Kim, Mimi; Yaggi, H Klar; Redline, Susan; Kaplan, Robert C

    2016-06-01

    We developed and validated the first-ever sleep apnea (SA) risk calculator in a large population-based cohort of Hispanic/Latino subjects. Cross-sectional data on adults from the Hispanic Community Health Study/Study of Latinos (2008-2011) were analyzed. Subjective and objective sleep measurements were obtained. Clinically significant SA was defined as an apnea-hypopnea index ≥ 15 events per hour. Using logistic regression, four prediction models were created: three sex-specific models (female-only, male-only, and a sex × covariate interaction model to allow differential predictor effects), and one overall model with sex included as a main effect only. Models underwent 10-fold cross-validation and were assessed by using the C statistic. SA and its predictive variables; a total of 17 variables were considered. A total of 12,158 participants had complete sleep data available; 7,363 (61%) were women. The population-weighted prevalence of SA (apnea-hypopnea index ≥ 15 events per hour) was 6.1% in female subjects and 13.5% in male subjects. Male-only (C statistic, 0.808) and female-only (C statistic, 0.836) prediction models had the same predictor variables (ie, age, BMI, self-reported snoring). The sex-interaction model (C statistic, 0.836) contained sex, age, age × sex, BMI, BMI × sex, and self-reported snoring. The final overall model (C statistic, 0.832) contained age, BMI, snoring, and sex. We developed two websites for our SA risk calculator: one in English (https://www.montefiore.org/sleepapneariskcalc.html) and another in Spanish (http://www.montefiore.org/sleepapneariskcalc-es.html). We created an internally validated, highly discriminating, well-calibrated, and parsimonious prediction model for SA. Contrary to the study hypothesis, the variables did not have different predictive magnitudes in male and female subjects. Copyright © 2016 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

  4. The Cognitive Processes underlying Affective Decision-making Predicting Adolescent Smoking Behaviors in a Longitudinal Study

    Directory of Open Access Journals (Sweden)

    Lin eXiao

    2013-10-01

    Full Text Available This study investigates the relationship between three different cognitive processes underlying the Iowa Gambling Task (IGT and adolescent smoking behaviors in a longitudinal study. We conducted a longitudinal study of 181 Chinese adolescents in Chengdu City, China. The participants were followed from 10th grade to 11th grade. When they were in the 10th grade (Time 1, we tested these adolescents’ decision-making using the Iowa Gambling Task and working memory capacity using the Self-ordered Pointing Test (SOPT. Self-report questionnaires were used to assess school academic performance and smoking behaviors. The same questionnaires were completed again at the one-year follow-up (Time 2. The Expectancy-Valence (EV Model was applied to distill the IGT performance into three different underlying psychological components: (i a motivational component which indicates the subjective weight the adolescents assign to gains versus losses; (ii a learning-rate component which indicates the sensitivity to recent outcomes versus past experiences; and (iii a response component which indicates how consistent the adolescents are between learning and responding. The subjective weight to gains vs. losses at Time 1 significantly predicted current smokers and current smoking levels at Time 2, controlling for demographic variables and baseline smoking behaviors. Therefore, by decomposing the IGT into three different psychological components, we found that the motivational process of weight gain vs. losses may serve as a neuropsychological marker to predict adolescent smoking behaviors in a general youth population.

  5. Performance of a process-based hydrodynamic model in predicting shoreline change

    Science.gov (United States)

    Safak, I.; Warner, J. C.; List, J. H.

    2012-12-01

    Shoreline change is controlled by a complex combination of processes that include waves, currents, sediment characteristics and availability, geologic framework, human interventions, and sea level rise. A comprehensive data set of shoreline position (14 shorelines between 1978-2002) along the continuous and relatively non-interrupted North Carolina Coast from Oregon Inlet to Cape Hatteras (65 km) reveals a spatial pattern of alternating erosion and accretion, with an erosional average shoreline change rate of -1.6 m/yr and up to -8 m/yr in some locations. This data set gives a unique opportunity to study long-term shoreline change in an area hit by frequent storm events while relatively uninfluenced by human interventions and the effects of tidal inlets. Accurate predictions of long-term shoreline change may require a model that accurately resolves surf zone processes and sediment transport patterns. Conventional methods for predicting shoreline change such as one-line models and regression of shoreline positions have been designed for computational efficiency. These methods, however, not only have several underlying restrictions (validity for small angle of wave approach, assuming bottom contours and shoreline to be parallel, depth of closure, etc.) but also their empirical estimates of sediment transport rates in the surf zone have been shown to vary greatly from the calculations of process-based hydrodynamic models. We focus on hind-casting long-term shoreline change using components of the process-based, three-dimensional coupled-ocean-atmosphere-wave-sediment transport modeling system (COAWST). COAWST is forced with historical predictions of atmospheric and oceanographic data from public-domain global models. Through a method of coupled concurrent grid-refinement approach in COAWST, the finest grid with resolution of O(10 m) that covers the surf zone along the section of interest is forced at its spatial boundaries with waves and currents computed on the grids

  6. The prediction of late-onset preeclampsia: Results from a longitudinal proteomics study.

    Science.gov (United States)

    Erez, Offer; Romero, Roberto; Maymon, Eli; Chaemsaithong, Piya; Done, Bogdan; Pacora, Percy; Panaitescu, Bogdan; Chaiworapongsa, Tinnakorn; Hassan, Sonia S; Tarca, Adi L

    2017-01-01

    Late-onset preeclampsia is the most prevalent phenotype of this syndrome; nevertheless, only a few biomarkers for its early diagnosis have been reported. We sought to correct this deficiency using a high through-put proteomic platform. A case-control longitudinal study was conducted, including 90 patients with normal pregnancies and 76 patients with late-onset preeclampsia (diagnosed at ≥34 weeks of gestation). Maternal plasma samples were collected throughout gestation (normal pregnancy: 2-6 samples per patient, median of 2; late-onset preeclampsia: 2-6, median of 5). The abundance of 1,125 proteins was measured using an aptamers-based proteomics technique. Protein abundance in normal pregnancies was modeled using linear mixed-effects models to estimate mean abundance as a function of gestational age. Data was then expressed as multiples of-the-mean (MoM) values in normal pregnancies. Multi-marker prediction models were built using data from one of five gestational age intervals (8-16, 16.1-22, 22.1-28, 28.1-32, 32.1-36 weeks of gestation). The predictive performance of the best combination of proteins was compared to placental growth factor (PIGF) using bootstrap. 1) At 8-16 weeks of gestation, the best prediction model included only one protein, matrix metalloproteinase 7 (MMP-7), that had a sensitivity of 69% at a false positive rate (FPR) of 20% (AUC = 0.76); 2) at 16.1-22 weeks of gestation, MMP-7 was the single best predictor of late-onset preeclampsia with a sensitivity of 70% at a FPR of 20% (AUC = 0.82); 3) after 22 weeks of gestation, PlGF was the best predictor of late-onset preeclampsia, identifying 1/3 to 1/2 of the patients destined to develop this syndrome (FPR = 20%); 4) 36 proteins were associated with late-onset preeclampsia in at least one interval of gestation (after adjustment for covariates); 5) several biological processes, such as positive regulation of vascular endothelial growth factor receptor signaling pathway, were perturbed; and 6

  7. The prediction of late-onset preeclampsia: Results from a longitudinal proteomics study

    Science.gov (United States)

    Erez, Offer; Romero, Roberto; Maymon, Eli; Chaemsaithong, Piya; Done, Bogdan; Pacora, Percy; Panaitescu, Bogdan; Chaiworapongsa, Tinnakorn; Hassan, Sonia S.

    2017-01-01

    Background Late-onset preeclampsia is the most prevalent phenotype of this syndrome; nevertheless, only a few biomarkers for its early diagnosis have been reported. We sought to correct this deficiency using a high through-put proteomic platform. Methods A case-control longitudinal study was conducted, including 90 patients with normal pregnancies and 76 patients with late-onset preeclampsia (diagnosed at ≥34 weeks of gestation). Maternal plasma samples were collected throughout gestation (normal pregnancy: 2–6 samples per patient, median of 2; late-onset preeclampsia: 2–6, median of 5). The abundance of 1,125 proteins was measured using an aptamers-based proteomics technique. Protein abundance in normal pregnancies was modeled using linear mixed-effects models to estimate mean abundance as a function of gestational age. Data was then expressed as multiples of-the-mean (MoM) values in normal pregnancies. Multi-marker prediction models were built using data from one of five gestational age intervals (8–16, 16.1–22, 22.1–28, 28.1–32, 32.1–36 weeks of gestation). The predictive performance of the best combination of proteins was compared to placental growth factor (PIGF) using bootstrap. Results 1) At 8–16 weeks of gestation, the best prediction model included only one protein, matrix metalloproteinase 7 (MMP-7), that had a sensitivity of 69% at a false positive rate (FPR) of 20% (AUC = 0.76); 2) at 16.1–22 weeks of gestation, MMP-7 was the single best predictor of late-onset preeclampsia with a sensitivity of 70% at a FPR of 20% (AUC = 0.82); 3) after 22 weeks of gestation, PlGF was the best predictor of late-onset preeclampsia, identifying 1/3 to 1/2 of the patients destined to develop this syndrome (FPR = 20%); 4) 36 proteins were associated with late-onset preeclampsia in at least one interval of gestation (after adjustment for covariates); 5) several biological processes, such as positive regulation of vascular endothelial growth factor

  8. Prediction by data mining, of suicide attempts in Korean adolescents: a national study

    Directory of Open Access Journals (Sweden)

    Bae SM

    2015-09-01

    Full Text Available Sung Man Bae,1 Seung A Lee,2 Seung-Hwan Lee2,3 1Department of Counseling Psychology, The Cyber University of Korea, Seoul, South Korea; 2Clinical Emotion and Cognition Research Laboratory, Goyang, South Korea; 3Department of Psychiatry, Ilsan Paik Hospital, Inje University College of Medicine, Goyang, South Korea Objective: This study aimed to develop a prediction model for suicide attempts in Korean adolescents.Methods: We conducted a decision tree analysis of 2,754 middle and high school students nationwide. We fixed suicide attempt as the dependent variable and eleven sociodemographic, intrapersonal, and extrapersonal variables as independent variables.Results: The rate of suicide attempts of the total sample was 9.5%, and severity of depression was the strongest variable to predict suicide attempt. The rates of suicide attempts in the depression and potential depression groups were 5.4 and 2.8 times higher than that of the non-depression group. In the depression group, the most powerful factor to predict a suicide attempt was delinquency, and the rate of suicide attempts in those in the depression group with higher delinquency was two times higher than in those in the depression group with lower delinquency. Of special note, the rate of suicide attempts in the depressed females with higher delinquency was the highest. Interestingly, in the potential depression group, the most impactful factor to predict a suicide attempt was intimacy with family, and the rate of suicide attempts of those in the potential depression group with lower intimacy with family was 2.4 times higher than that of those in the potential depression group with higher intimacy with family. And, among the potential depression group, middle school students with lower intimacy with family had a 2.5-times higher rate of suicide attempts than high school students with lower intimacy with family. Finally, in the non-depression group, stress level was the most powerful factor to

  9. Metabolic activity in the insular cortex and hypothalamus predicts hot flashes: an FDG-PET study.

    Science.gov (United States)

    Joffe, Hadine; Deckersbach, Thilo; Lin, Nancy U; Makris, Nikos; Skaar, Todd C; Rauch, Scott L; Dougherty, Darin D; Hall, Janet E

    2012-09-01

    Hot flashes are a common side effect of adjuvant endocrine therapies (AET; leuprolide, tamoxifen, aromatase inhibitors) that reduce quality of life and treatment adherence in breast cancer patients. Because hot flashes affect only some women, preexisting neurobiological traits might predispose to their development. Previous studies have implicated the insula during the perception of hot flashes and the hypothalamus in thermoregulatory dysfunction. The aim of the study was to understand whether neurobiological factors predict hot flashes. [18F]-Fluorodeoxyglucose (FDG) positron emission tomography (PET) brain scans coregistered with structural magnetic resonance imaging were used to determine whether metabolic activity in the insula and hypothalamic thermoregulatory and estrogen-feedback regions measured before and in response to AET predict hot flashes. Findings were correlated with CYP2D6 genotype because of CYP2D6 polymorphism associations with tamoxifen-induced hot flashes. We measured regional cerebral metabolic rate of glucose uptake (rCMRglu) in the insula and hypothalamus on FDG-PET. Of 18 women without hot flashes who began AET, new-onset hot flashes were reported by 10 (55.6%) and were detected objectively in nine (50%) participants. Prior to the use of all AET, rCMRglu in the insula (P ≤ 0.01) and hypothalamic thermoregulatory (P = 0.045) and estrogen-feedback (P = 0.007) regions was lower in women who reported developing hot flashes. In response to AET, rCMRglu was further reduced in the insula in women developing hot flashes (P ≤ 0.02). Insular and hypothalamic rCMRglu levels were lower in intermediate than extensive CYP2D6 metabolizers. Trait neurobiological characteristics predict hot flashes. Genetic variability in CYP2D6 may underlie the neurobiological predisposition to hot flashes induced by AET.

  10. Clinical prediction in defined populations: a simulation study investigating when and how to aggregate existing models

    Directory of Open Access Journals (Sweden)

    Glen P. Martin

    2017-01-01

    Full Text Available Abstract Background Clinical prediction models (CPMs are increasingly deployed to support healthcare decisions but they are derived inconsistently, in part due to limited data. An emerging alternative is to aggregate existing CPMs developed for similar settings and outcomes. This simulation study aimed to investigate the impact of between-population-heterogeneity and sample size on aggregating existing CPMs in a defined population, compared with developing a model de novo. Methods Simulations were designed to mimic a scenario in which multiple CPMs for a binary outcome had been derived in distinct, heterogeneous populations, with potentially different predictors available in each. We then generated a new ‘local’ population and compared the performance of CPMs developed for this population by aggregation, using stacked regression, principal component analysis or partial least squares, with redevelopment from scratch using backwards selection and penalised regression. Results While redevelopment approaches resulted in models that were miscalibrated for local datasets of less than 500 observations, model aggregation methods were well calibrated across all simulation scenarios. When the size of local data was less than 1000 observations and between-population-heterogeneity was small, aggregating existing CPMs gave better discrimination and had the lowest mean square error in the predicted risks compared with deriving a new model. Conversely, given greater than 1000 observations and significant between-population-heterogeneity, then redevelopment outperformed the aggregation approaches. In all other scenarios, both aggregation and de novo derivation resulted in similar predictive performance. Conclusion This study demonstrates a pragmatic approach to contextualising CPMs to defined populations. When aiming to develop models in defined populations, modellers should consider existing CPMs, with aggregation approaches being a suitable modelling

  11. Preoperative predictive model for acute kidney injury after elective cardiac surgery: a prospective multicentre cohort study.

    Science.gov (United States)

    Callejas, Raquel; Panadero, Alfredo; Vives, Marc; Duque, Paula; Echarri, Gemma; Monedero, Pablo

    2018-05-11

    Predictive models of CS-AKI include emergency surgery and patients with haemodynamic instability. Our objective was to evaluate the performance of validated predictive models (Thakar and Demirjian) in elective cardiac surgery and to propose a better score in the case of poor performance. A prospective, multicentre, observational study was designed. Data were collected from 942 patients undergoing cardiac surgery, after excluding emergency surgery and patients with an intraaortic balloon pump. The main outcome measure was CS-AKI defined by the composite of requiring dialysis or doubling baseline creatinine values. Both models showed poor discrimination in elective surgery (Thakar's model, AUROC = 0.57, 95% CI = 0.50-0.64 and Demirjian's model, AUROC= 0.64, 95% CI = 0.58-0.71). We generated a new model whose significant independent predictors were: anaemia, age, hypertension, obesity, congestive heart failure, previous cardiac surgery and type of surgery. It classifies patients with scores 0-3 as low risk ( 8 as high risk (>30%) of developing CS-AKI with a statistically significant correlation (p <0.001). Our model reflects acceptable discriminatory ability (AUC = 0.72, 95% CI = 0.66-0.78) which is significantly better than Thakar and Demirjian's models (p<0.01). We developed a new simple predictive model of CS-AKI in elective surgery based on available preoperative information. Our new model is easy to calculate and can be an effective tool for communicating risk to patients and guiding decision-making in the perioperative period. The study requires external validation.

  12. Using iron studies to predict HFE mutations in New Zealand: implications for laboratory testing.

    Science.gov (United States)

    O'Toole, Rebecca; Romeril, Kenneth; Bromhead, Collette

    2017-04-01

    The diagnosis of hereditary haemochromatosis (HH) is not straightforward because symptoms are often absent or non-specific. Biochemical markers of iron-overloading may be affected by other conditions. To measure the correlation between iron studies and HFE genotype to inform evidence-based recommendations for laboratory testing in New Zealand. Results from 2388 patients genotyped for C282Y, H63D and S65C in Wellington, New Zealand from 2007 to 2013 were compared with their biochemical phenotype as quantified by serum ferritin (SF), transferrin saturation (TS), serum iron (SI) and serum transferrin (ST). The predictive power of these markers was evaluated by receiver operator characteristic (ROC) curve analysis, and if a statistically significant association for a variable was seen, sensitivity, specificity and predictive values were calculated. Test ordering patterns showed that 62% of HFE genotyping tests were ordered because of an elevated SF alone and only 11% of these had a C-reactive protein test to rule out an acute phase reaction. The association between SF and significant HFE genotypes SF was low. However, TS values ≥45% predicted HH mutations with the highest sensitivity and specificity. A SF of >1000 µg/L was found in one at-risk patient (C282Y homozygote) who had a TS <45%. Our analysis highlights the need for clear guidelines for investigation of hyperferritinaemia and HH in New Zealand. Using our findings, we developed an evidence-based laboratory testing algorithm based on a TS ≥45%, a SF ≥1000 µg/L and/or a family history of HH which identified all C282Y homozygotes in this study. © 2016 Royal Australasian College of Physicians.

  13. Symptom clusters predict mortality among dialysis patients in Norway: a prospective observational cohort study.

    Science.gov (United States)

    Amro, Amin; Waldum, Bård; von der Lippe, Nanna; Brekke, Fredrik Barth; Dammen, Toril; Miaskowski, Christine; Os, Ingrid

    2015-01-01

    Patients with end-stage renal disease on dialysis have reduced survival rates compared with the general population. Symptoms are frequent in dialysis patients, and a symptom cluster is defined as two or more related co-occurring symptoms. The aim of this study was to explore the associations between symptom clusters and mortality in dialysis patients. In a prospective observational cohort study of dialysis patients (n = 301), Kidney Disease and Quality of Life Short Form and Beck Depression Inventory questionnaires were administered. To generate symptom clusters, principal component analysis with varimax rotation was used on 11 kidney-specific self-reported physical symptoms. A Beck Depression Inventory score of 16 or greater was defined as clinically significant depressive symptoms. Physical and mental component summary scores were generated from Short Form-36. Multivariate Cox regression analysis was used for the survival analysis, Kaplan-Meier curves and log-rank statistics were applied to compare survival rates between the groups. Three different symptom clusters were identified; one included loading of several uremic symptoms. In multivariate analyses and after adjustment for health-related quality of life and depressive symptoms, the worst perceived quartile of the "uremic" symptom cluster independently predicted all-cause mortality (hazard ratio 2.47, 95% CI 1.44-4.22, P = 0.001) compared with the other quartiles during a follow-up period that ranged from four to 52 months. The two other symptom clusters ("neuromuscular" and "skin") or the individual symptoms did not predict mortality. Clustering of uremic symptoms predicted mortality. Assessing co-occurring symptoms rather than single symptoms may help to identify dialysis patients at high risk for mortality. Copyright © 2015 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

  14. Predictive error detection in pianists: A combined ERP and motion capture study

    Directory of Open Access Journals (Sweden)

    Clemens eMaidhof

    2013-09-01

    Full Text Available Performing a piece of music involves the interplay of several cognitive and motor processes and requires extensive training to achieve a high skill level. However, even professional musicians commit errors occasionally. Previous event-related potential (ERP studies have investigated the neurophysiological correlates of pitch errors during piano performance, and reported pre-error negativity already occurring approximately 70-100 ms before the error had been committed and audible. It was assumed that this pre-error negativity reflects predictive control processes that compare predicted consequences with actual consequences of one’s own actions. However, in previous investigations, correct and incorrect pitch events were confounded by their different tempi. In addition, no data about the underlying movements were available. In the present study, we exploratively recorded the ERPs and 3D movement data of pianists’ fingers simultaneously while they performed fingering exercises from memory. Results showed a pre-error negativity for incorrect keystrokes when both correct and incorrect keystrokes were performed with comparable tempi. Interestingly, even correct notes immediately preceding erroneous keystrokes elicited a very similar negativity. In addition, we explored the possibility of computing ERPs time-locked to a kinematic landmark in the finger motion trajectories defined by when a finger makes initial contact with the key surface, that is, at the onset of tactile feedback. Results suggest that incorrect notes elicited a small difference after the onset of tactile feedback, whereas correct notes preceding incorrect ones elicited negativity before the onset of tactile feedback. The results tentatively suggest that tactile feedback plays an important role in error-monitoring during piano performance, because the comparison between predicted and actual sensory (tactile feedback may provide the information necessary for the detection of an

  15. Fundamental studies of aluminum corrosion in acidic and basic environments: Theoretical predictions and experimental observations

    International Nuclear Information System (INIS)

    Lashgari, Mohsen; Malek, Ali M.

    2010-01-01

    Using quantum electrochemical approaches based on density functional theory and cluster/polarized continuum model, we investigated the corrosion behavior of aluminum in HCl and NaOH media containing phenol inhibitor. In this regard, we determined the geometry and electronic structure of the species at metal/solution interface. The investigations revealed that the interaction energies of hydroxide corrosive agents with aluminum surface should be more negative than those of chloride ones. The inhibitor adsorption in acid is more likely to have a physical nature while it appears as though to be chemical in basic media. To verify these predictions, using Tafel plots, we studied the phenomena from experimental viewpoint. The studies confirmed that the rate of corrosion in alkaline solution is substantially greater than in HCl media. Moreover, phenol is a potential-molecule having mixed-type inhibition mechanism. The relationship between inhibitory action and molecular parameters was discussed and the activity in alkaline media was also theoretically anticipated. This prediction was in accord with experiment.

  16. An appraisal of wind speed distribution prediction by soft computing methodologies: A comparative study

    International Nuclear Information System (INIS)

    Petković, Dalibor; Shamshirband, Shahaboddin; Anuar, Nor Badrul; Saboohi, Hadi; Abdul Wahab, Ainuddin Wahid; Protić, Milan; Zalnezhad, Erfan; Mirhashemi, Seyed Mohammad Amin

    2014-01-01

    Highlights: • Probabilistic distribution functions of wind speed. • Two parameter Weibull probability distribution. • To build an effective prediction model of distribution of wind speed. • Support vector regression application as probability function for wind speed. - Abstract: The probabilistic distribution of wind speed is among the more significant wind characteristics in examining wind energy potential and the performance of wind energy conversion systems. When the wind speed probability distribution is known, the wind energy distribution can be easily obtained. Therefore, the probability distribution of wind speed is a very important piece of information required in assessing wind energy potential. For this reason, a large number of studies have been established concerning the use of a variety of probability density functions to describe wind speed frequency distributions. Although the two-parameter Weibull distribution comprises a widely used and accepted method, solving the function is very challenging. In this study, the polynomial and radial basis functions (RBF) are applied as the kernel function of support vector regression (SVR) to estimate two parameters of the Weibull distribution function according to previously established analytical methods. Rather than minimizing the observed training error, SVR p oly and SVR r bf attempt to minimize the generalization error bound, so as to achieve generalized performance. According to the experimental results, enhanced predictive accuracy and capability of generalization can be achieved using the SVR approach compared to other soft computing methodologies

  17. Internalizing and externalizing traits predict changes in sleep efficiency in emerging adulthood: An actigraphy study

    Directory of Open Access Journals (Sweden)

    Ashley eYaugher

    2015-10-01

    Full Text Available Research on psychopathology and experimental studies of sleep restriction support a relationship between sleep disruption and both internalizing and externalizing disorders. The objective of the current study was to extend this research by examining sleep, impulsivity, antisocial personality traits, and internalizing traits in a university sample. Three hundred and eighty six individuals (161 males between the ages of 18 and 27 years (M = 18.59, SD = 0.98 wore actigraphs for 7 days and completed established measures of disorder-linked personality traits and sleep quality (i.e., Personality Assessment Inventory, Triarchic Psychopathy Measure, Barratt Impulsiveness Scale-11, and the Pittsburgh Sleep Quality Index. As expected, sleep measures and questionnaire scores fell within the normal range of values and sex differences in sleep and personality were consistent with previous research results. Similar to findings in predominantly male forensic psychiatric settings, higher levels of impulsivity predicted poorer subjective sleep quality in both women and men. Consistent with well-established associations between depression and sleep, higher levels of depression in both sexes predicted poorer subjective sleep quality. Bidirectional analyses showed that better sleep efficiency decreases depression. Finally, moderation analyses showed that gender does have a primary role in sleep efficiency and marginal effects were found. The observed relations between sleep and personality traits in a typical university sample add to converging evidence of the relationship between sleep and psychopathology and may inform our understanding of the development of psychopathology in young adulthood.

  18. Prenatal Stress due to a Natural Disaster Predicts Adiposity in Childhood: The Iowa Flood Study

    Directory of Open Access Journals (Sweden)

    Kelsey N. Dancause

    2015-01-01

    Full Text Available Prenatal stress can affect lifelong physical growth, including increased obesity risk. However, human studies remain limited. Natural disasters provide models of independent stressors unrelated to confounding maternal characteristics. We assessed degree of objective hardship and subjective distress in women pregnant during severe flooding. At ages 2.5 and 4 years we assessed body mass index (BMI, subscapular plus triceps skinfolds (SS + TR, an index of total adiposity, and SS : TR ratio (an index of central adiposity in their children (n=106. Hierarchical regressions controlled first for several potential confounds. Controlling for these, flood exposure during early gestation predicted greater BMI increase from age 2.5 to 4, as well as total adiposity at 2.5. Greater maternal hardship and distress due to the floods, as well as other nonflood life events during pregnancy, independently predicted greater increase in total adiposity between 2.5 and 4 years. These results support the hypothesis that prenatal stress increases adiposity beginning in childhood and suggest that early gestation is a sensitive period. Results further highlight the additive effects of maternal objective and subjective stress, life events, and depression, emphasizing the importance of continued studies on multiple, detailed measures of maternal mental health and experience in pregnancy and child growth.

  19. Prediction of fruit and vegetable intake from biomarkers using individual participant data of diet-controlled intervention studies

    DEFF Research Database (Denmark)

    Souverein, Olga W; de Vries, Jeanne H M; Freese, Riitta

    2015-01-01

    concentrations. Furthermore, a prediction model of fruit and vegetable intake based on these biomarkers and subject characteristics (i.e. age, sex, BMI and smoking status) was established. Data from twelve diet-controlled intervention studies were obtained to develop a prediction model for fruit and vegetable...

  20. A Study of the Predictive Validity of the Children's Depression Inventory for Major Depression Disorder in Puerto Rican Adolescents

    Science.gov (United States)

    Rivera-Medina, Carmen L.; Bernal, Guillermo; Rossello, Jeannette; Cumba-Aviles, Eduardo

    2010-01-01

    This study aims to evaluate the predictive validity of the Children's Depression Inventory items for major depression disorder (MDD) in an outpatient clinic sample of Puerto Rican adolescents. The sample consisted of 130 adolescents, 13 to 18 years old. The five most frequent symptoms of the Children's Depression Inventory that best predict the…

  1. Electroencephalography Predicts Poor and Good Outcomes After Cardiac Arrest: A Two-Center Study.

    Science.gov (United States)

    Rossetti, Andrea O; Tovar Quiroga, Diego F; Juan, Elsa; Novy, Jan; White, Roger D; Ben-Hamouda, Nawfel; Britton, Jeffrey W; Oddo, Mauro; Rabinstein, Alejandro A

    2017-07-01

    The prognostic role of electroencephalography during and after targeted temperature management in postcardiac arrest patients, relatively to other predictors, is incompletely known. We assessed performances of electroencephalography during and after targeted temperature management toward good and poor outcomes, along with other recognized predictors. Cohort study (April 2009 to March 2016). Two academic hospitals (Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland; Mayo Clinic, Rochester, MN). Consecutive comatose adults admitted after cardiac arrest, identified through prospective registries. All patients were managed with targeted temperature management, receiving prespecified standardized clinical, neurophysiologic (particularly, electroencephalography during and after targeted temperature management), and biochemical evaluations. We assessed electroencephalography variables (reactivity, continuity, epileptiform features, and prespecified "benign" or "highly malignant" patterns based on the American Clinical Neurophysiology Society nomenclature) and other clinical, neurophysiologic (somatosensory-evoked potential), and biochemical prognosticators. Good outcome (Cerebral Performance Categories 1 and 2) and mortality predictions at 3 months were calculated. Among 357 patients, early electroencephalography reactivity and continuity and flexor or better motor reaction had greater than 70% positive predictive value for good outcome; reactivity (80.4%; 95% CI, 75.9-84.4%) and motor response (80.1%; 95% CI, 75.6-84.1%) had highest accuracy. Early benign electroencephalography heralded good outcome in 86.2% (95% CI, 79.8-91.1%). False positive rates for mortality were less than 5% for epileptiform or nonreactive early electroencephalography, nonreactive late electroencephalography, absent somatosensory-evoked potential, absent pupillary or corneal reflexes, presence of myoclonus, and neuron-specific enolase greater than 75 µg/L; accuracy was highest for

  2. Narrative Changes Predict a Decrease in Symptoms in CBT for Depression: An Exploratory Study.

    Science.gov (United States)

    Gonçalves, Miguel M; Silva, Joana Ribeiro; Mendes, Inês; Rosa, Catarina; Ribeiro, António P; Batista, João; Sousa, Inês; Fernandes, Carlos F

    2017-07-01

    Innovative moments (IMs) are new and more adjusted ways of thinking, acting, feeling and relating that emerge during psychotherapy. Previous research on IMs has provided sustainable evidence that IMs differentiate recovered from unchanged psychotherapy cases. However, studies with cognitive behavioural therapy (CBT) are so far absent. The present study tests whether IMs can be reliably identified in CBT and examines if IMs and symptoms' improvement are associated. The following variables were assessed in each session from a sample of six cases of CBT for depression (a total of 111 sessions): (a) symptomatology outcomes (Outcome Questionnaire-OQ-10) and (b) IMs. Two hierarchical linear models were used: one to test whether IMs predicted a symptom decrease in the next session and a second one to test whether symptoms in one session predicted the emergence of IMs in the next session. Innovative moments were better predictors of symptom decrease than the reverse. A higher proportion of a specific type of IMs-reflection 2-in one session predicted a decrease in symptoms in the next session. Thus, when clients further elaborated this type of IM (in which clients describe positive contrasts or elaborate on changes processes), a reduction in symptoms was observed in the next session. A higher expression and elaboration of reflection 2 IMs appear to have a facilitative function in the reduction of depressive symptoms in this sample of CBT. Copyright © 2016 John Wiley & Sons, Ltd. Elaborating innovative moments (IMs) that are new ways of thinking, feeling, behaving and relating, in the therapeutic dialogue, may facilitate change. IMs that are more predictive of amelioration of symptoms in CBT are the ones focused on contrasts between former problematic patterns and new adjusted ones; and the ones in which the clients elaborate on processes of change. Therapists may integrate these kinds of questions (centred on contrasts and centred on what allowed change from the client

  3. Numerical Study on the Effect of Air–Sea–Land Interaction on the Atmospheric Boundary Layer in Coastal Area

    Directory of Open Access Journals (Sweden)

    Zixuan Yang

    2018-02-01

    Full Text Available We have performed large-eddy simulations (LES to study the effect of complex land topography on the atmospheric boundary layer (ABL in coastal areas. The areas under investigation are located at three beaches in Monterey Bay, CA, USA. The sharp-interface immersed boundary method is employed to resolve the land topography down to grid scale. We have considered real-time and what-if cases. In the real-time cases, measurement data and realistic land topographies are directly incorporated. In the what-if cases, the effects of different scenarios of wind speed, wind direction, and terrain pattern on the momentum flux at the beach are studied. The LES results are compared with simulations using the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS and field measurement data. We find that the land topography imposes a critical influence on the ABL in the coastal area. The momentum fluxes obtained from our LES agree with measurement data. Our results indicate the importance of capturing the effects of land topographies in simulations.

  4. Recent Achievements of the Collaboratory for the Study of Earthquake Predictability

    Science.gov (United States)

    Jackson, D. D.; Liukis, M.; Werner, M. J.; Schorlemmer, D.; Yu, J.; Maechling, P. J.; Zechar, J. D.; Jordan, T. H.

    2015-12-01

    Maria Liukis, SCEC, USC; Maximilian Werner, University of Bristol; Danijel Schorlemmer, GFZ Potsdam; John Yu, SCEC, USC; Philip Maechling, SCEC, USC; Jeremy Zechar, Swiss Seismological Service, ETH; Thomas H. Jordan, SCEC, USC, and the CSEP Working Group The Collaboratory for the Study of Earthquake Predictability (CSEP) supports a global program to conduct prospective earthquake forecasting experiments. CSEP testing centers are now operational in California, New Zealand, Japan, China, and Europe with 435 models under evaluation. The California testing center, operated by SCEC, has been operational since Sept 1, 2007, and currently hosts 30-minute, 1-day, 3-month, 1-year and 5-year forecasts, both alarm-based and probabilistic, for California, the Western Pacific, and worldwide. We have reduced testing latency, implemented prototype evaluation of M8 forecasts, and are currently developing formats and procedures to evaluate externally-hosted forecasts and predictions. These efforts are related to CSEP support of the USGS program in operational earthquake forecasting and a DHS project to register and test external forecast procedures from experts outside seismology. A retrospective experiment for the 2010-2012 Canterbury earthquake sequence has been completed, and the results indicate that some physics-based and hybrid models outperform purely statistical (e.g., ETAS) models. The experiment also demonstrates the power of the CSEP cyberinfrastructure for retrospective testing. Our current development includes evaluation strategies that increase computational efficiency for high-resolution global experiments, such as the evaluation of the Global Earthquake Activity Rate (GEAR) model. We describe the open-source CSEP software that is available to researchers as they develop their forecast models (http://northridge.usc.edu/trac/csep/wiki/MiniCSEP). We also discuss applications of CSEP infrastructure to geodetic transient detection and how CSEP procedures are being

  5. Predictive validity of driving-simulator assessments following traumatic brain injury: a preliminary study.

    Science.gov (United States)

    Lew, Henry L; Poole, John H; Lee, Eun Ha; Jaffe, David L; Huang, Hsiu-Chen; Brodd, Edward

    2005-03-01

    To evaluate whether driving simulator and road test evaluations can predict long-term driving performance, we conducted a prospective study on 11 patients with moderate to severe traumatic brain injury. Sixteen healthy subjects were also tested to provide normative values on the simulator at baseline. At their initial evaluation (time-1), subjects' driving skills were measured during a 30-minute simulator trial using an automated 12-measure Simulator Performance Index (SPI), while a trained observer also rated their performance using a Driving Performance Inventory (DPI). In addition, patients were evaluated on the road by a certified driving evaluator. Ten months later (time-2), family members observed patients driving for at least 3 hours over 4 weeks and rated their driving performance using the DPI. At time-1, patients were significantly impaired on automated SPI measures of driving skill, including: speed and steering control, accidents, and vigilance to a divided-attention task. These simulator indices significantly predicted the following aspects of observed driving performance at time-2: handling of automobile controls, regulation of vehicle speed and direction, higher-order judgment and self-control, as well as a trend-level association with car accidents. Automated measures of simulator skill (SPI) were more sensitive and accurate than observational measures of simulator skill (DPI) in predicting actual driving performance. To our surprise, the road test results at time-1 showed no significant relation to driving performance at time-2. Simulator-based assessment of patients with brain injuries can provide ecologically valid measures that, in some cases, may be more sensitive than a traditional road test as predictors of long-term driving performance in the community.

  6. Dental enamel defects predict adolescent health indicators: A cohort study among the Tsimane' of Bolivia.

    Science.gov (United States)

    Masterson, Erin E; Fitzpatrick, Annette L; Enquobahrie, Daniel A; Mancl, Lloyd A; Eisenberg, Dan T A; Conde, Esther; Hujoel, Philippe P

    2018-05-01

    Bioarchaeological findings have linked defective enamel formation in preadulthood with adult mortality. We investigated how defective enamel formation in infancy and childhood is associated with risk factors for adult morbidity and mortality in adolescents. This cohort study of 349 Amerindian adolescents (10-17 years of age) related extent of enamel defects on the central maxillary incisors (none, less than 1/3, 1/3 to 2/3, more than 2/3) to adolescent anthropometrics (height, weight) and biomarkers (hemoglobin, glycated hemoglobin, white blood cell count, and blood pressure). Risk differences and 95% confidence intervals were estimated using multiple linear regression. Enamel defects and stunted growth were compared in their ability to predict adolescent health indicators using log-binomial regression and receiver operating characteristics (ROCs). Greater extent of defective enamel formation on the tooth surface was associated with shorter height (-1.35 cm, 95% CI: -2.17, -0.53), lower weight (-0.98 kg, 95% CI: -1.70, -0.26), lower hemoglobin (-0.36 g/dL, 95% CI: -0.59, -0.13), lower glycated hemoglobin (-0.04 %A 1c , 95% CI: -0.08, -0.00008), and higher white blood cell count (0.74 10 9 /L, 95% CI: 0.35, 1.14) in adolescence. Extent of enamel defects and stunted growth independently performed similarly as risk factors for adverse adolescent outcomes, including anemia, prediabetes/type II diabetes, elevated WBC count, prehypertension/hypertension, and metabolic health. Defective enamel formation in infancy and childhood predicted adolescent health outcomes and may be primarily associated with infection. Extent of enamel defects and stunted growth may be equally predictive of adverse adolescent health outcomes. © 2018 Wiley Periodicals, Inc.

  7. Improved USLE-K factor prediction: A case study on water erosion areas in China

    Directory of Open Access Journals (Sweden)

    Bin Wang

    2016-09-01

    Full Text Available Soil erodibility (K-factor is an essential factor in soil erosion prediction and conservation practises. The major obstacles to any accurate, large-scale soil erodibility estimation are the lack of necessary data on soil characteristics and the misuse of variable K-factor calculators. In this study, we assessed the performance of available erodibility estimators Universal Soil Loss Equation (USLE, Revised Universal Soil Loss Equation (RUSLE, Erosion Productivity Impact Calculator (EPIC and the Geometric Mean Diameter based (Dg model for different geographic regions based on the Chinese soil erodibility database (CSED. Results showed that previous estimators overestimated almost all K-values. Furthermore, only the USLE and Dg approaches could be directly and reliably applicable to black and loess soil regions. Based on the nonlinear best fitting techniques, we improved soil erodibility prediction by combining Dg and soil organic matter (SOM. The NSE, R2 and RE values were 0.94, 0.67 and 9.5% after calibrating the results independently; similar model performance was showed for the validation process. The results obtained via the proposed approach were more accurate that the former K-value predictions. Moreover, those improvements allowed us to effectively establish a regional soil erodibility map (1:250,000 scale of water erosion areas in China. The mean K-value of Chinese water erosion regions was 0.0321 (t ha h·(ha MJ mm−1 with a standard deviation of 0.0107 (t ha h·(ha MJ mm−1; K-values present a decreasing trend from North to South in water erosion areas in China. The yield soil erodibility dataset also satisfactorily corresponded to former K-values from different scales (local, regional, and national.

  8. Gemcitabine-Based Chemotherapy in Adrenocortical Carcinoma: A Multicenter Study of Efficacy and Predictive Factors.

    Science.gov (United States)

    Henning, Judith E K; Deutschbein, Timo; Altieri, Barbara; Steinhauer, Sonja; Kircher, Stefan; Sbiera, Silviu; Wild, Vanessa; Schlötelburg, Wiebke; Kroiss, Matthias; Perotti, Paola; Rosenwald, Andreas; Berruti, Alfredo; Fassnacht, Martin; Ronchi, Cristina L

    2017-11-01

    Adrenocortical carcinoma (ACC) is rare and confers an unfavorable prognosis in advanced stages. Other than combination chemotherapy with cisplatin, etoposide, doxorubicin, and mitotane, the second- and third-line regimens are not well-established. Gemcitabine (GEM)-based chemotherapy was suggested in a phase 2 clinical trial with 28 patients. In other solid tumors, human equilibrative nucleoside transporter type 1 (hENT1) and/or ribonucleotide reductase catalytic subunit M1 (RRM1) expression have been associated with resistance to GEM. To assess the efficacy of GEM-based chemotherapy in ACC in a real-world setting and the predictive role of molecular parameters. Retrospective multicenter study. Referral centers of university hospitals. A total of 145 patients with advanced ACC were treated with GEM-based chemotherapy (132 with concomitant capecitabine). Formalin-fixed paraffin-embedded tumor material was available for 70 patients for immunohistochemistry. The main outcome measures were progression-free survival (PFS) and an objective response to GEM-based chemotherapy. The secondary objective was the predictive role of hENT1 and RRM1. The median PFS for the patient population was 12 weeks (range, 1 to 94). A partial response or stable disease was achieved in 4.9% and 25.0% of cases, with a median duration of 26.8 weeks. Treatment was generally well tolerated, with adverse events of grade 3 or 4 occurring in 11.0% of cases. No substantial effect of hENT1 and/or RRM1 expression was observed in response to GEM-based chemotherapy. GEM-based chemotherapy is a well-tolerated, but modestly active, regimen against advanced ACC. No reliable molecular predictive factors could be identified. Owing to the scarce alternative therapeutic options, GEM-based chemotherapy remains an important option for salvage treatment for advanced ACC. Copyright © 2017 Endocrine Society

  9. Model of lifetime prediction - Study of the behaviour of polymers and organic matrix composites

    International Nuclear Information System (INIS)

    Colin, X.

    2009-01-01

    The team 'Aging of Organic Materials' of the Process and Engineering Laboratory in Mechanics and Materials (Arts et Metiers, ParisTech) has developed the model of lifetime prediction for the prediction of the behaviour of polymers and organic composites. This model has already given evidence of a real predictive mean for various industrial applications, as for instance the prediction of a rupture under the coupled effect of a mechanical load and a chemical degradation. (O.M.)

  10. Study on the Contra-Rotating Propeller system design and full-scale performance prediction method

    Directory of Open Access Journals (Sweden)

    Keh-Sik Min

    2009-09-01

    Full Text Available A ship's screw-propeller produces thrust by rotation and, at the same time, generates rotational flow behind the propeller. This rotational flow has no contribution to the generation of thrust, but instead produces energy loss. By recovering part of the lost energy in the rotational flow, therefore, it is possible to improve the propulsion efficiency. The contra-rotating propeller (CRP system is the representing example of such devices. Unfortunately, however, neither a design method nor a full-scale performance prediction procedure for the CRP system has been well established yet. The authors have long performed studies on the CRP system, and some of the results from the authors’ studies shall be presented and discussed.

  11. Validation Study of a Predictive Algorithm to Evaluate Opioid Use Disorder in a Primary Care Setting

    Science.gov (United States)

    Sharma, Maneesh; Lee, Chee; Kantorovich, Svetlana; Tedtaotao, Maria; Smith, Gregory A.

    2017-01-01

    Background: Opioid abuse in chronic pain patients is a major public health issue. Primary care providers are frequently the first to prescribe opioids to patients suffering from pain, yet do not always have the time or resources to adequately evaluate the risk of opioid use disorder (OUD). Purpose: This study seeks to determine the predictability of aberrant behavior to opioids using a comprehensive scoring algorithm (“profile”) incorporating phenotypic and, more uniquely, genotypic risk factors. Methods and Results: In a validation study with 452 participants diagnosed with OUD and 1237 controls, the algorithm successfully categorized patients at high and moderate risk of OUD with 91.8% sensitivity. Regardless of changes in the prevalence of OUD, sensitivity of the algorithm remained >90%. Conclusion: The algorithm correctly stratifies primary care patients into low-, moderate-, and high-risk categories to appropriately identify patients in need for additional guidance, monitoring, or treatment changes. PMID:28890908

  12. Prediction of required ozone dosage for pilot recirculating aquaculture systems based on laboratory studies

    DEFF Research Database (Denmark)

    Spiliotopoulou, Aikaterini; Rojas-Tirado, Paula Andrea; Kaarsholm, Kamilla Marie Speht

    2017-01-01

    In recirculating aquaculture systems (RAS), the water quality changes continuously. Organic and inorganic compounds accumulates creating toxic conditions for the farmed organisms. Ozone improves water quality diminishing significantly both bacteria load and dissolved organic matter. However......, in a non-meticulously designed system, residual ozone might reach the culture tanks causing significant harm to cultured species or excess costs. The aim of the study was to predict the suitable ozone dosage in pilot RAS, for water treatment purposes, based on laboratory studies. The ozone effect on water...... quality of freshwater RAS and system’s ozone demand was investigated. Bench-scale ozonation experiments revealed the ozone demand of the system to be 180 mg O3/h. Three different ozone dosages were applied to four replicated systems with fixed feed loading (1.56 kg feed/m3 make up water). Results...

  13. A model of integration among prediction tools: applied study to road freight transportation

    Directory of Open Access Journals (Sweden)

    Henrique Dias Blois

    Full Text Available Abstract This study has developed a scenery analysis model which has integrated decision-making tools on investments: prospective scenarios (Grumbach Method and systems dynamics (hard modeling, with the innovated multivariate analysis of experts. It was designed through analysis and simulation scenarios and showed which are the most striking events in the study object as well as highlighted the actions could redirect the future of the analyzed system. Moreover, predictions are likely to be developed through the generated scenarios. The model has been validated empirically with road freight transport data from state of Rio Grande do Sul, Brazil. The results showed that the model contributes to the analysis of investment because it identifies probabilities of events that impact on decision making, and identifies priorities for action, reducing uncertainties in the future. Moreover, it allows an interdisciplinary discussion that correlates different areas of knowledge, fundamental when you wish more consistency in creating scenarios.

  14. Predictive typing of drug-induced neurological sufferings from studies of the distribution of labelled drugs

    International Nuclear Information System (INIS)

    Takasu, T.

    1980-01-01

    A drug given to an animal becomes widely distributed throughout the body, acting on the living mechanisms or structures, and is gradually excreted. Some drugs can remain in some parts of the body for a long period. For example, 14 C-chloramphenical was found to remain preferentially in the salivary gland, liver and bone marrow of mice 24 hours after its oral administration. If such a drug is given repeatedly, it could possibly accumulate gradually in these organs. Thus, when its accumulation in a particular part of the body exceeds a certain level, the living mechanism or structure may possibly be injured. The harmful effects of a drug in repeated administration are called its chronic toxicity. The author discusses whether it is possible to predict the toxicity of a drug by studying its distribution in relation to time, and, if possible, the points in time. This problem is studied especially in relation to the nervous system. (Auth.)

  15. Self-perceived memory complaints predict progression to Alzheimer disease. The LADIS study

    DEFF Research Database (Denmark)

    Verdelho, Ana; Madureira, Sofia; Moleiro, Carla

    2011-01-01

    the follow-up (ß = 2.7, p = 0.008; HR = 15.5, CI 95% [2.04, 117.6]), independently of other confounders, namely depressive symptoms, WMC severity, medial temporal lobe atrophy, and global cognition status at baseline. Self perceived memory complaints did not predict vascular dementia. In the LADIS study......Memory complaints are frequent in the elderly but its implications in cognition over time remain a controversial issue. Our objective was to evaluate the risk of self perceived memory complaints in the evolution for future dementia. The LADIS (Leukoaraiosis and Disability) prospective multinational...... battery. Dementia and subtypes of dementia were classified. Self perceived memory complaints in independent elderly were collected during the interview. MRI was performed at entry and at the end of the study. 639 subjects were included (74.1 ± 5 years old, 55% women, 9.6 ± 3.8 years of schooling). At end...

  16. Effect of dissolved organic matter on pre-equilibrium passive sampling: A predictive QSAR modeling study.

    Science.gov (United States)

    Lin, Wei; Jiang, Ruifen; Shen, Yong; Xiong, Yaxin; Hu, Sizi; Xu, Jianqiao; Ouyang, Gangfeng

    2018-04-13

    Pre-equilibrium passive sampling is a simple and promising technique for studying sampling kinetics, which is crucial to determine the distribution, transfer and fate of hydrophobic organic compounds (HOCs) in environmental water and organisms. Environmental water samples contain complex matrices that complicate the traditional calibration process for obtaining the accurate rate constants. This study proposed a QSAR model to predict the sampling rate constants of HOCs (polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs) and pesticides) in aqueous systems containing complex matrices. A homemade flow-through system was established to simulate an actual aqueous environment containing dissolved organic matter (DOM) i.e. humic acid (HA) and (2-Hydroxypropyl)-β-cyclodextrin (β-HPCD)), and to obtain the experimental rate constants. Then, a quantitative structure-activity relationship (QSAR) model using Genetic Algorithm-Multiple Linear Regression (GA-MLR) was found to correlate the experimental rate constants to the system state including physicochemical parameters of the HOCs and DOM which were calculated and selected as descriptors by Density Functional Theory (DFT) and Chem 3D. The experimental results showed that the rate constants significantly increased as the concentration of DOM increased, and the enhancement factors of 70-fold and 34-fold were observed for the HOCs in HA and β-HPCD, respectively. The established QSAR model was validated as credible (R Adj. 2 =0.862) and predictable (Q 2 =0.835) in estimating the rate constants of HOCs for complex aqueous sampling, and a probable mechanism was developed by comparison to the reported theoretical study. The present study established a QSAR model of passive sampling rate constants and calibrated the effect of DOM on the sampling kinetics. Copyright © 2018 Elsevier B.V. All rights reserved.

  17. Evaluation of the white finger risk prediction model in ISO 5349 suggests need for prospective studies.

    Science.gov (United States)

    Gemne, G; Lundström, R

    1996-05-01

    The risk prediction model for white fingers in Annex A of ISO 5349 is not likely to offer protection from all tools and all work processes. It is also probable that some work place changes it has initiated are either redundant or lack the intended effect. The main reasons for these shortcomings are the following. The often demonstrated disagreement between predicted and observed white fingers occurrence may be related to the fact that the model is based on latency data. This leads to an overestimation, to an unknown extent, of true group risks. A possible healthy worker effect, resulting in underestimation, has not been considered, and uncertainty because of recall bias is connected with using latency as effect variable in a slowly developing disorder like white fingers. The diagnostic criteria for white fingers have varied over the years, causing a possible inclusion of circulatory disturbances other than those induced by vibration. Among insufficiently clarified matters unrelated to vibration are variations in individual susceptibility and other host factors that modify vibration effects, uncertainty concerning daily or total effective exposure, and the fact that variation in work methods and processes as well as ergonomic factors other than vibration tend to make different groups incomparable form the viewpoint of risk of injury. Lack of sufficient data on vibration measurements and employment durations add to the uncertainty, as do variations in tool conditions (grinder wheels, etc) and inherent difficulties in measurement. Finally, the ISO 5349 frequency-weighting curve only relates to acute sensory effects rather than chronic effects on vascular functions like white fingers, and directional difference in sensitivity has not been incorporated in the curve. Data on exposure-response relationships are needed from prospective studies that monitor the dose of exposure to special vibration types and all relevant environmental agents, employ diagnostics with good

  18. A prediction rule for shoulder pain related sick leave: a prospective cohort study

    Directory of Open Access Journals (Sweden)

    van der Heijden Geert JMG

    2006-12-01

    Full Text Available Abstract Background Shoulder pain is common in primary care, and has an unfavourable outcome in many patients. Information about predictors of shoulder pain related sick leave in workers is scarce and inconsistent. The objective was to develop a clinical prediction rule for calculating the risk of shoulder pain related sick leave for individual workers, during the 6 months following first consultation in general practice. Methods A prospective cohort study with 6 months follow-up was conducted among 350 workers with a new episode of shoulder pain. Potential predictors included the results of a physical examination, sociodemographic variables, disease characteristics (duration of symptoms, sick leave in the 2 months prior to consultation, pain intensity, disability, comorbidity, physical activity, physical work load, psychological factors, and the psychosocial work environment. The main outcome measure was sick leave during 6 months following first consultation in general practice. Results Response rate to the follow-up questionnaire at 6 months was 85%. During the 6 months after first consultation 30% (89/298 of the workers reported sick leave. 16% (47 reported 10 days sick leave or more. Sick leave during this period was predicted in a multivariable model by a longer duration of sick leave prior to consultation, more shoulder pain, a perceived cause of strain or overuse during regular activities, and co-existing psychological complaints. The discriminative ability of the prediction model was satisfactory with an area under the curve of 0.70 (95% CI 0.64–0.76. Conclusion Although 30% of all workers with shoulder pain reported sick leave during follow-up, the duration of sick leave was limited to a few days in most workers. We developed a prediction rule and a score chart that can be used by general practitioners and occupational health care providers to calculate the absolute risk of sick leave in individual workers with shoulder pain, which

  19. Personalized prediction of lifetime benefits with statin therapy for asymptomatic individuals: a modeling study.

    Directory of Open Access Journals (Sweden)

    Bart S Ferket

    Full Text Available BACKGROUND: Physicians need to inform asymptomatic individuals about personalized outcomes of statin therapy for primary prevention of cardiovascular disease (CVD. However, current prediction models focus on short-term outcomes and ignore the competing risk of death due to other causes. We aimed to predict the potential lifetime benefits with statin therapy, taking into account competing risks. METHODS AND FINDINGS: A microsimulation model based on 5-y follow-up data from the Rotterdam Study, a population-based cohort of individuals aged 55 y and older living in the Ommoord district of Rotterdam, the Netherlands, was used to estimate lifetime outcomes with and without statin therapy. The model was validated in-sample using 10-y follow-up data. We used baseline variables and model output to construct (1 a web-based calculator for gains in total and CVD-free life expectancy and (2 color charts for comparing these gains to the Systematic Coronary Risk Evaluation (SCORE charts. In 2,428 participants (mean age 67.7 y, 35.5% men, statin therapy increased total life expectancy by 0.3 y (SD 0.2 and CVD-free life expectancy by 0.7 y (SD 0.4. Age, sex, smoking, blood pressure, hypertension, lipids, diabetes, glucose, body mass index, waist-to-hip ratio, and creatinine were included in the calculator. Gains in total and CVD-free life expectancy increased with blood pressure, unfavorable lipid levels, and body mass index after multivariable adjustment. Gains decreased considerably with advancing age, while SCORE 10-y CVD mortality risk increased with age. Twenty-five percent of participants with a low SCORE risk achieved equal or larger gains in CVD-free life expectancy than the median gain in participants with a high SCORE risk. CONCLUSIONS: We developed tools to predict personalized increases in total and CVD-free life expectancy with statin therapy. The predicted gains we found are small. If the underlying model is validated in an independent cohort, the

  20. A simplified approach to the pooled analysis of calibration of clinical prediction rules for systematic reviews of validation studies

    Directory of Open Access Journals (Sweden)

    Dimitrov BD

    2015-04-01

    Full Text Available Borislav D Dimitrov,1,2 Nicola Motterlini,2,† Tom Fahey2 1Academic Unit of Primary Care and Population Sciences, University of Southampton, Southampton, United Kingdom; 2HRB Centre for Primary Care Research, Department of General Medicine, Division of Population Health Sciences, Royal College of Surgeons in Ireland, Dublin, Ireland †Nicola Motterlini passed away on November 11, 2012 Objective: Estimating calibration performance of clinical prediction rules (CPRs in systematic reviews of validation studies is not possible when predicted values are neither published nor accessible or sufficient or no individual participant or patient data are available. Our aims were to describe a simplified approach for outcomes prediction and calibration assessment and evaluate its functionality and validity. Study design and methods: Methodological study of systematic reviews of validation studies of CPRs: a ABCD2 rule for prediction of 7 day stroke; and b CRB-65 rule for prediction of 30 day mortality. Predicted outcomes in a sample validation study were computed by CPR distribution patterns (“derivation model”. As confirmation, a logistic regression model (with derivation study coefficients was applied to CPR-based dummy variables in the validation study. Meta-analysis of validation studies provided pooled estimates of “predicted:observed” risk ratios (RRs, 95% confidence intervals (CIs, and indexes of heterogeneity (I2 on forest plots (fixed and random effects models, with and without adjustment of intercepts. The above approach was also applied to the CRB-65 rule. Results: Our simplified method, applied to ABCD2 rule in three risk strata (low, 0–3; intermediate, 4–5; high, 6–7 points, indicated that predictions are identical to those computed by univariate, CPR-based logistic regression model. Discrimination was good (c-statistics =0.61–0.82, however, calibration in some studies was low. In such cases with miscalibration, the under-prediction

  1. Can prematurity risk in twin pregnancies after in vitro fertilization be predicted? A retrospective study

    Directory of Open Access Journals (Sweden)

    Barad David

    2009-01-01

    Full Text Available Abstract Background Assisted reproduction (ART contributes to world-wide increases of twin pregnancies, in turn raising prematurity risks. Whether characteristics of ART cycles, resulting in twin gestations, can predict prematurity risks was the subject of this study. Methods One-hundred-and-six women, ages 20 to 39 years, with consecutive dichorionic-diamniotic (DC/DA twin gestations were retrospectively investigated. All pregnancies investigated followed fresh ART cycles, with use of autologous gamets, and were delivered at a university-based high-risk, maternal-fetal medicine unit. Only premature deliveries (i.e., <37.0 weeks gestational age, with viable neonate(s of ≥ 500 grams, were considered for analysis. Results After 1.8 +/- 1.2 ART cycles, 11.0 +/- 5.4 oocytes were retrieved and 2.4 +/- 0.9 embryos transferred in 106 women aged 31.6 +/- 4.2 years. Indications for ART treatment were male factor in 51.9%, female infertility in 27.4% and combined infertility in 20.8%. Though maternal age significantly influenced prematurity risk (p < 0.05, paternal age, maternal body mass index, indications for fertility treatment, number of previous ART attempts, oocytes retrieved or embryos transferred, as well as stimulation protocols and previous ART pregnancies, were not associated with gestational duration in twin pregnancies. Summary Except for female age, baseline and ART cycle characteristics do not allow for prediction of prematurity risk in dichorionic twin gestations after assisted reproduction.

  2. Quantitative Lymphoscintigraphy to Predict the Possibility of Lymphedema Development After Breast Cancer Surgery: Retrospective Clinical Study.

    Science.gov (United States)

    Kim, Paul; Lee, Ju Kang; Lim, Oh Kyung; Park, Heung Kyu; Park, Ki Deok

    2017-12-01

    To predict the probability of lymphedema development in breast cancer patients in the early post-operation stage, we investigated the ability of quantitative lymphoscintigraphic assessment. This retrospective study included 201 patients without lymphedema after unilateral breast cancer surgery. Lymphoscintigraphy was performed between 4 and 8 weeks after surgery to evaluate the lymphatic system in the early postoperative stage. Quantitative lymphoscintigraphy was performed using four methods: ratio of radiopharmaceutical clearance rate of the affected to normal hand; ratio of radioactivity of the affected to normal hand; ratio of radiopharmaceutical uptake rate of the affected to normal axilla (RUA); and ratio of radioactivity of the affected to normal axilla (RRA). During a 1-year follow-up, patients with a circumferential interlimb difference of 2 cm at any measurement location and a 200-mL interlimb volume difference were diagnosed with lymphedema. We investigated the difference in quantitative lymphoscintigraphic assessment between the non-lymphedema and lymphedema groups. Quantitative lymphoscintigraphic assessment revealed that the RUA and RRA were significantly lower in the lymphedema group than in the non-lymphedema group. After adjusting the model for all significant variables (body mass index, N-stage, T-stage, type of surgery, and type of lymph node surgery), RRA was associated with lymphedema (odds ratio=0.14; 95% confidence interval, 0.04-0.46; p=0.001). In patients in the early postoperative stage after unilateral breast cancer surgery, quantitative lymphoscintigraphic assessment can be used to predict the probability of developing lymphedema.

  3. Study on performance prediction and energy saving of indirect evaporative cooling system

    Energy Technology Data Exchange (ETDEWEB)

    Yoo, Seong Yeon; Kim, Tae Ho; Kim, Myung Ho [Dept. of Mechanical Design Engineering, Chungnam National University, Daejeon (Korea, Republic of)

    2015-09-15

    The purpose of this study is to predict the performance of an indirect evaporative cooling system, and to evaluate its energy saving effect when applied to the exhaust heat recovery system of an air-handling unit. We derive the performance correlation of the indirect evaporative cooling system using a plastic heat exchanger based on experimental data obtained in various conditions. We predict the variations in the performance of the system for various return and outdoor air conditioning systems using the obtained correlation. We also analyze the energy saving of the system realized by the exhaust heat recovery using the typical meteorological data for several cities in Korea. The average utilization rate of the sensible cooling system for the exhaust heat recovery is 44.3% during summer, while that of the evaporative cooling system is 96.7%. The energy saving of the evaporative cooling system is much higher compared to the sensible cooling system, and was about 3.89 times the value obtained in Seoul.

  4. Blood pressure reactivity to psychological stress predicts hypertension in the CARDIA study.

    Science.gov (United States)

    Matthews, Karen A; Katholi, Charles R; McCreath, Heather; Whooley, Mary A; Williams, David R; Zhu, Sha; Markovitz, Jerry H

    2004-07-06

    A longstanding but controversial hypothesis is that individuals who exhibit frequent, large increases in blood pressure (BP) during psychological stress are at risk for developing essential hypertension. We tested whether BP changes during psychological stress predict incident hypertension in young adults. We used survival analysis to predict hypertensive status during 13 years of follow-up in a sample of >4100 normotensive black and white men and women (age at entry, 18 to 30 years) enrolled in the CARDIA study. BP responses to 3 psychological challenges--cold pressor, star tracing, and video game tasks--were measured. Hypertensive status was defined as use of antihypertensive medication or measured BP > or =140/90 mm Hg. After adjustment for race, gender, covariates (education, body mass index, age, and resting pressure), and their significant interactions, the larger the BP responses were to each of the 3 tasks, the earlier hypertension occurred (Pvideo game was apparent for men. Young adults who show a large BP response to psychological stress may be at risk for hypertension as they approach midlife.

  5. Predicting expressway crash frequency using a random effect negative binomial model: A case study in China.

    Science.gov (United States)

    Ma, Zhuanglin; Zhang, Honglu; Chien, Steven I-Jy; Wang, Jin; Dong, Chunjiao

    2017-01-01

    To investigate the relationship between crash frequency and potential influence factors, the accident data for events occurring on a 50km long expressway in China, including 567 crash records (2006-2008), were collected and analyzed. Both the fixed-length and the homogeneous longitudinal grade methods were applied to divide the study expressway section into segments. A negative binomial (NB) model and a random effect negative binomial (RENB) model were developed to predict crash frequency. The parameters of both models were determined using the maximum likelihood (ML) method, and the mixed stepwise procedure was applied to examine the significance of explanatory variables. Three explanatory variables, including longitudinal grade, road width, and ratio of longitudinal grade and curve radius (RGR), were found as significantly affecting crash frequency. The marginal effects of significant explanatory variables to the crash frequency were analyzed. The model performance was determined by the relative prediction error and the cumulative standardized residual. The results show that the RENB model outperforms the NB model. It was also found that the model performance with the fixed-length segment method is superior to that with the homogeneous longitudinal grade segment method. Copyright © 2016. Published by Elsevier Ltd.

  6. Spontaneous prematurity in fetuses with congenital diaphragmatic hernia: a retrospective cohort study about prenatal predictive factors.

    Science.gov (United States)

    Barbosa, Bruna Maria Lopes; Rodrigues, Agatha S; Carvalho, Mario Henrique Burlacchini; Bittar, Roberto Eduardo; Francisco, Rossana Pulcineli Vieira; Bernardes, Lisandra Stein

    2018-01-12

    To evaluate possible predictive factors of spontaneous prematurity in fetuses with congenital diaphragmatic hernia (CDH). A retrospective cohort study was performed. Inclusion criteria were presence of CDH; absence of fetoscopy; absence of karyotype abnormality; maximum of one major malformation associated with diaphragmatic hernia; ultrasound monitoring at the Obstetrics Clinic of Clinicas Hospital at the University of São Paulo School of Medicine, from January 2001 to October 2014. The data were obtained through the electronic records and ultrasound system of our fetal medicine service. The following variables were analyzed: maternal age, primiparity, associated maternal diseases, smoking, previous spontaneous preterm birth, fetal malformation associated with hernia, polyhydramnios, fetal growth restriction, presence of intrathoracic liver, invasive procedures performed, side of hernia and observed-to- expected lung to head ratio (o/e LHR). On individual analysis, variables were assessed using the Chi-square test and the Mann-Whitney test. A multiple logistic regression model was applied to select variables independently influencing the prediction of preterm delivery. A ROC curve was constructed with the significant variable, identifying the values with best sensitivity and specificity to be suggested for use in clinical practice. Eighty fetuses were evaluated, of which, 21 (26.25%) were premature. O/e LHR was the only factor associated with prematurity (p = 0.020). The ROC curve showed 93% sensitivity with 48.4% specificity for the cutoff of 40%. O/e LHR was the only predictor of prematurity in this sample.

  7. Prediction of Radionuclide transfer based on soil parameters: application to vulnerability studies

    International Nuclear Information System (INIS)

    Roig, M.; Vidal, M.; Rauret, G.

    1998-01-01

    The multi factorial character of the radiocaesium and radiostrontium soil-to-plan transfer, which depends on the radionuclide level in the soil solution amplified by a plant factor, prevents from establishing univariate relationships between transfer factors and soil and/or plant parameters. The plant factor is inversely proportional to the level of competitive species in the soil solution (Ca and Mg, for radiostrontium, and K and NH 4 for radiocaesium). Radionuclide level in soil solution depends on the radionuclide available fraction and its distribution coefficient. For radiostrontium, this may be obtained from the Cationic Exchange Capacity (CEC), whereas for radiocaesium the Specific Interception Potential should be calculate, both corrected by the concentrations of the competitive species and selectivity coefficients. Therefore, the transfer factor eventually depends on soil solution composition, the available fraction and the number of sorption sites, as well as on the plant factor. For a given plant, a relative sequence of transfer can be set up based solely on soil parameters, since the plant factor is cancelled. This prediction model has been compared with transfer data from experiments with Mediterranean, mineral soils, contaminated with a thermo generated aerosol, and with podzolic and organic soils, contaminated by the Chernobyl fallout. These studies revealed that it was possible to predict a relative scale of transfer for any type of soil, also allowing a scale of soil vulnerability to radiostrontium and radiocaesium contamination to be set up. (Author)

  8. Large-scale replication study reveals a limit on probabilistic prediction in language comprehension.

    Science.gov (United States)

    Nieuwland, Mante S; Politzer-Ahles, Stephen; Heyselaar, Evelien; Segaert, Katrien; Darley, Emily; Kazanina, Nina; Von Grebmer Zu Wolfsthurn, Sarah; Bartolozzi, Federica; Kogan, Vita; Ito, Aine; Mézière, Diane; Barr, Dale J; Rousselet, Guillaume A; Ferguson, Heather J; Busch-Moreno, Simon; Fu, Xiao; Tuomainen, Jyrki; Kulakova, Eugenia; Husband, E Matthew; Donaldson, David I; Kohút, Zdenko; Rueschemeyer, Shirley-Ann; Huettig, Falk

    2018-04-03

    Do people routinely pre-activate the meaning and even the phonological form of upcoming words? The most acclaimed evidence for phonological prediction comes from a 2005 Nature Neuroscience publication by DeLong, Urbach and Kutas, who observed a graded modulation of electrical brain potentials (N400) to nouns and preceding articles by the probability that people use a word to continue the sentence fragment ('cloze'). In our direct replication study spanning 9 laboratories ( N =334), pre-registered replication-analyses and exploratory Bayes factor analyses successfully replicated the noun-results but, crucially, not the article-results. Pre-registered single-trial analyses also yielded a statistically significant effect for the nouns but not the articles. Exploratory Bayesian single-trial analyses showed that the article-effect may be non-zero but is likely far smaller than originally reported and too small to observe without very large sample sizes. Our results do not support the view that readers routinely pre-activate the phonological form of predictable words. © 2018, Nieuwland et al.

  9. Prediction of Surface Roughness in End Milling Process Using Intelligent Systems: A Comparative Study

    Directory of Open Access Journals (Sweden)

    Abdel Badie Sharkawy

    2011-01-01

    Full Text Available A study is presented to model surface roughness in end milling process. Three types of intelligent networks have been considered. They are (i radial basis function neural networks (RBFNs, (ii adaptive neurofuzzy inference systems (ANFISs, and (iii genetically evolved fuzzy inference systems (G-FISs. The machining parameters, namely, the spindle speed, feed rate, and depth of cut have been used as inputs to model the workpiece surface roughness. The goal is to get the best prediction accuracy. The procedure is illustrated using experimental data of end milling 6061 aluminum alloy. The three networks have been trained using experimental training data. After training, they have been examined using another set of data, that is, validation data. Results are compared with previously published results. It is concluded that ANFIS networks may suffer the local minima problem, and genetic tuning of fuzzy networks cannot insure perfect optimality unless suitable parameter setting (population size, number of generations etc. and tuning range for the FIS, parameters are used which can be hardly satisfied. It is shown that the RBFN model has the best performance (prediction accuracy in this particular case.

  10. Predictive gene lists for breast cancer prognosis: A topographic visualisation study

    Directory of Open Access Journals (Sweden)

    Lowe David

    2008-04-01

    Full Text Available Abstract Background The controversy surrounding the non-uniqueness of predictive gene lists (PGL of small selected subsets of genes from very large potential candidates as available in DNA microarray experiments is now widely acknowledged 1. Many of these studies have focused on constructing discriminative semi-parametric models and as such are also subject to the issue of random correlations of sparse model selection in high dimensional spaces. In this work we outline a different approach based around an unsupervised patient-specific nonlinear topographic projection in predictive gene lists. Methods We construct nonlinear topographic projection maps based on inter-patient gene-list relative dissimilarities. The Neuroscale, the Stochastic Neighbor Embedding(SNE and the Locally Linear Embedding(LLE techniques have been used to construct two-dimensional projective visualisation plots of 70 dimensional PGLs per patient, classifiers are also constructed to identify the prognosis indicator of each patient using the resulting projections from those visualisation techniques and investigate whether a-posteriori two prognosis groups are separable on the evidence of the gene lists. A literature-proposed predictive gene list for breast cancer is benchmarked against a separate gene list using the above methods. Generalisation ability is investigated by using the mapping capability of Neuroscale to visualise the follow-up study, but based on the projections derived from the original dataset. Results The results indicate that small subsets of patient-specific PGLs have insufficient prognostic dissimilarity to permit a distinction between two prognosis patients. Uncertainty and diversity across multiple gene expressions prevents unambiguous or even confident patient grouping. Comparative projections across different PGLs provide similar results. Conclusion The random correlation effect to an arbitrary outcome induced by small subset selection from very high

  11. Hopefulness predicts resilience after hereditary colorectal cancer genetic testing: a prospective outcome trajectories study

    Directory of Open Access Journals (Sweden)

    Chu Annie TW

    2010-06-01

    Full Text Available Abstract Background - Genetic testing for hereditary colorectal cancer (HCRC had significant psychological consequences for test recipients. This prospective longitudinal study investigated the factors that predict psychological resilience in adults undergoing genetic testing for HCRC. Methods - A longitudinal study was carried out from April 2003 to August 2006 on Hong Kong Chinese HCRC family members who were recruited and offered genetic testing by the Hereditary Gastrointestinal Cancer Registry to determine psychological outcomes after genetic testing. Self-completed questionnaires were administered immediately before (pre-disclosure baseline and 2 weeks, 4 months and 1 year after result disclosure. Using validated psychological inventories, the cognitive style of hope was measured at baseline, and the psychological distress of depression and anxiety was measured at all time points. Results - Of the 76 participating subjects, 71 individuals (43 men and 28 women; mean age 38.9 ± 9.2 years from nine FAP and 24 HNPCC families completed the study, including 39 mutated gene carriers. Four patterns of outcome trajectories were created using established norms for the specified outcome measures of depression and anxiety. These included chronic dysfunction (13% and 8.7%, recovery (0% and 4.3%, delayed dysfunction (13% and 15.9% and resilience (76.8% and 66.7%. Two logistic regression analyses were conducted using hope at baseline to predict resilience, with depression and anxiety employed as outcome indicators. Because of the small number of participants, the chronic dysfunction and delayed dysfunction groups were combined into a non-resilient group for comparison with the resilient group in all subsequent analysis. Because of low frequencies, participants exhibiting a recovery trajectory (n = 3 for anxiety and n = 0 for depression were excluded from further analysis. Both regression equations were significant. Baseline hope was a significant

  12. Hopefulness predicts resilience after hereditary colorectal cancer genetic testing: a prospective outcome trajectories study.

    Science.gov (United States)

    Ho, Samuel M Y; Ho, Judy W C; Bonanno, George A; Chu, Annie T W; Chan, Emily M S

    2010-06-11

    Genetic testing for hereditary colorectal cancer (HCRC) had significant psychological consequences for test recipients. This prospective longitudinal study investigated the factors that predict psychological resilience in adults undergoing genetic testing for HCRC. A longitudinal study was carried out from April 2003 to August 2006 on Hong Kong Chinese HCRC family members who were recruited and offered genetic testing by the Hereditary Gastrointestinal Cancer Registry to determine psychological outcomes after genetic testing. Self-completed questionnaires were administered immediately before (pre-disclosure baseline) and 2 weeks, 4 months and 1 year after result disclosure. Using validated psychological inventories, the cognitive style of hope was measured at baseline, and the psychological distress of depression and anxiety was measured at all time points. Of the 76 participating subjects, 71 individuals (43 men and 28 women; mean age 38.9 +/- 9.2 years) from nine FAP and 24 HNPCC families completed the study, including 39 mutated gene carriers. Four patterns of outcome trajectories were created using established norms for the specified outcome measures of depression and anxiety. These included chronic dysfunction (13% and 8.7%), recovery (0% and 4.3%), delayed dysfunction (13% and 15.9%) and resilience (76.8% and 66.7%). Two logistic regression analyses were conducted using hope at baseline to predict resilience, with depression and anxiety employed as outcome indicators. Because of the small number of participants, the chronic dysfunction and delayed dysfunction groups were combined into a non-resilient group for comparison with the resilient group in all subsequent analysis. Because of low frequencies, participants exhibiting a recovery trajectory (n = 3 for anxiety and n = 0 for depression) were excluded from further analysis. Both regression equations were significant. Baseline hope was a significant predictor of a resilience outcome trajectory for depression

  13. What do predict anxiety and depression in breast cancer patients? A follow-up study.

    Science.gov (United States)

    Vahdaninia, Mariam; Omidvari, Sepideh; Montazeri, Ali

    2010-03-01

    Psychological adjustment following cancer occurrence remains a key issue among the survivors. This study aimed to investigate psychological distress in patients with breast cancer following completion of breast cancer treatments and to determine its associated factors. This was a prospective study of anxiety and depression in breast cancer patients. Anxiety and depression were measured using the Hospital Anxiety and Depression Scale at three points in time: baseline (pre-diagnosis), 3 months after initial treatment and 1 year after completion of treatment (in all 18 months follow-up). At baseline, the questionnaires were administered to all the suspected patients while both patients and the interviewer were blind to the final diagnosis. Socio-demographic and clinical data included age, education, marital status, disease stage and initial treatment. Repeated measure analysis was performed to compare anxiety and depression over the study period. Logistic regression analysis was performed to determine variables that predict anxiety and depression. Altogether 167 patients were diagnosed with breast cancer. The mean age of breast cancer patients was 47.2 (SD = 13.5) years, and the vast majority underwent mastectomy (82.6%). At 18 months follow-up, data for 99 patients were available. The results showed that anxiety and depression improved over the time (P < 0.001) although at 18-month follow-up, 38.4% and 22.2% of the patients presented with severe anxiety and depression, respectively. 'Fatigue' was found to be a risk factor for developing anxiety and depression at 3 months follow-up [odds ratio (OR) = 1.04, 95% Confidence interval (CI) = 1.01-1.07 and OR = 1.04, 95% CI = 1.02-1.07 respectively]. At 18 months follow-up, anxiety was predicted by 'pain' (OR = 1.02, 95% CI = 1.00-1.05), whereas depression was predicted by both 'fatigue' (OR = 1.06, 95% CI = 1.02-1.09) and 'pain' (OR = 1.05, 95% CI = 1.01-1.08). Although the findings indicated that the levels of anxiety and

  14. Predictive genetic testing in children and adults: a study of emotional impact.

    Science.gov (United States)

    Michie, S; Bobrow, M; Marteau, T M

    2001-08-01

    To determine whether, following predictive genetic testing for familial adenomatous polyposis (FAP), children or adults receiving positive results experience clinically significant levels of anxiety or depression, and whether children receiving positive results experience higher levels of anxiety or depression than adults receiving positive results. Two studies, one cross sectional and one prospective. 208 unaffected subjects (148 adults and 60 children) at risk for FAP who have undergone genetic testing since 1990. anxiety, depression; independent variables: test results, demographic measures, psychological resources (optimism, self-esteem). Study 1. In children receiving positive results, mean scores for anxiety and depression were within the normal range. There was a trend for children receiving positive results to be more anxious and depressed than those receiving negative results. In adults, mean scores for anxiety were within the normal range for those receiving negative results, but were in the clinical range for those receiving positive results, with 43% (95% CI 23-65) of the latter having scores in this range. Regardless of test result, adults were more likely to be clinically anxious if they were low in optimism or self-esteem. Children receiving positive or negative results did not experience greater anxiety or depression than adults. Study 2. For children receiving a positive test result, mean scores for anxiety, depression, and self-esteem were unchanged over the year following the result, while mean anxiety scores decreased and self-esteem increased after receipt of a negative test result over the same period of time. Children, as a group, did not show clinically significant distress over the first year following predictive genetic testing. Adults were more likely to be clinically anxious if they received a positive result or were low in optimism or self-esteem, with interacting effects. The association between anxiety, self-esteem, and optimism

  15. Cheiloscopy, Palatoscopy and Odontometrics in Sex Prediction and Dis-crimination - a Comparative Study.

    Science.gov (United States)

    V, Nagalaxmi; Ugrappa, Sridevi; M, Naga Jyothi; Ch, Lalitha; Maloth, Kotya Naik; Kodangal, Srikanth

    2014-01-01

    Human identification is the forensic odontologist's primary duty in the fields like violent crime, child abuse, elder abuse, missing persons and mass disaster scenarios. In each context, dental traits may produce compelling evidence to aid victim identity, suspect identity and narrow down the outcome of investigative casework. Sometimes it becomes necessary to apply some least known and less popular techniques in identification procedure where lip prints, rugae patterns and canine odontometrics can give us comparatively valid conclusions pertaining to person's identification. This study elucidates the significance of cheiloscopy, palatoscopy and canine odontometry in sex prediction and discrimination. A cross- sectional study involving a total of 60 subjects, 30 males and 30 females were selected from the outpatient department of oral medicine and radiology. Lip prints were recorded using lipstick, palatal impressions were taken with alginate and odontometric measurements were taken with digital vernier calipers from every subject. All the obtained records were analyzed by two observers. Reliability of lip prints was assessed using Kappa coefficient. Comparison of rugae patterns was done using Chi-square test. Mean canine and inter canine width was compared using t test. A p-value of print patterns analyzed in males and females, while no significant difference was observed in the rugae patterns but a significant difference in the mesio-distal width of mandibular canines in males and females was found with right mandibular canine(3.73%) showing greater sexual dimorphism compared to left mandibular canine(3.06%). This study shows the uniqueness of the lip prints and rugae patterns with the lip prints showing sensitivity of 81.7% giving reliable prediction of sex over palatoscopy. Hence, cheiloscopy along with the canine odontometrics aid in sex determination and can be considered as an ancilliary forensic tool in identification.

  16. Personality traits and types predict medical school stress: a six-year longitudinal and nationwide study.

    Science.gov (United States)

    Tyssen, Reidar; Dolatowski, Filip C; Røvik, Jan Ole; Thorkildsen, Ruth F; Ekeberg, Oivind; Hem, Erlend; Gude, Tore; Grønvold, Nina T; Vaglum, Per

    2007-08-01

    Personality types (combinations of traits) that take into account the interplay between traits give a more detailed picture of an individual's character than do single traits. This study examines whether both personality types and traits predict stress during medical school training. We surveyed Norwegian medical students (n = 421) 1 month after they began medical school (T1), at the mid-point of undergraduate Year 3 (T2), and at the end of undergraduate Year 6 (T3). A total of 236 medical students (56%) responded at all time-points. They were categorised according to Torgersen's personality typology by their combination of high and low scores on the 'Big Three' personality traits of extroversion, neuroticism and conscientiousness. We studied the effects of both personality types (spectator, insecure, sceptic, brooder, hedonist, impulsive, entrepreneur and complicated) and traits on stress during medical school. There was a higher level of stress among female students. The traits of neuroticism (P = 0.002) and conscientiousness (P = 0.03) were independent predictors of stress, whereas female gender was absorbed by neuroticism in the multivariate model. When controlled for age and gender, 'brooders' (low extroversion, high neuroticism, high conscientiousness) were at risk of experiencing more stress (P = 0.02), whereas 'hedonists' (high extroversion, low neuroticism, low conscientiousness) were more protected against stress (P = 0.001). This is the first study to show that a specific combination of personality traits can predict medical school stress. The combination of high neuroticism and high conscientiousness is considered to be particularly high risk.

  17. Autonomic and Adrenocortical Interactions Predict Mental Health in Late Adolescence: The TRAILS Study.

    Science.gov (United States)

    Nederhof, Esther; Marceau, Kristine; Shirtcliff, Elizabeth A; Hastings, Paul D; Oldehinkel, Albertine J

    2015-07-01

    The present study is informed by the theory of allostatic load to examine how multiple stress responsive biomarkers are related to mental health outcomes. Data are from the TRAILS study, a large prospective population study of 715 Dutch adolescents (50.9 % girls), assessed at 16.3 and 19.1 years. Reactivity measures of the hypothalamic pituitary-adrenal (HPA) axis and autonomic nervous system (ANS) biomarkers (heart rate, HR; respiratory sinus arrhythmia, RSA; and pre-ejection period, PEP) to a social stress task were used to predict concurrent and longitudinal changes in internalizing and externalizing symptoms. Hierarchical linear modeling revealed relatively few single effects for each biomarker with the exception that high HR reactivity predicted concurrent internalizing problems in boys. More interestingly, interactions were found between HPA-axis reactivity and sympathetic and parasympathetic reactivity. Boys with high HPA reactivity and low RSA reactivity had the largest increases in internalizing problems from 16 to 19 years. Youth with low HPA reactivity along with increased ANS activation characterized by both decreases in RSA and decreases in PEP had the most concurrent externalizing problems, consistent with broad theories of hypo-arousal. Youth with high HPA reactivity along with increases in RSA but decreases in PEP also had elevated concurrent externalizing problems, which increased over time, especially within boys. This profile illustrates the utility of examining the parasympathetic and sympathetic components of the ANS which can act in opposition to one another to achieve, overall, stress responsivity. The framework of allostasis and allostatic load is supported in that examination of multiple biomarkers working together in concert was of value in understanding mental health problems concurrently and longitudinally. Findings argue against an additive panel of risk and instead illustrate the dynamic interplay of stress physiology systems.

  18. First Trimester Urine and Serum Metabolomics for Prediction of Preeclampsia and Gestational Hypertension: A Prospective Screening Study

    Directory of Open Access Journals (Sweden)

    Marie Austdal

    2015-09-01

    Full Text Available Hypertensive disorders of pregnancy, including preeclampsia, are major contributors to maternal morbidity. The goal of this study was to evaluate the potential of metabolomics to predict preeclampsia and gestational hypertension from urine and serum samples in early pregnancy, and elucidate the metabolic changes related to the diseases. Metabolic profiles were obtained by nuclear magnetic resonance spectroscopy of serum and urine samples from 599 women at medium to high risk of preeclampsia (nulliparous or previous preeclampsia/gestational hypertension. Preeclampsia developed in 26 (4.3% and gestational hypertension in 21 (3.5% women. Multivariate analyses of the metabolic profiles were performed to establish prediction models for the hypertensive disorders individually and combined. Urinary metabolomic profiles predicted preeclampsia and gestational hypertension at 51.3% and 40% sensitivity, respectively, at 10% false positive rate, with hippurate as the most important metabolite for the prediction. Serum metabolomic profiles predicted preeclampsia and gestational hypertension at 15% and 33% sensitivity, respectively, with increased lipid levels and an atherogenic lipid profile as most important for the prediction. Combining maternal characteristics with the urinary hippurate/creatinine level improved the prediction rates of preeclampsia in a logistic regression model. The study indicates a potential future role of clinical importance for metabolomic analysis of urine in prediction of preeclampsia.

  19. First Trimester Urine and Serum Metabolomics for Prediction of Preeclampsia and Gestational Hypertension: A Prospective Screening Study.

    Science.gov (United States)

    Austdal, Marie; Tangerås, Line H; Skråstad, Ragnhild B; Salvesen, Kjell; Austgulen, Rigmor; Iversen, Ann-Charlotte; Bathen, Tone F

    2015-09-08

    Hypertensive disorders of pregnancy, including preeclampsia, are major contributors to maternal morbidity. The goal of this study was to evaluate the potential of metabolomics to predict preeclampsia and gestational hypertension from urine and serum samples in early pregnancy, and elucidate the metabolic changes related to the diseases. Metabolic profiles were obtained by nuclear magnetic resonance spectroscopy of serum and urine samples from 599 women at medium to high risk of preeclampsia (nulliparous or previous preeclampsia/gestational hypertension). Preeclampsia developed in 26 (4.3%) and gestational hypertension in 21 (3.5%) women. Multivariate analyses of the metabolic profiles were performed to establish prediction models for the hypertensive disorders individually and combined. Urinary metabolomic profiles predicted preeclampsia and gestational hypertension at 51.3% and 40% sensitivity, respectively, at 10% false positive rate, with hippurate as the most important metabolite for the prediction. Serum metabolomic profiles predicted preeclampsia and gestational hypertension at 15% and 33% sensitivity, respectively, with increased lipid levels and an atherogenic lipid profile as most important for the prediction. Combining maternal characteristics with the urinary hippurate/creatinine level improved the prediction rates of preeclampsia in a logistic regression model. The study indicates a potential future role of clinical importance for metabolomic analysis of urine in prediction of preeclampsia.

  20. Monthly to seasonal low flow prediction: statistical versus dynamical models

    Science.gov (United States)

    Ionita-Scholz, Monica; Klein, Bastian; Meissner, Dennis; Rademacher, Silke

    2016-04-01

    While the societal and economical impacts of floods are well documented and assessable, the impacts of lows flows are less studied and sometimes overlooked. For example, over the western part of Europe, due to intense inland waterway transportation, the economical loses due to low flows are often similar compared to the ones due to floods. In general, the low flow aspect has the tendency to be underestimated by the scientific community. One of the best examples in this respect is the facts that at European level most of the countries have an (early) flood alert system, but in many cases no real information regarding the development, evolution and impacts of droughts. Low flows, occurring during dry periods, may result in several types of problems to society and economy: e.g. lack of water for drinking, irrigation, industrial use and power production, deterioration of water quality, inland waterway transport, agriculture, tourism, issuing and renewing waste disposal permits, and for assessing the impact of prolonged drought on aquatic ecosystems. As such, the ever-increasing demand on water resources calls for better a management, understanding and prediction of the water deficit situation and for more reliable and extended studies regarding the evolution of the low flow situations. In order to find an optimized monthly to seasonal forecast procedure for the German waterways, the Federal Institute of Hydrology (BfG) is exploring multiple approaches at the moment. On the one hand, based on the operational short- to medium-range forecasting chain, existing hydrological models are forced with two different hydro-meteorological inputs: (i) resampled historical meteorology generated by the Ensemble Streamflow Prediction approach and (ii) ensemble (re-) forecasts of ECMWF's global coupled ocean-atmosphere general circulation model, which have to be downscaled and bias corrected before feeding the hydrological models. As a second approach BfG evaluates in cooperation with

  1. PREDICT-CP: study protocol of implementation of comprehensive surveillance to predict outcomes for school-aged children with cerebral palsy.

    Science.gov (United States)

    Boyd, Roslyn N; Davies, Peter Sw; Ziviani, Jenny; Trost, Stewart; Barber, Lee; Ware, Robert; Rose, Stephen; Whittingham, Koa; Sakzewski, Leanne; Bell, Kristie; Carty, Christopher; Obst, Steven; Benfer, Katherine; Reedman, Sarah; Edwards, Priya; Kentish, Megan; Copeland, Lisa; Weir, Kelly; Davenport, Camilla; Brooks, Denise; Coulthard, Alan; Pelekanos, Rebecca; Guzzetta, Andrea; Fiori, Simona; Wynter, Meredith; Finn, Christine; Burgess, Andrea; Morris, Kym; Walsh, John; Lloyd, Owen; Whitty, Jennifer A; Scuffham, Paul A

    2017-07-12

    Cerebral palsy (CP) remains the world's most common childhood physical disability with total annual costs of care and lost well-being of $A3.87b. The PREDICT-CP (NHMRC 1077257 Partnership Project: Comprehensive surveillance to PREDICT outcomes for school age children with CP) study will investigate the influence of brain structure, body composition, dietary intake, oropharyngeal function, habitual physical activity, musculoskeletal development (hip status, bone health) and muscle performance on motor attainment, cognition, executive function, communication, participation, quality of life and related health resource use costs. The PREDICT-CP cohort provides further follow-up at 8-12 years of two overlapping preschool-age cohorts examined from 1.5 to 5 years (NHMRC 465128 motor and brain development; NHMRC 569605 growth, nutrition and physical activity). This population-based cohort study undertakes state-wide surveillance of 245 children with CP born in Queensland (birth years 2006-2009). Children will be classified for Gross Motor Function Classification System; Manual Ability Classification System, Communication Function Classification System and Eating and Drinking Ability Classification System. Outcomes include gross motor function, musculoskeletal development (hip displacement, spasticity, muscle contracture), upper limb function, communication difficulties, oropharyngeal dysphagia, dietary intake and body composition, participation, parent-reported and child-reported quality of life and medical and allied health resource use. These detailed phenotypical data will be compared with brain macrostructure and microstructure using 3 Tesla MRI (3T MRI). Relationships between brain lesion severity and outcomes will be analysed using multilevel mixed-effects models. The PREDICT-CP protocol is a prospectively registered and ethically accepted study protocol. The study combines data at 1.5-5 then 8-12 years of direct clinical assessment to enable prediction of outcomes

  2. A simulation study of capacity utilization to predict future capacity for manufacturing system sustainability

    Science.gov (United States)

    Rimo, Tan Hauw Sen; Chai Tin, Ong

    2017-12-01

    Capacity utilization (CU) measurement is an important task in a manufacturing system, especially in make-to-order (MTO) type manufacturing system with product customization, in predicting capacity to meet future demand. A stochastic discrete-event simulation is developed using ARENA software to determine CU and capacity gap (CG) in short run production function. This study focused on machinery breakdown and product defective rate as random variables in the simulation. The study found that the manufacturing system run in 68.01% CU and 31.99% CG. It is revealed that machinery breakdown and product defective rate have a direct relationship with CU. By improving product defective rate into zero defect, manufacturing system can improve CU up to 73.56% and CG decrease to 26.44%. While improving machinery breakdown into zero breakdowns will improve CU up to 93.99% and the CG decrease to 6.01%. This study helps operation level to study CU using “what-if” analysis in order to meet future demand in more practical and easier method by using simulation approach. Further study is recommended by including other random variables that affect CU to make the simulation closer with the real-life situation for a better decision.

  3. Study for discharge coefficient of flow nozzles. Prediction by using numerical simulation

    International Nuclear Information System (INIS)

    Ikeda, Hiroshi; Sakai, Norio; Yamamoto, Yasushi; Arai, Kenji; Matsumoto, Masaaki

    2008-01-01

    In nuclear plant, as water feeding into reactor have much effect on thermal power of plant, it is important to measure accurately the flow rate of water. Flow nozzle is on of typical differential pressure type flow meters and the discharge coefficient is used to calculate the flow rate. This coefficient is given by actual experiment and theory. We studied the theoretical assumption of the discharge coefficient curve using numerical simulation and evaluated the effect of flow nozzle configuration on the coefficient numerically and experimentally. As the result, numerical simulation can predict the discharge coefficient of theoretical curve within 0.3%. And we found that the throat length and throat tapping location of flow nozzle have much effect on the coefficient. (author)

  4. A study on two phase flows of linear compressors for the prediction of refrigerant leakage

    International Nuclear Information System (INIS)

    Hwang, Il Sun; Lee, Young Lim; Oh, Won Sik; Park, Kyeong Bae

    2015-01-01

    Usage of linear compressors is on the rise due to their high efficiency. In this paper, leakage of a linear compressor has been studied through numerical analysis and experiments. First, nitrogen leakage for a stagnant piston with fixed cylinder pressure as well as for a moving piston with fixed cylinder pressure was analyzed to verify the validity of the two-phase flow analysis model. Next, refrigerant leakage of a linear compressor in operation was finally predicted through 3-dimensional unsteady, two phase flow CFD (Computational fluid dynamics). According to the research results, the numerical analyses for the fixed cylinder pressure models were in good agreement with the experimental results. The refrigerant leakage of the linear compressor in operation mainly occurred through the oil exit and the leakage became negligible after about 0.4s following operation where the leakage became lower than 2.0x10 -4 kg/s.

  5. Boolean Dynamic Modeling Approaches to Study Plant Gene Regulatory Networks: Integration, Validation, and Prediction.

    Science.gov (United States)

    Velderraín, José Dávila; Martínez-García, Juan Carlos; Álvarez-Buylla, Elena R

    2017-01-01

    Mathematical models based on dynamical systems theory are well-suited tools for the integration of available molecular experimental data into coherent frameworks in order to propose hypotheses about the cooperative regulatory mechanisms driving developmental processes. Computational analysis of the proposed models using well-established methods enables testing the hypotheses by contrasting predictions with observations. Within such framework, Boolean gene regulatory network dynamical models have been extensively used in modeling plant development. Boolean models are simple and intuitively appealing, ideal tools for collaborative efforts between theorists and experimentalists. In this chapter we present protocols used in our group for the study of diverse plant developmental processes. We focus on conceptual clarity and practical implementation, providing directions to the corresponding technical literature.

  6. QSAR and docking studies of anthraquinone derivatives by similarity cluster prediction.

    Science.gov (United States)

    Harsa, Alexandra M; Harsa, Teodora E; Diudea, Mircea V

    2016-01-01

    Forty anthraquinone derivatives have been downloaded from PubChem database and investigated in a quantitative structure-activity relationships (QSAR) study. The models describing log P and LD50 of this set were built up on the hypermolecule scheme that mimics the investigated receptor space; the models were validated by the leave-one-out procedure, in the external test set and in a new version of prediction by using similarity clusters. Molecular docking approach using Lamarckian Genetic Algorithm was made on this class of anthraquinones with respect to 3Q3B receptor. The best scored molecules in the docking assay were used as leaders in the similarity clustering procedure. It is demonstrated that the LD50 data of this set of anthraquinones are related to the binding energies of anthraquinone ligands to the 3Q3B receptor.

  7. A study of upwind schemes on the laminar hypersonic heating predictions for the reusable space vehicle

    Science.gov (United States)

    Qu, Feng; Sun, Di; Zuo, Guang

    2018-06-01

    With the rapid development of the Computational Fluid Dynamics (CFD), Accurate computing hypersonic heating is in a high demand for the design of the new generation reusable space vehicle to conduct deep space exploration. In the past years, most researchers try to solve this problem by concentrating on the choice of the upwind schemes or the definition of the cell Reynolds number. However, the cell Reynolds number dependencies and limiter dependencies of the upwind schemes, which are of great importance to their performances in hypersonic heating computations, are concerned by few people. In this paper, we conduct a systematic study on these properties respectively. Results in our test cases show that SLAU (Simple Low-dissipation AUSM-family) is with a much higher level of accuracy and robustness in hypersonic heating predictions. Also, it performs much better in terms of the limiter dependency and the cell Reynolds number dependency.

  8. A comparative study: classification vs. user-based collaborative filtering for clinical prediction

    Directory of Open Access Journals (Sweden)

    Fang Hao

    2016-12-01

    Full Text Available Abstract Background Recommender systems have shown tremendous value for the prediction of personalized item recommendations for individuals in a variety of settings (e.g., marketing, e-commerce, etc.. User-based collaborative filtering is a popular recommender system, which leverages an individuals’ prior satisfaction with items, as well as the satisfaction of individuals that are “similar”. Recently, there have been applications of collaborative filtering based recommender systems for clinical risk prediction. In these applications, individuals represent patients, and items represent clinical data, which includes an outcome. Methods Application of recommender systems to a problem of this type requires the recasting a supervised learning problem as unsupervised. The rationale is that patients with similar clinical features carry a similar disease risk. As the “Big Data” era progresses, it is likely that approaches of this type will be reached for as biomedical data continues to grow in both size and complexity (e.g., electronic health records. In the present study, we set out to understand and assess the performance of recommender systems in a controlled yet realistic setting. User-based collaborative filtering recommender systems are compared to logistic regression and random forests with different types of imputation and varying amounts of missingness on four different publicly available medical data sets: National Health and Nutrition Examination Survey (NHANES, 2011-2012 on Obesity, Study to Understand Prognoses Preferences Outcomes and Risks of Treatment (SUPPORT, chronic kidney disease, and dermatology data. We also examined performance using simulated data with observations that are Missing At Random (MAR or Missing Completely At Random (MCAR under various degrees of missingness and levels of class imbalance in the response variable. Results Our results demonstrate that user-based collaborative filtering is consistently inferior

  9. A comparative study: classification vs. user-based collaborative filtering for clinical prediction.

    Science.gov (United States)

    Hao, Fang; Blair, Rachael Hageman

    2016-12-08

    Recommender systems have shown tremendous value for the prediction of personalized item recommendations for individuals in a variety of settings (e.g., marketing, e-commerce, etc.). User-based collaborative filtering is a popular recommender system, which leverages an individuals' prior satisfaction with items, as well as the satisfaction of individuals that are "similar". Recently, there have been applications of collaborative filtering based recommender systems for clinical risk prediction. In these applications, individuals represent patients, and items represent clinical data, which includes an outcome. Application of recommender systems to a problem of this type requires the recasting a supervised learning problem as unsupervised. The rationale is that patients with similar clinical features carry a similar disease risk. As the "Big Data" era progresses, it is likely that approaches of this type will be reached for as biomedical data continues to grow in both size and complexity (e.g., electronic health records). In the present study, we set out to understand and assess the performance of recommender systems in a controlled yet realistic setting. User-based collaborative filtering recommender systems are compared to logistic regression and random forests with different types of imputation and varying amounts of missingness on four different publicly available medical data sets: National Health and Nutrition Examination Survey (NHANES, 2011-2012 on Obesity), Study to Understand Prognoses Preferences Outcomes and Risks of Treatment (SUPPORT), chronic kidney disease, and dermatology data. We also examined performance using simulated data with observations that are Missing At Random (MAR) or Missing Completely At Random (MCAR) under various degrees of missingness and levels of class imbalance in the response variable. Our results demonstrate that user-based collaborative filtering is consistently inferior to logistic regression and random forests with different

  10. Predictive risk modelling in the Spanish population: a cross-sectional study.

    Science.gov (United States)

    Orueta, Juan F; Nuño-Solinis, Roberto; Mateos, Maider; Vergara, Itziar; Grandes, Gonzalo; Esnaola, Santiago

    2013-07-09

    An increase in chronic conditions is currently the greatest threat to human health and to the sustainability of health systems. Risk adjustment systems may enable population stratification programmes to be developed and become instrumental in implementing new models of care.The objectives of this study are to evaluate the capability of ACG-PM, DCG-HCC and CRG-based models to predict healthcare costs and identify patients that will be high consumers and to analyse changes to predictive capacity when socio-economic variables are added. This cross-sectional study used data of all Basque Country citizens over 14 years of age (n = 1,964,337) collected in a period of 2 years. Data from the first 12 months (age, sex, area deprivation index, diagnoses, procedures, prescriptions and previous cost) were used to construct the explanatory variables. The ability of models to predict healthcare costs in the following 12 months was assessed using the coefficient of determination and to identify the patients with highest costs by means of receiver operating characteristic (ROC) curve analysis. The coefficients of determination ranged from 0.18 to 0.21 for diagnosis-based models, 0.17-0.18 for prescription-based and 0.21-0.24 for the combination of both. The observed area under the ROC curve was 0.78-0.86 (identifying patients with a cost higher than P-95) and 0.83-0.90 (P-99). The values of the DCG-HCC models are slightly higher and those of the CRG models are lower, although prescription information could not be used in the latter. On adding previous cost data, differences between the three systems decrease appreciably. Inclusion of the deprivation index led to only marginal improvements in explanatory power. The case-mix systems developed in the USA can be useful in a publicly financed healthcare system with universal coverage to identify people at risk of high health resource consumption and whose situation is potentially preventable through proactive interventions.

  11. Predictive models and spatial analysis for the study of deserted medieval villages in Basilicata Region (Italy)

    Science.gov (United States)

    Biscione, Marilisa; Danese, Maria; Masini, Nicola; Sabia, Canio

    2016-04-01

    The study is focused on villages that are abandoned throughout the Basilicata from the 13th to the 15th century (Masini 1998), which is an emblematic case of abandonment of settlements in Late Middle Ages, which was a very common phenomenon throughout the whole Europe, attracting the interest of several historians and archaeologists (Demians d'Archimbaud 2001) The aim of the present study is to offer a contribution to knowledge of the medieval Basilicata's landscapes and settlement's dynamics with a multidisciplinary approach, derived from the rescue archeology: we have integrated the documentary sources with the use of spatial analysis and predictive models (Danese et al. 2009). The preventive archeology was born to conciliate the protection of archeological heritage, in evidence and potential, with the needs of urban design and planning. It is of fundamental importance, for a reliable evaluation of archaeological potential (identifying invisible traces) to use innovative diagnostic technologies: geophysical prospections, remote sensing (Lasaponara & Masini 2010; Lasaponara et al. 2016) and spatial analysis for the creation of predictive models. The latter are used to accomplish operational purposes but also for the historical landscape reconstruction (Danese et al. 2013; 2014). They contribute to analyse settlements and their dynamics on the basis of definite method and parameters. Thanks to predictive models it is possible, in fact, to start off by information of well-known archeological sites and use this knowledge as an empiric test for understand which elements have influenced their localization in the space. The relationships among natural environment, social context and position site are analysed in order to make clear the rules of settlement. These rules are then used into the model (Podobnikar et al. 2001). In this work the employed methodology is Spatial Analysis, in order to subdivide the territory based on its importance respect to a given function

  12. The predictive value of ePAQ in the urodynamic diagnoses-A prospective cohort study.

    Science.gov (United States)

    McCooty, Shanteela; Nightingale, Peter; Latthe, Pallavi

    2018-01-01

    To assess whether the electronic Personal Assessment Questionnaire-Pelvic Floor (ePAQ-PF) had accuracy in predicting the urodynamic diagnoses of Detrusor Overactivity (DO) and/or Urodynamic Stress Incontinence (USI). Tertiary urogynaecology unit linked to an academic university teaching hospital. Consecutive women who presented with lower urinary tract symptoms (LUTS) and were booked to have urodynamic studies. Women completed an ePAQ-PF prior to having urodynamics (UDS) by clinicians who were blinded to the ePAQ-PF results while conducting this procedure. Receiver Operating Characteristics (ROC) curves were constructed for predictive accuracy of overactive bladder (OAB) score in DO and of stress urinary incontinence (SUI) score in USI. Prospective cohort study designed to meet the requirements of the standards for reporting of diagnostic accuracy (STARD). 390 women with a mean age of 54.2 (range 21-92) years were recruited. The majority (n = 294; 75%) were White Caucasian and had two children (n = 157; 40.3%). Of them, 67.2% (n = 262) had DO and USI was confirmed in 21.5% (n = 84). The area under the ROC curve for DO was 0.704 (95% confidence interval 0.650-0.759) and for USI it was 0.731 (95% confidence interval 0.652-0.778). The OAB and SUI scores on the ePAQ-PF demonstrated that they are fair predictors in diagnosing DO and USI. As the OAB and SUI score on ePAQ-PF increased so did the likelihood of DO (up to a score of 75) and USI on UDS. © 2017 Wiley Periodicals, Inc.

  13. Handgrip strength predicts functional decline at discharge in hospitalized male elderly: a hospital cohort study.

    Directory of Open Access Journals (Sweden)

    Carmen García-Peña

    Full Text Available Functional decline after hospitalization is a common adverse outcome in elderly. An easy to use, reproducible and accurate tool to identify those at risk would aid focusing interventions in those at higher risk. Handgrip strength has been shown to predict adverse outcomes in other settings. The aim of this study was to determine if handgrip strength measured upon admission to an acute care facility would predict functional decline (either incident or worsening of preexisting at discharge among older Mexican, stratified by gender. In addition, cutoff points as a function of specificity would be determined. A cohort study was conducted in two hospitals in Mexico City. The primary endpoint was functional decline on discharge, defined as a 30-point reduction in the Barthel Index score from that of the baseline score. Handgrip strength along with other variables was measured at initial assessment, including: instrumental activities of daily living, cognition, depressive symptoms, delirium, hospitalization length and quality of life. All analyses were stratified by gender. Logistic regression to test independent association between handgrip strength and functional decline was performed, along with estimation of handgrip strength test values (specificity, sensitivity, area under the curve, etc.. A total of 223 patients admitted to an acute care facility between 2007 and 2009 were recruited. A total of 55 patients (24.7% had functional decline, 23.46% in male and 25.6% in women. Multivariate analysis showed that only males with low handgrip strength had an increased risk of functional decline at discharge (OR 0.88, 95% CI 0.79-0.98, p = 0.01, with a specificity of 91.3% and a cutoff point of 20.65 kg for handgrip strength. Females had not a significant association between handgrip strength and functional decline. Measurement of handgrip strength on admission to acute care facilities may identify male elderly patients at risk of having functional decline

  14. International scale implementation of the CNOSSOS-EU road traffic noise prediction model for epidemiological studies

    International Nuclear Information System (INIS)

    Morley, D.W.; Hoogh, K. de; Fecht, D.; Fabbri, F.; Bell, M.; Goodman, P.S.; Elliott, P.; Hodgson, S.; Hansell, A.L.; Gulliver, J.

    2015-01-01

    The EU-FP7-funded BioSHaRE project is using individual-level data pooled from several national cohort studies in Europe to investigate the relationship of road traffic noise and health. The detailed input data (land cover and traffic characteristics) required for noise exposure modelling are not always available over whole countries while data that are comparable in spatial resolution between different countries is needed for harmonised exposure assessment. Here, we assess the feasibility using the CNOSSOS-EU road traffic noise prediction model with coarser input data in terms of model performance. Starting with a model using the highest resolution datasets, we progressively introduced lower resolution data over five further model runs and compared noise level estimates to measurements. We conclude that a low resolution noise model should provide adequate performance for exposure ranking (Spearman's rank = 0.75; p < 0.001), but with relatively large errors in predicted noise levels (RMSE = 4.46 dB(A)). - Highlights: • The first implementation of CNOSSOS-EU for national scale noise exposure assessment. • Road traffic noise model performance with varying resolution of inputs is assessed. • Model performance is good with low resolution inputs (r_s = 0.75). • This model will be applied in epidemiological studies of European cohorts. - The CNOSSOS-EU road traffic noise model estimates can be used for international scale exposure assessment when parameterised with freely available low resolution covering a large geographic area.

  15. Effect of elevated lithium on the waterside corrosion of zircaloy-4: Experimental and predictive studies

    International Nuclear Information System (INIS)

    Pecheur, D.; Giordano, A.; Picard, E.; Billot, P.; Thomazet, J.

    1997-01-01

    Lithium and boron content in the coolant are known to influence the oxidation behaviour of the fuel cladding. Since new PWR operating conditions could consist in an increase of the lithium and the boron concentration in the coolant early in the cycle, a specific study has been conducted to analyze and to predict the effect of such new water chemistry conditions on the oxidation kinetics of the Zircaloy-4 material. Experimental studies have been performed in out-of-pile loop tests, under one and two phase flow heat transfer in various water chemistry conditions (0≤Li≤350 ppm, 0≤B≤1000 ppm, 0≤K≤56 ppm). A simulation of the effect of elevated lithium on the corrosion has been made using the semi-empirical COCHISE corrosion code. Under one phase flow heat transfer conditions, the addition of lithium hydroxide in the coolant increases the oxidation rate, essentially in the post-transition regime for low lithium levels (≤ 75 ppm) and immediately in the pre-transition phase for very high lithium level (350 ppm). Under two phase flow heat transfer, an enhancement of the corrosion is observed in the area of the rod submitted to boiling. Based on the out-of-pile loop test performed in presence of KOH instead of LiOH, such an enhancement of the corrosion appears to be due to a lithium enrichment in the oxide layer induced by boiling and not to a pH effect. The simulation of the increase of lithium content in the coolant from 2.2 to 3.5 ppm leads to an enhancement in corrosion rates which becomes only significant at high burn up. This predictive result of elevated lithium effect on corrosion is then compared with oxidation data derived from reactors operating under an elevated lithium regime. (author). 14 refs, 9 figs, 3 tabs

  16. Investigating the relationship between predictability and imbalance in minimisation: a simulation study

    Science.gov (United States)

    2013-01-01

    Background The use of restricted randomisation methods such as minimisation is increasing. This paper investigates under what conditions it is preferable to use restricted randomisation in order to achieve balance between treatment groups at baseline with regard to important prognostic factors and whether trialists should be concerned that minimisation may be considered deterministic. Methods Using minimisation as the randomisation algorithm, treatment allocation was simulated for hypothetical patients entering a theoretical study having values for prognostic factors randomly assigned with a stipulated probability. The number of times the allocation could have been determined with certainty and the imbalances which might occur following randomisation using minimisation were examined. Results Overall treatment balance is relatively unaffected by reducing the probability of allocation to optimal treatment group (P) but within-variable balance can be affected by any P <1. This effect is magnified by increased numbers of prognostic variables, the number of categories within them and the prevalence of these categories within the study population. Conclusions In general, for smaller trials, probability of treatment allocation to the treatment group with fewer numbers requires a larger value P to keep treatment and variable groups balanced. For larger trials probability of allocation values from P = 0.5 to P = 0.8 can be used while still maintaining balance. For one prognostic variable there is no significant benefit in terms of predictability in reducing the value of P. However, for more than one prognostic variable, significant reduction in levels of predictability can be achieved with the appropriate choice of P for the given trial design. PMID:23537389

  17. Does organized sport participation during youth predict healthy habits in adulthood? A 28-year longitudinal study.

    Science.gov (United States)

    Palomäki, S; Hirvensalo, M; Smith, K; Raitakari, O; Männistö, S; Hutri-Kähönen, N; Tammelin, T

    2018-04-26

    Health behaviors in youth can predict the same behaviors later in life, but the role of sport participation in predicting healthy lifestyle habits is unclear. This study aimed to investigate the association between participation in organized youth sport and adult healthy lifestyle habits. Data from the longitudinal Cardiovascular Risk in Young Finns Study (YFS) with a 28-year follow-up were used. The participation in sport-club training sessions was self-reported by 9-18-year-olds in 1983 and 1986 (n = 1285). During 2011, participants (aged 37-43-year old) reported their smoking status, alcohol consumption, fruit and vegetable consumption, and physical activity. Odd ratios (OR) were calculated using logistic regression, to examine how participation in organized youth sport was associated with having three or four versus fewer (0-2) healthy habits in adulthood. Participants who were active in youth sport in both 1983 and 1986 had almost two times greater odds of having three or four healthy habits in adulthood than those who were not active at both time points (OR: 1.75, 95%CI: 1.11-2.76). When the analyses were stratified by sex, the findings were statistically significant among women (OR: 2.13, 95%Cl: 1.13-3.99) but not men (OR: 1.27, 95%CI: 0.63-2.58). The results suggest that participation in organized youth sport could promote healthy lifestyle choices. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  18. Effect of elevated lithium on the waterside corrosion of zircaloy-4: Experimental and predictive studies

    Energy Technology Data Exchange (ETDEWEB)

    Pecheur, D; Giordano, A; Picard, E; Billot, P [CEA Centre d` Etudes de Cadarache, 13 - Saint-Paul-lez-Durance (France); Thomazet, J [FRAMATOME, Nuclear Fuel Div., Lyon (France)

    1997-02-01

    Lithium and boron content in the coolant are known to influence the oxidation behaviour of the fuel cladding. Since new PWR operating conditions could consist in an increase of the lithium and the boron concentration in the coolant early in the cycle, a specific study has been conducted to analyze and to predict the effect of such new water chemistry conditions on the oxidation kinetics of the Zircaloy-4 material. Experimental studies have been performed in out-of-pile loop tests, under one and two phase flow heat transfer in various water chemistry conditions (0{<=}Li{<=}350 ppm, 0{<=}B{<=}1000 ppm, 0{<=}K{<=}56 ppm). A simulation of the effect of elevated lithium on the corrosion has been made using the semi-empirical COCHISE corrosion code. Under one phase flow heat transfer conditions, the addition of lithium hydroxide in the coolant increases the oxidation rate, essentially in the post-transition regime for low lithium levels ({<=} 75 ppm) and immediately in the pre-transition phase for very high lithium level (350 ppm). Under two phase flow heat transfer, an enhancement of the corrosion is observed in the area of the rod submitted to boiling. Based on the out-of-pile loop test performed in presence of KOH instead of LiOH, such an enhancement of the corrosion appears to be due to a lithium enrichment in the oxide layer induced by boiling and not to a pH effect. The simulation of the increase of lithium content in the coolant from 2.2 to 3.5 ppm leads to an enhancement in corrosion rates which becomes only significant at high burn up. This predictive result of elevated lithium effect on corrosion is then compared with oxidation data derived from reactors operating under an elevated lithium regime. (author). 14 refs, 9 figs, 3 tabs.

  19. Handgrip Strength Predicts Functional Decline at Discharge in Hospitalized Male Elderly: A Hospital Cohort Study

    Science.gov (United States)

    García-Peña, Carmen; García-Fabela, Luis C.; Gutiérrez-Robledo, Luis M.; García-González, Jose J.; Arango-Lopera, Victoria E.; Pérez-Zepeda, Mario U.

    2013-01-01

    Functional decline after hospitalization is a common adverse outcome in elderly. An easy to use, reproducible and accurate tool to identify those at risk would aid focusing interventions in those at higher risk. Handgrip strength has been shown to predict adverse outcomes in other settings. The aim of this study was to determine if handgrip strength measured upon admission to an acute care facility would predict functional decline (either incident or worsening of preexisting) at discharge among older Mexican, stratified by gender. In addition, cutoff points as a function of specificity would be determined. A cohort study was conducted in two hospitals in Mexico City. The primary endpoint was functional decline on discharge, defined as a 30-point reduction in the Barthel Index score from that of the baseline score. Handgrip strength along with other variables was measured at initial assessment, including: instrumental activities of daily living, cognition, depressive symptoms, delirium, hospitalization length and quality of life. All analyses were stratified by gender. Logistic regression to test independent association between handgrip strength and functional decline was performed, along with estimation of handgrip strength test values (specificity, sensitivity, area under the curve, etc.). A total of 223 patients admitted to an acute care facility between 2007 and 2009 were recruited. A total of 55 patients (24.7%) had functional decline, 23.46% in male and 25.6% in women. Multivariate analysis showed that only males with low handgrip strength had an increased risk of functional decline at discharge (OR 0.88, 95% CI 0.79–0.98, p = 0.01), with a specificity of 91.3% and a cutoff point of 20.65 kg for handgrip strength. Females had not a significant association between handgrip strength and functional decline. Measurement of handgrip strength on admission to acute care facilities may identify male elderly patients at risk of having functional decline, and

  20. Nuclear grade and necrosis predict prognosis in malignant epithelioid pleural mesothelioma: a multi-institutional study.

    Science.gov (United States)

    Rosen, Lauren E; Karrison, Theodore; Ananthanarayanan, Vijayalakshmi; Gallan, Alexander J; Adusumilli, Prasad S; Alchami, Fouad S; Attanoos, Richard; Brcic, Luka; Butnor, Kelly J; Galateau-Sallé, Françoise; Hiroshima, Kenzo; Kadota, Kyuichi; Klampatsa, Astero; Stang, Nolween Le; Lindenmann, Joerg; Litzky, Leslie A; Marchevsky, Alberto; Medeiros, Filomena; Montero, M Angeles; Moore, David A; Nabeshima, Kazuki; Pavlisko, Elizabeth N; Roggli, Victor L; Sauter, Jennifer L; Sharma, Anupama; Sheaff, Michael; Travis, William D; Vigneswaran, Wickii T; Vrugt, Bart; Walts, Ann E; Tjota, Melissa Y; Krausz, Thomas; Husain, Aliya N

    2018-04-01

    A recently described nuclear grading system predicted survival in patients with epithelioid malignant pleural mesothelioma. The current study was undertaken to validate the grading system and to identify additional prognostic factors. We analyzed cases of epithelioid malignant pleural mesothelioma from 17 institutions across the globe from 1998 to 2014. Nuclear grade was computed combining nuclear atypia and mitotic count into a grade of I-III using the published system. Nuclear grade was assessed by one pathologist for three institutions, the remaining were scored independently. The presence or absence of necrosis and predominant growth pattern were also evaluated. Two additional scoring systems were evaluated, one combining nuclear grade and necrosis and the other mitotic count and necrosis. Median overall survival was the primary endpoint. A total of 776 cases were identified including 301 (39%) nuclear grade I tumors, 354 (45%) grade II tumors and 121 (16%) grade III tumors. The overall survival was 16 months, and correlated independently with age (P=0.006), sex (0.015), necrosis (0.030), mitotic count (0.001), nuclear atypia (0.009), nuclear grade (<0.0001), and mitosis and necrosis score (<0.0001). The addition of necrosis to nuclear grade further stratified overall survival, allowing classification of epithelioid malignant pleural mesothelioma into four distinct prognostic groups: nuclear grade I tumors without necrosis (29 months), nuclear grade I tumors with necrosis and grade II tumors without necrosis (16 months), nuclear grade II tumors with necrosis (10 months) and nuclear grade III tumors (8 months). The mitosis-necrosis score stratified patients by survival, but not as well as the combination of necrosis and nuclear grade. This study confirms that nuclear grade predicts survival in epithelioid malignant pleural mesothelioma, identifies necrosis as factor that further stratifies overall survival, and validates the grading system across multiple

  1. Risk prediction models for selection of lung cancer screening candidates: A retrospective validation study.

    Directory of Open Access Journals (Sweden)

    Kevin Ten Haaf

    2017-04-01

    slightly higher specificity for some models. The PLCOm2012, Bach, and Two-Stage Clonal Expansion incidence models had the best overall performance, with AUCs >0.68 in the NLST and >0.77 in the PLCO. These three models had the highest sensitivity and specificity for predicting 6-y lung cancer incidence in the PLCO chest radiography arm, with sensitivities >79.8% and specificities >62.3%. In contrast, the NLST eligibility criteria yielded a sensitivity of 71.4% and a specificity of 62.2%. Limitations of this study include the lack of identification of optimal risk thresholds, as this requires additional information on the long-term benefits (e.g., life-years gained and mortality reduction and harms (e.g., overdiagnosis of risk-based screening strategies using these models. In addition, information on some predictor variables included in the risk prediction models was not available.Selection of individuals for lung cancer screening using individual risk is superior to selection criteria based on age and pack-years alone. The benefits, harms, and feasibility of implementing lung cancer screening policies based on risk prediction models should be assessed and compared with those of current recommendations.

  2. Real-Time Prediction of Gamers Behavior Using Variable Order Markov and Big Data Technology: A Case of Study

    Directory of Open Access Journals (Sweden)

    Alejandro Baldominos Gómez

    2016-03-01

    Full Text Available This paper presents the results and conclusions found when predicting the behavior of gamers in commercial videogames datasets. In particular, it uses Variable-Order Markov (VOM to build a probabilistic model that is able to use the historic behavior of gamers and to infer what will be their next actions. Being able to predict with accuracy the next user’s actions can be of special interest to learn from the behavior of gamers, to make them more engaged and to reduce churn rate. In order to support a big volume and velocity of data, the system is built on top of the Hadoop ecosystem, using HBase for real-time processing; and the prediction tool is provided as a service (SaaS and accessible through a RESTful API. The prediction system is evaluated using a case of study with two commercial videogames, attaining promising results with high prediction accuracies.

  3. Factors predicting emotional cue-responding behaviors of nurses in Taiwan: An observational study.

    Science.gov (United States)

    Lin, Mei-Feng; Lee, An-Yu; Chou, Cheng-Chen; Liu, Tien-Yu; Tang, Chia-Chun

    2017-10-01

    Responding to emotional cues is an essential element of therapeutic communication. The purpose of this study is to examine nurses' competence of responding to emotional cues (CRE) and related factors while interacting with standardized patients with cancer. This is an exploratory and predictive correlational study. A convenience sample of registered nurses who have passed the probationary period in southern Taiwan was recruited to participate in 15-minute videotaped interviews with standardized patients. The Medical Interview Aural Rating Scale was used to describe standardized patients' emotional cues and to measure nurses' CRE. The State-Trait Anxiety Inventory was used to evaluate nurses' anxiety level before the conversation. We used descriptive statistics to describe the data and stepwise regression to examine the predictors of nurses' CRE. A total of 110 nurses participated in the study. Regardless of the emotional cue level, participants predominately responded to cues with inappropriate distancing strategies. Prior formal communication training, practice unit, length of nursing practice, and educational level together explain 36.3% variances of the nurses' CRE. This study is the first to explore factors related to Taiwanese nurses' CRE. Compared to nurses in other countries, Taiwanese nurses tended to respond to patients' emotional cues with more inappropriate strategies. We also identified significant predictors of CRE that show the importance of communication training. Future research and education programs are needed to enhance nurses' CRE and to advocate for emotion-focused communication. Copyright © 2016 John Wiley & Sons, Ltd.

  4. A predictive Bayesian approach to the design and analysis of bridging studies.

    Science.gov (United States)

    Gould, A Lawrence; Jin, Tian; Zhang, Li Xin; Wang, William W B

    2012-09-01

    Pharmaceutical product development culminates in confirmatory trials whose evidence for the product's efficacy and safety supports regulatory approval for marketing. Regulatory agencies in countries whose patients were not included in the confirmatory trials often require confirmation of efficacy and safety in their patient populations, which may be accomplished by carrying out bridging studies to establish consistency for local patients of the effects demonstrated by the original trials. This article describes and illustrates an approach for designing and analyzing bridging studies that fully incorporates the information provided by the original trials. The approach determines probability contours or regions of joint predictive intervals for treatment effect and response variability, or endpoints of treatment effect confidence intervals, that are functions of the findings from the original trials, the sample sizes for the bridging studies, and possible deviations from complete consistency with the original trials. The bridging studies are judged consistent with the original trials if their findings fall within the probability contours or regions. Regulatory considerations determine the region definitions and appropriate probability levels. Producer and consumer risks provide a way to assess alternative region and probability choices. [Supplemental materials are available for this article. Go to the Publisher's online edition of the Journal of Biopharmaceutical Statistics for the following free supplemental resource: Appendix 2: R code for Calculations.].

  5. Predicting mental disorders from hypothalamic-pituitary-adrenal axis functioning: a 3-year follow-up in the TRAILS study.

    Science.gov (United States)

    Nederhof, E; van Oort, F V A; Bouma, E M C; Laceulle, O M; Oldehinkel, A J; Ormel, J

    2015-08-01

    Hypothalamic-pituitary-adrenal axis functioning, with cortisol as its major output hormone, has been presumed to play a key role in the development of psychopathology. Predicting affective disorders from diurnal cortisol levels has been inconclusive, whereas the predictive value of stress-induced cortisol concentrations has not been studied before. The aim of this study was to predict mental disorders over a 3-year follow-up from awakening and stress-induced cortisol concentrations. Data were used from 561 TRAILS (TRacking Adolescents' Individual Lives Survey) participants, a prospective cohort study of Dutch adolescents. Saliva samples were collected at awakening and half an hour later and during a social stress test at age 16. Mental disorders were assessed 3 years later with the Composite International Diagnostic Interview (CIDI). A lower cortisol awakening response (CAR) marginally significantly predicted new disorders [odds ratio (OR) 0.77, p = 0.06]. A flat recovery slope predicted disorders with a first onset after the experimental session (OR 1.27, p = 0.04). Recovery revealed smaller, non-significant ORs when predicting new onset affective or anxiety disorders, major depressive disorder, or dependence disorders in three separate models, corrected for all other new onsets. Our results suggest that delayed recovery and possibly reduced CAR are indicators of a more general risk status and may be part of a common pathway to psychopathology. Delayed recovery suggests that individuals at risk for mental disorders perceived the social stress test as less controllable and less predictable.

  6. Prediction of disease and phenotype associations from genome-wide association studies.

    Directory of Open Access Journals (Sweden)

    Stephanie N Lewis

    Full Text Available Genome wide association studies (GWAS have proven useful as a method for identifying genetic variations associated with diseases. In this study, we analyzed GWAS data for 61 diseases and phenotypes to elucidate common associations based on single nucleotide polymorphisms (SNP. The study was an expansion on a previous study on identifying disease associations via data from a single GWAS on seven diseases.Adjustments to the originally reported study included expansion of the SNP dataset using Linkage Disequilibrium (LD and refinement of the four levels of analysis to encompass SNP, SNP block, gene, and pathway level comparisons. A pair-wise comparison between diseases and phenotypes was performed at each level and the Jaccard similarity index was used to measure the degree of association between two diseases/phenotypes. Disease relatedness networks (DRNs were used to visualize our results. We saw predominant relatedness between Multiple Sclerosis, type 1 diabetes, and rheumatoid arthritis for the first three levels of analysis. Expected relatedness was also seen between lipid- and blood-related traits.The predominant associations between Multiple Sclerosis, type 1 diabetes, and rheumatoid arthritis can be validated by clinical studies. The diseases have been proposed to share a systemic inflammation phenotype that can result in progression of additional diseases in patients with one of these three diseases. We also noticed unexpected relationships between metabolic and neurological diseases at the pathway comparison level. The less significant relationships found between diseases require a more detailed literature review to determine validity of the predictions. The results from this study serve as a first step towards a better understanding of seemingly unrelated diseases and phenotypes with similar symptoms or modes of treatment.

  7. Predictability, Work-Family Conflict, and Intent to Stay: An Air Force Case Study

    National Research Council Canada - National Science Library

    Obruba, Patrick

    2001-01-01

    A survey was completed by 362 active duty Air Force members in December 2000 regarding their perceptions of schedule predictability, work-family conflict, job satisfaction, organizational commitment...

  8. Predicting the evolution of large cholera outbreaks: lessons learnt from the Haiti case study

    Science.gov (United States)

    Bertuzzo, Enrico; Mari, Lorenzo; Righetto, Lorenzo; Knox, Allyn; Finger, Flavio; Casagrandi, Renato; Gatto, Marino; Rodriguez-Iturbe, Ignacio; Rinaldo, Andrea

    2013-04-01

    Mathematical models can provide key insights into the course of an ongoing epidemic, potentially aiding real-time emergency management in allocating health care resources and possibly anticipating the impact of alternative interventions. Spatially explicit models of waterborne disease are made routinely possible by widespread data mapping of hydrology, road network, population distribution, and sanitation. Here, we study the ex-post reliability of predictions of the ongoing Haiti cholera outbreak. Our model consists of a set of dynamical equations (SIR-like, i.e. subdivided into the compartments of Susceptible, Infected and Recovered individuals) describing a connected network of human communities where the infection results from the exposure to excess concentrations of pathogens in the water, which are, in turn, driven by hydrologic transport through waterways and by mobility of susceptible and infected individuals. Following the evidence of a clear correlation between rainfall events and cholera resurgence, we test a new mechanism explicitly accounting for rainfall as a driver of enhanced disease transmission by washout of open-air defecation sites or cesspool overflows. A general model for Haitian epidemic cholera and the related uncertainty is thus proposed and applied to the dataset of reported cases now available. The model allows us to draw predictions on longer-term epidemic cholera in Haiti from multi-season Monte Carlo runs, carried out up to January 2014 by using a multivariate Poisson rainfall generator, with parameters varying in space and time. Lessons learned and open issues are discussed and placed in perspective. We conclude that, despite differences in methods that can be tested through model-guided field validation, mathematical modeling of large-scale outbreaks emerges as an essential component of future cholera epidemic control.

  9. Simplified versus geometrically accurate models of forefoot anatomy to predict plantar pressures: A finite element study.

    Science.gov (United States)

    Telfer, Scott; Erdemir, Ahmet; Woodburn, James; Cavanagh, Peter R

    2016-01-25

    Integration of patient-specific biomechanical measurements into the design of therapeutic footwear has been shown to improve clinical outcomes in patients with diabetic foot disease. The addition of numerical simulations intended to optimise intervention design may help to build on these advances, however at present the time and labour required to generate and run personalised models of foot anatomy restrict their routine clinical utility. In this study we developed second-generation personalised simple finite element (FE) models of the forefoot with varying geometric fidelities. Plantar pressure predictions from barefoot, shod, and shod with insole simulations using simplified models were compared to those obtained from CT-based FE models incorporating more detailed representations of bone and tissue geometry. A simplified model including representations of metatarsals based on simple geometric shapes, embedded within a contoured soft tissue block with outer geometry acquired from a 3D surface scan was found to provide pressure predictions closest to the more complex model, with mean differences of 13.3kPa (SD 13.4), 12.52kPa (SD 11.9) and 9.6kPa (SD 9.3) for barefoot, shod, and insole conditions respectively. The simplified model design could be produced in 3h in the case of the more detailed model, and solved on average 24% faster. FE models of the forefoot based on simplified geometric representations of the metatarsal bones and soft tissue surface geometry from 3D surface scans may potentially provide a simulation approach with improved clinical utility, however further validity testing around a range of therapeutic footwear types is required. Copyright © 2015 Elsevier Ltd. All rights reserved.

  10. Performance of FHWA model for predicting traffic noise: a case study of metropolitan city, Lucknow (India

    Directory of Open Access Journals (Sweden)

    J. B. Srivastava

    2009-09-01

    Full Text Available Industrial and transport activities are the two major sources of noise pollution in any metropolitan city. Lucknow city, the capital of the largest populated state Uttar Pradesh in India has an area of 310 sq. km and is rapidly growing as a commercial, industrial and trading centre of northern India. The population of Lucknow city as per census 2001 is 22.45 Lacs. It is expected that by the year 2021 it will make 45 Lacs. The total vehicle population in Lucknow city on 31 March 2008, was nearly 1 million with almost 80% two wheelers, 12% cars, 1.36% three wheelers, 0.45% buses etc. A study was carried out to assess the existing status of noise levels and its impacts on the environment with a possibility of further expansion of the city. Ambient noise levels were measured at different locations selected on the basis of land use such as silence, heavy traffic and residential and commercial zones. It was found that noise levels at all selected locations were much higher (75–90 dB than the prescribed limits. The observed traffic volume and data on road geometry were used to predict noise levels using Federal Highway Administration Agency (FHWA model and the calculated noise levels were compared with the observed levels for checking the suitability of this model for predicting the future levels. It was established that the results obtained by FHWA model were very close to the observed noise levels and that the model was suitable to be used for other similar metropolitan cities in India.

  11. Evidence That a Psychopathology Interactome Has Diagnostic Value, Predicting Clinical Needs: An Experience Sampling Study

    Science.gov (United States)

    van Os, Jim; Lataster, Tineke; Delespaul, Philippe; Wichers, Marieke; Myin-Germeys, Inez

    2014-01-01

    Background For the purpose of diagnosis, psychopathology can be represented as categories of mental disorder, symptom dimensions or symptom networks. Also, psychopathology can be assessed at different levels of temporal resolution (monthly episodes, daily fluctuating symptoms, momentary fluctuating mental states). We tested the diagnostic value, in terms of prediction of treatment needs, of the combination of symptom networks and momentary assessment level. Method Fifty-seven patients with a psychotic disorder participated in an ESM study, capturing psychotic experiences, emotions and circumstances at 10 semi-random moments in the flow of daily life over a period of 6 days. Symptoms were assessed by interview with the Positive and Negative Syndrome Scale (PANSS); treatment needs were assessed using the Camberwell Assessment of Need (CAN). Results Psychotic symptoms assessed with the PANSS (Clinical Psychotic Symptoms) were strongly associated with psychotic experiences assessed with ESM (Momentary Psychotic Experiences). However, the degree to which Momentary Psychotic Experiences manifested as Clinical Psychotic Symptoms was determined by level of momentary negative affect (higher levels increasing probability of Momentary Psychotic Experiences manifesting as Clinical Psychotic Symptoms), momentary positive affect (higher levels decreasing probability of Clinical Psychotic Symptoms), greater persistence of Momentary Psychotic Experiences (persistence predicting increased probability of Clinical Psychotic Symptoms) and momentary environmental stress associated with events and activities (higher levels increasing probability of Clinical Psychotic Symptoms). Similarly, the degree to which momentary visual or auditory hallucinations manifested as Clinical Psychotic Symptoms was strongly contingent on the level of accompanying momentary paranoid delusional ideation. Momentary Psychotic Experiences were associated with CAN unmet treatment needs, over and above PANSS

  12. A generic analytical foot rollover model for predicting translational ankle kinematics in gait simulation studies.

    Science.gov (United States)

    Ren, Lei; Howard, David; Ren, Luquan; Nester, Chris; Tian, Limei

    2010-01-19

    The objective of this paper is to develop an analytical framework to representing the ankle-foot kinematics by modelling the foot as a rollover rocker, which cannot only be used as a generic tool for general gait simulation but also allows for case-specific modelling if required. Previously, the rollover models used in gait simulation have often been based on specific functions that have usually been of a simple form. In contrast, the analytical model described here is in a general form that the effective foot rollover shape can be represented by any polar function rho=rho(phi). Furthermore, a normalized generic foot rollover model has been established based on a normative foot rollover shape dataset of 12 normal healthy subjects. To evaluate model accuracy, the predicted ankle motions and the centre of pressure (CoP) were compared with measurement data for both subject-specific and general cases. The results demonstrated that the ankle joint motions in both vertical and horizontal directions (relative RMSE approximately 10%) and Co