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Sample records for modeling daily precipitationat

  1. Downscaler Model for predicting daily air pollution

    EPA Pesticide Factsheets

    This model combines daily ozone and particulate matter monitoring and modeling data from across the U.S. to provide improved fine-scale estimates of air quality in communities and other specific locales.

  2. Stochastic daily modeling of arctic tundra ecosystems

    NASA Astrophysics Data System (ADS)

    Erler, A.; Epstein, H. E.; Frazier, J.

    2011-12-01

    ArcVeg is a dynamic vegetation model that has simulated interannual variability of production and abundance of arctic tundra plant types in previous studies. In order to address the effects of changing seasonality on tundra plant community composition and productivity, we have uniquely adapted the model to operate on the daily timescale. Each section of the model-weather generation, nitrogen mineralization, and plant growth dynamics-are driven by daily fluctuations in simulated temperature conditions. These simulation dynamics are achieved by calibrating stochastic iterative loops and mathematical functions with raw field data. Air temperature is the fundamental driver in the model, parameterized by climate data collected in the field across numerous arctic tundra sites, and key daily statistics are extracted (mean and standard deviation of temperature for each day of the year). Nitrogen mineralization is calculated as an exponential function from the simulated temperature. The seasonality of plant growth is driven by the availability of nitrogen and constrained by historical patterns and dynamics of the remotely sensed normalized difference vegetation index (NDVI), as they pertain to the seasonal onset of growth. Here we describe the methods used for daily weather generation, nitrogen mineralization, and the daily competition among twelve plant functional types for nitrogen and subsequent growth. This still rather simple approach to vegetation dynamics has the capacity to generate complex relationships between seasonal patterns of temperature and arctic tundra vegetation community structure and function.

  3. Modelling erosion on a daily basis

    NASA Astrophysics Data System (ADS)

    Pikha Shrestha, Dhruba; Jetten, Victor

    2016-04-01

    Effect of soil erosion causing negative impact on ecosystem services and food security is well known. To assess annual erosion rates various empirical models have been extensively used in all the climatic regions. While these models are simple to operate and do not require lot of input data, the effect of extreme rain is not taken into account in the annual estimations. For analysing the effects of extreme rain the event- based models become handy. These models can simulate detail erosional processes including particle detachment, transportation and deposition of sediments during a storm. But they are not applicable for estimating annual erosion rates. Moreover storm event data may not be available everywhere which prohibits their extensive use. In this paper we describe a method by adapting the revised MMF model to assess erosion on daily basis so that the effects of extreme rains are taken into account. We couple it to a simple surface soil moisture balance on a daily basis and include estimation of daily vegetation cover changes. Annual soil loss is calculated by adding daily erosion rates. We compare the obtained results with that obtained from applying the revised MMF model in a case study in the Mamora plateau in northwest Morocco which is affected by severe gully formation. The results show clearly the effects of exceptional rain in erosional processes which cannot be captured in an annual model.

  4. Global daily reference evapotranspiration modeling and evaluation

    USGS Publications Warehouse

    Senay, G.B.; Verdin, J.P.; Lietzow, R.; Melesse, Assefa M.

    2008-01-01

    Accurate and reliable evapotranspiration (ET) datasets are crucial in regional water and energy balance studies. Due to the complex instrumentation requirements, actual ET values are generally estimated from reference ET values by adjustment factors using coefficients for water stress and vegetation conditions, commonly referred to as crop coefficients. Until recently, the modeling of reference ET has been solely based on important weather variables collected from weather stations that are generally located in selected agro-climatic locations. Since 2001, the National Oceanic and Atmospheric Administration’s Global Data Assimilation System (GDAS) has been producing six-hourly climate parameter datasets that are used to calculate daily reference ET for the whole globe at 1-degree spatial resolution. The U.S. Geological Survey Center for Earth Resources Observation and Science has been producing daily reference ET (ETo) since 2001, and it has been used on a variety of operational hydrological models for drought and streamflow monitoring all over the world. With the increasing availability of local station-based reference ET estimates, we evaluated the GDAS-based reference ET estimates using data from the California Irrigation Management Information System (CIMIS). Daily CIMIS reference ET estimates from 85 stations were compared with GDAS-based reference ET at different spatial and temporal scales using five-year daily data from 2002 through 2006. Despite the large difference in spatial scale (point vs. ∼100 km grid cell) between the two datasets, the correlations between station-based ET and GDAS-ET were very high, exceeding 0.97 on a daily basis to more than 0.99 on time scales of more than 10 days. Both the temporal and spatial correspondences in trend/pattern and magnitudes between the two datasets were satisfactory, suggesting the reliability of using GDAS parameter-based reference ET for regional water and energy balance studies in many parts of the world

  5. Modeling maximum daily temperature using a varying coefficient regression model

    NASA Astrophysics Data System (ADS)

    Li, Han; Deng, Xinwei; Kim, Dong-Yun; Smith, Eric P.

    2014-04-01

    Relationships between stream water and air temperatures are often modeled using linear or nonlinear regression methods. Despite a strong relationship between water and air temperatures and a variety of models that are effective for data summarized on a weekly basis, such models did not yield consistently good predictions for summaries such as daily maximum temperature. A good predictive model for daily maximum temperature is required because daily maximum temperature is an important measure for predicting survival of temperature sensitive fish. To appropriately model the strong relationship between water and air temperatures at a daily time step, it is important to incorporate information related to the time of the year into the modeling. In this work, a time-varying coefficient model is used to study the relationship between air temperature and water temperature. The time-varying coefficient model enables dynamic modeling of the relationship, and can be used to understand how the air-water temperature relationship varies over time. The proposed model is applied to 10 streams in Maryland, West Virginia, Virginia, North Carolina, and Georgia using daily maximum temperatures. It provides a better fit and better predictions than those produced by a simple linear regression model or a nonlinear logistic model.

  6. Modeling maximum daily temperature using a varying coefficient regression model

    Treesearch

    Han Li; Xinwei Deng; Dong-Yum Kim; Eric P. Smith

    2014-01-01

    Relationships between stream water and air temperatures are often modeled using linear or nonlinear regression methods. Despite a strong relationship between water and air temperatures and a variety of models that are effective for data summarized on a weekly basis, such models did not yield consistently good predictions for summaries such as daily maximum temperature...

  7. Time series ARIMA models for daily price of palm oil

    NASA Astrophysics Data System (ADS)

    Ariff, Noratiqah Mohd; Zamhawari, Nor Hashimah; Bakar, Mohd Aftar Abu

    2015-02-01

    Palm oil is deemed as one of the most important commodity that forms the economic backbone of Malaysia. Modeling and forecasting the daily price of palm oil is of great interest for Malaysia's economic growth. In this study, time series ARIMA models are used to fit the daily price of palm oil. The Akaike Infromation Criterion (AIC), Akaike Infromation Criterion with a correction for finite sample sizes (AICc) and Bayesian Information Criterion (BIC) are used to compare between different ARIMA models being considered. It is found that ARIMA(1,2,1) model is suitable for daily price of crude palm oil in Malaysia for the year 2010 to 2012.

  8. Models for estimating daily rainfall erosivity in China

    NASA Astrophysics Data System (ADS)

    Xie, Yun; Yin, Shui-qing; Liu, Bao-yuan; Nearing, Mark A.; Zhao, Ying

    2016-04-01

    The rainfall erosivity factor (R) represents the multiplication of rainfall energy and maximum 30 min intensity by event (EI30) and year. This rainfall erosivity index is widely used for empirical soil loss prediction. Its calculation, however, requires high temporal resolution rainfall data that are not readily available in many parts of the world. The purpose of this study was to parameterize models suitable for estimating erosivity from daily rainfall data, which are more widely available. One-minute resolution rainfall data recorded in sixteen stations over the eastern water erosion impacted regions of China were analyzed. The R-factor ranged from 781.9 to 8258.5 MJ mm ha-1 h-1 y-1. A total of 5942 erosive events from one-minute resolution rainfall data of ten stations were used to parameterize three models, and 4949 erosive events from the other six stations were used for validation. A threshold of daily rainfall between days classified as erosive and non-erosive was suggested to be 9.7 mm based on these data. Two of the models (I and II) used power law functions that required only daily rainfall totals. Model I used different model coefficients in the cool season (Oct.-Apr.) and warm season (May-Sept.), and Model II was fitted with a sinusoidal curve of seasonal variation. Both Model I and Model II estimated the erosivity index for average annual, yearly, and half-month temporal scales reasonably well, with the symmetric mean absolute percentage error MAPEsym ranging from 10.8% to 32.1%. Model II predicted slightly better than Model I. However, the prediction efficiency for the daily erosivity index was limited, with the symmetric mean absolute percentage error being 68.0% (Model I) and 65.7% (Model II) and Nash-Sutcliffe model efficiency being 0.55 (Model I) and 0.57 (Model II). Model III, which used the combination of daily rainfall amount and daily maximum 60-min rainfall, improved predictions significantly, and produced a Nash-Sutcliffe model efficiency

  9. Daily total global solar radiation modeling from several meteorological data

    NASA Astrophysics Data System (ADS)

    Bilgili, Mehmet; Ozgoren, Muammer

    2011-05-01

    This paper investigates the modeling of the daily total global solar radiation in Adana city of Turkey using multi-linear regression (MLR), multi-nonlinear regression (MNLR) and feed-forward artificial neural network (ANN) methods. Several daily meteorological data, i.e., measured sunshine duration, air temperature and wind speed and date of the year, i.e., monthly and daily, were used as independent variables to the MLR, MNLR and ANN models. In order to determine the relationship between the total global solar radiation and other meteorological data, and also to obtain the best independent variables, the MLR and MNLR analyses were performed with the "Stepwise" method in the Statistical Packages for the Social Sciences (SPSS) program. Thus, various models consisting of the combination of the independent variables were constructed and the best input structure was investigated. The performances of all models in the training and testing data sets were compared with the measured daily global solar radiation values. The obtained results indicated that the ANN method was better than the other methods in modeling daily total global solar radiation. For the ANN model, mean absolute error (MAE), mean absolute percentage error (MAPE), correlation coefficient ( R) and coefficient of determination ( R 2) for the training/testing data set were found to be 0.89/1.00 MJ/m2 day, 7.88/9.23%, 0.9824/0.9751, and 0.9651/0.9508, respectively.

  10. Stochastic daily precipitation model with a heavy-tailed component

    NASA Astrophysics Data System (ADS)

    Neykov, N. M.; Neytchev, P. N.; Zucchini, W.

    2014-09-01

    Stochastic daily precipitation models are commonly used to generate scenarios of climate variability or change on a daily timescale. The standard models consist of two components describing the occurrence and intensity series, respectively. Binary logistic regression is used to fit the occurrence data, and the intensity series is modeled using a continuous-valued right-skewed distribution, such as gamma, Weibull or lognormal. The precipitation series is then modeled using the joint density, and standard software for generalized linear models can be used to perform the computations. A drawback of these precipitation models is that they do not produce a sufficiently heavy upper tail for the distribution of daily precipitation amounts; they tend to underestimate the frequency of large storms. In this study, we adapted the approach of Furrer and Katz (2008) based on hybrid distributions in order to correct for this shortcoming. In particular, we applied hybrid gamma-generalized Pareto (GP) and hybrid Weibull-GP distributions to develop a stochastic precipitation model for daily rainfall at Ihtiman in western Bulgaria. We report the results of simulations designed to compare the models based on the hybrid distributions and those based on the standard distributions. Some potential difficulties are outlined.

  11. Stochastic daily precipitation model with a heavy-tailed component

    NASA Astrophysics Data System (ADS)

    Neykov, N. M.; Neytchev, P. N.; Zucchini, W.

    2014-02-01

    Stochastic daily precipitation models are commonly used to generate scenarios of climate variability or change on a daily time scale. The standard models consist of two components describing the occurrence and intensity series, respectively. Binary logistic regression is used to fit the occurrence data, and the intensity series is modeled by a continuous-valued right-skewed distribution, such as gamma, Weibull or lognormal. The precipitation series is then modeled using the joint density and standard software for generalized linear models can be used to perform the computations. A drawback of these precipitation models is that they do not produce a sufficiently heavy upper tail for the distribution of daily precipitation amounts; they tend to underestimate the frequency of large storms. In this study we adapted the approach of Furrer and Katz (2008) based on hybrid distributions in order to correct for this shortcoming. In particular we applied hybrid gamma - generalized Pareto (GP) and hybrid Weibull-GP distributions to develop a stochastic precipitation model for daily rainfall at Ihtiman in western Bulgaria. We report the results of simulations designed to compare the models based on the hybrid distributions and those based on the standard distributions. Some potential difficulties are outlined.

  12. Statistical procedures for evaluating daily and monthly hydrologic model predictions

    USGS Publications Warehouse

    Coffey, M.E.; Workman, S.R.; Taraba, J.L.; Fogle, A.W.

    2004-01-01

    The overall study objective was to evaluate the applicability of different qualitative and quantitative methods for comparing daily and monthly SWAT computer model hydrologic streamflow predictions to observed data, and to recommend statistical methods for use in future model evaluations. Statistical methods were tested using daily streamflows and monthly equivalent runoff depths. The statistical techniques included linear regression, Nash-Sutcliffe efficiency, nonparametric tests, t-test, objective functions, autocorrelation, and cross-correlation. None of the methods specifically applied to the non-normal distribution and dependence between data points for the daily predicted and observed data. Of the tested methods, median objective functions, sign test, autocorrelation, and cross-correlation were most applicable for the daily data. The robust coefficient of determination (CD*) and robust modeling efficiency (EF*) objective functions were the preferred methods for daily model results due to the ease of comparing these values with a fixed ideal reference value of one. Predicted and observed monthly totals were more normally distributed, and there was less dependence between individual monthly totals than was observed for the corresponding predicted and observed daily values. More statistical methods were available for comparing SWAT model-predicted and observed monthly totals. The 1995 monthly SWAT model predictions and observed data had a regression Rr2 of 0.70, a Nash-Sutcliffe efficiency of 0.41, and the t-test failed to reject the equal data means hypothesis. The Nash-Sutcliffe coefficient and the R r2 coefficient were the preferred methods for monthly results due to the ability to compare these coefficients to a set ideal value of one.

  13. Daily Flow Model of the Delaware River Basin. Appendices.

    DTIC Science & Technology

    1981-09-01

    AD-AlIO 113 CAP DRESSLER AND MCKEE INC ANNANDALE VA F/6 13/ 2 DAILY FLOW MODEL OF THE DELAWARE RIVER BASIN. APPENDICES.(U) SEP 81 DACW6178-C-0127...DELAWARE RIVER LAJ I I. I "" 01 22 02 046 APPENDICES IBOOK 2 of 2 September 1981 ..REPORT t4’+ bA6 Af- 5 ..O I Z o 1 - ~ ~ ~ ~ ~ ~ ~ o l kN...KEY FOR SERIES OF DOCUMENTATION MAIN REPORT (Book 1 of 2 ) VOLUME I - Phase I Report for Development ot a Daily Flow Model of the Delaware River

  14. Coupling SWAT and ANN models for enhanced daily streamflow prediction

    NASA Astrophysics Data System (ADS)

    Noori, Navideh; Kalin, Latif

    2016-02-01

    To improve daily flow prediction in unmonitored watersheds a hybrid model was developed by combining a quasi-distributed watershed model and artificial neural network (ANN). Daily streamflow data from 29 nearby watersheds in and around the city of Atlanta, Southeastern United States, with leave-one-site-out jackknifing technique were used to build the flow predictive models during warm and cool seasons. Daily streamflow was first simulated with the Soil and Water Assessment Tool (SWAT) and then the SWAT simulated baseflow and stormflow were used as inputs to ANN. Out of the total 29 test watersheds, 62% and 83% of them had Nash-Sutcliffe values above 0.50 during the cool and warm seasons, respectively (considered good or better). As the percent forest cover or the size of test watershed increased, the performances of the models gradually decreased during both warm and cool seasons. This indicates that the developed models work better in urbanized watersheds. In addition, SWAT and SWAT Calibration Uncertainty Procedure (SWAT-CUP) program were run separately for each station to compare the flow prediction accuracy of the hybrid approach to SWAT. Only 31% of the sites during the calibration and 34% of validation runs had ENASH values ⩾0.50. This study showed that coupling ANN with semi-distributed models can lead to improved daily streamflow predictions in ungauged watersheds.

  15. A stochastic model for the analysis of maximum daily temperature

    NASA Astrophysics Data System (ADS)

    Sirangelo, B.; Caloiero, T.; Coscarelli, R.; Ferrari, E.

    2016-08-01

    In this paper, a stochastic model for the analysis of the daily maximum temperature is proposed. First, a deseasonalization procedure based on the truncated Fourier expansion is adopted. Then, the Johnson transformation functions were applied for the data normalization. Finally, the fractionally autoregressive integrated moving average model was used to reproduce both short- and long-memory behavior of the temperature series. The model was applied to the data of the Cosenza gauge (Calabria region) and verified on other four gauges of southern Italy. Through a Monte Carlo simulation procedure based on the proposed model, 105 years of daily maximum temperature have been generated. Among the possible applications of the model, the occurrence probabilities of the annual maximum values have been evaluated. Moreover, the procedure was applied for the estimation of the return periods of long sequences of days with maximum temperature above prefixed thresholds.

  16. Models of Deafness: Cochlear Implants in the Australian Daily Press

    ERIC Educational Resources Information Center

    Power, Des

    2005-01-01

    This article examined a database of Australian daily newspapers on the terms cochlear implant and deaf children to investigate how journalists and columnists report competing models of deafness: as either "medical" (deafness is a condition to be cured) or "sociocultural" (deafness provides a way of life to be lived). The…

  17. A Hidden Markov Model of Daily Precipitation over Western Colombia.

    NASA Astrophysics Data System (ADS)

    Rojo Hernández, Julián; Lall, Upmanu; Mesa Sanchez, Oscar

    2017-04-01

    A Hidden Markov Model of Daily Precipitation over Western Colombia. The western Pacific coast of Colombia (Chocó Region) is among the rainiest on earth, largely due to low level jet activity and orographic lifting along the western Andes. A hidden Markov model (HMM) is used to characterize daily rainfall occurrence at 250 gauge stations over the Western Pacific coast and Andean plateau in Colombia during the wet season (September - November) from 1970 to 2015. Four ''hidden'' rainfall states are identified, with the first pair representing wet and dry conditions at all stations, and the second pair North-West to South-East gradients in rainfall occurrence. Using the ERA-Interim reanalysis data (1979-2012) we show that the first pair of states are associated with low level jet convergence and divergence, while the second pair is associated with South Atlantic Convergence Zone activity and local convection. The estimated daily state-sequence is characterized by a systematic seasonal evolution, together with considerable variability on intraseasonal and interannual time scales, exhibiting a strong relationship with ENSO. Finally, a nonhomogeneous HMM (NHMM) is then used to simulate daily precipitation occurrence at the 250 stations, using the ERA-Interim vertically integrated moisture flux anomalies (two weeks lagged) and monthly means of the sea surface temperatures (one month lagged). Simulations from the NHMM are found to reproduce the relationship between the ENSO and the western Colombian precipitation. The NHMM simulations are also able to capture interannual changes in daily rainfall occurrence and dry-wet frequencies at some individual stations. It is suggested that a) HMM provides a useful tool that contributes to characterizing the Colombian's Hydro-Meteorology and it's anomalies during the ENSO, and b) the NHMM is an important tool to produce station-scale daily rainfall sequence scenarios for input into hydrological models.

  18. A fuzzy-autoregressive model of daily river flows

    NASA Astrophysics Data System (ADS)

    Greco, Roberto

    2012-06-01

    A model for the identification of daily river flows has been developed, consisting of the combination of an autoregressive model with a fuzzy inference system. The AR model is devoted to the identification of base flow, supposed to be described by linear laws. The fuzzy model identifies the surface runoff, by applying a small set of linguistic statements, deriving from the knowledge of the physical features of the nonlinear rainfall-runoff transformation, to the inflow entering the river basin. The model has been applied to the identification of the daily flow series of river Volturno at Cancello-Arnone (Southern Italy), with a drainage basin of around 5560 km2, observed between 1970 and 1974. The inflow was estimated on the basis of daily precipitations registered during the same years at six rain gauges located throughout the basin. The first two years were used for model training, the remaining three for the validation. The obtained results show that the proposed model provides good predictions of either low river flows or high floods, although the analysis of residuals, which do not turn out to be a white noise, indicates that the cause and effect relationship between rainfall and runoff is not completely identified by the model.

  19. A fuzzy-autoregressive model of daily river flows

    NASA Astrophysics Data System (ADS)

    Greco, R.

    2012-04-01

    A model for the identification of daily river flows has been developed, consisting of the combination of an autoregressive model with a fuzzy inference system. The AR model is devoted to the identification of base flow, supposed to be described by linear laws. The fuzzy model identifies the surface runoff, by applying a small set of linguistic statements, deriving from the knowledge of the physical features of the non linear rainfall-runoff transformation, to the inflow entering the river basin. The model has been applied to the identification of the daily flow series of river Volturno at Cancello-Arnone (Southern Italy), with a drainage basin of around 5560 km2, observed between 1970 and 1974. The inflow was estimated on the basis of daily precipitations registered during the same years at six rain gauges located throughout the basin. The first two years were used for model training, the remaining three for the validation. The obtained results show that the proposed model provides good predictions of either low river flows or high floods, although the analysis of residuals, which do not turn out to be a white noise, indicates that the cause and effect relationship between rainfall and runoff is not completely identified by the model.

  20. Bayesian multinomial probit modeling of daily windows of ...

    EPA Pesticide Factsheets

    Past epidemiologic studies suggest maternal ambient air pollution exposure during critical periods of the pregnancy is associated with fetal development. We introduce a multinomial probit model that allows for the joint identification of susceptible daily periods during the pregnancy for 12 individual types of CHDs with respect to maternal PM2.5 exposure. We apply the model to a dataset of mothers from the National Birth Defect Prevention Study where daily PM2.5 exposures from weeks 2-8 of pregnancy are assigned (specific to each location and pregnancy date) using predictions from the downscaler pollution model. Results are compared to an aggregated exposure model which defines exposure as the average value over pregnancy weeks 2-8. Increased PM2.5 exposure during pregnancy days 53 and 50-51 for pulmonary valve stenosis and tetralogy of Fallot, respectively, are associated with an increased probability of development of each CHD. The largest estimated effect is seen for atrioventricular septal defects on pregnancy day 14. The aggregated exposure model fails to identify any significant windows of susceptibility during pregnancy weeks 2-8 for the considered CHDs. Considering daily PM2.5 exposures in a new modeling framework revealed positive associations for defects that the standard aggregated exposure model was unable to identify. Disclaimer: The views expressed in this manuscript are those of the authors and do not necessarily represent the views or policie

  1. Reconstruction of missing daily streamflow data using dynamic regression models

    NASA Astrophysics Data System (ADS)

    Tencaliec, Patricia; Favre, Anne-Catherine; Prieur, Clémentine; Mathevet, Thibault

    2015-12-01

    River discharge is one of the most important quantities in hydrology. It provides fundamental records for water resources management and climate change monitoring. Even very short data-gaps in this information can cause extremely different analysis outputs. Therefore, reconstructing missing data of incomplete data sets is an important step regarding the performance of the environmental models, engineering, and research applications, thus it presents a great challenge. The objective of this paper is to introduce an effective technique for reconstructing missing daily discharge data when one has access to only daily streamflow data. The proposed procedure uses a combination of regression and autoregressive integrated moving average models (ARIMA) called dynamic regression model. This model uses the linear relationship between neighbor and correlated stations and then adjusts the residual term by fitting an ARIMA structure. Application of the model to eight daily streamflow data for the Durance river watershed showed that the model yields reliable estimates for the missing data in the time series. Simulation studies were also conducted to evaluate the performance of the procedure.

  2. An empirical model of the quiet daily geomagnetic field variation

    USGS Publications Warehouse

    Yamazaki, Y.; Yumoto, K.; Cardinal, M.G.; Fraser, B.J.; Hattori, P.; Kakinami, Y.; Liu, J.Y.; Lynn, K.J.W.; Marshall, R.; McNamara, D.; Nagatsuma, T.; Nikiforov, V.M.; Otadoy, R.E.; Ruhimat, M.; Shevtsov, B.M.; Shiokawa, K.; Abe, S.; Uozumi, T.; Yoshikawa, A.

    2011-01-01

    An empirical model of the quiet daily geomagnetic field variation has been constructed based on geomagnetic data obtained from 21 stations along the 210 Magnetic Meridian of the Circum-pan Pacific Magnetometer Network (CPMN) from 1996 to 2007. Using the least squares fitting method for geomagnetically quiet days (Kp ??? 2+), the quiet daily geomagnetic field variation at each station was described as a function of solar activity SA, day of year DOY, lunar age LA, and local time LT. After interpolation in latitude, the model can describe solar-activity dependence and seasonal dependence of solar quiet daily variations (S) and lunar quiet daily variations (L). We performed a spherical harmonic analysis (SHA) on these S and L variations to examine average characteristics of the equivalent external current systems. We found three particularly noteworthy results. First, the total current intensity of the S current system is largely controlled by solar activity while its focus position is not significantly affected by solar activity. Second, we found that seasonal variations of the S current intensity exhibit north-south asymmetry; the current intensity of the northern vortex shows a prominent annual variation while the southern vortex shows a clear semi-annual variation as well as annual variation. Thirdly, we found that the total intensity of the L current system changes depending on solar activity and season; seasonal variations of the L current intensity show an enhancement during the December solstice, independent of the level of solar activity. Copyright 2011 by the American Geophysical Union.

  3. Modeling Errors in Daily Precipitation Measurements: Additive or Multiplicative?

    NASA Technical Reports Server (NTRS)

    Tian, Yudong; Huffman, George J.; Adler, Robert F.; Tang, Ling; Sapiano, Matthew; Maggioni, Viviana; Wu, Huan

    2013-01-01

    The definition and quantification of uncertainty depend on the error model used. For uncertainties in precipitation measurements, two types of error models have been widely adopted: the additive error model and the multiplicative error model. This leads to incompatible specifications of uncertainties and impedes intercomparison and application.In this letter, we assess the suitability of both models for satellite-based daily precipitation measurements in an effort to clarify the uncertainty representation. Three criteria were employed to evaluate the applicability of either model: (1) better separation of the systematic and random errors; (2) applicability to the large range of variability in daily precipitation; and (3) better predictive skills. It is found that the multiplicative error model is a much better choice under all three criteria. It extracted the systematic errors more cleanly, was more consistent with the large variability of precipitation measurements, and produced superior predictions of the error characteristics. The additive error model had several weaknesses, such as non constant variance resulting from systematic errors leaking into random errors, and the lack of prediction capability. Therefore, the multiplicative error model is a better choice.

  4. Retrospective forecast of ETAS model with daily parameters estimate

    NASA Astrophysics Data System (ADS)

    Falcone, Giuseppe; Murru, Maura; Console, Rodolfo; Marzocchi, Warner; Zhuang, Jiancang

    2016-04-01

    We present a retrospective ETAS (Epidemic Type of Aftershock Sequence) model based on the daily updating of free parameters during the background, the learning and the test phase of a seismic sequence. The idea was born after the 2011 Tohoku-Oki earthquake. The CSEP (Collaboratory for the Study of Earthquake Predictability) Center in Japan provided an appropriate testing benchmark for the five 1-day submitted models. Of all the models, only one was able to successfully predict the number of events that really happened. This result was verified using both the real time and the revised catalogs. The main cause of the failure was in the underestimation of the forecasted events, due to model parameters maintained fixed during the test. Moreover, the absence in the learning catalog of an event similar to the magnitude of the mainshock (M9.0), which drastically changed the seismicity in the area, made the learning parameters not suitable to describe the real seismicity. As an example of this methodological development we show the evolution of the model parameters during the last two strong seismic sequences in Italy: the 2009 L'Aquila and the 2012 Reggio Emilia episodes. The achievement of the model with daily updated parameters is compared with that of same model where the parameters remain fixed during the test time.

  5. Spatial and temporal modeling of daily pollen concentrations

    NASA Astrophysics Data System (ADS)

    Dellavalle, Curt T.; Triche, Elizabeth W.; Bell, Michelle L.

    2012-01-01

    Accurate assessments of pollen counts are valuable to allergy sufferers, the medical industry, and health researchers; however, monitoring stations do not exist in most areas. In addition, the degree of spatial reliability provided by the limited number of monitoring stations is poorly understood. We developed and compared spatial models to estimate pollen concentrations in locations without monitoring stations. Daily Acer, Quercus, and overall tree, grass, and weed pollen counts, in grains/m3, were obtained from 14 aeroallergen monitoring stations located in the northeastern and mid-Atlantic region of the United States from 2003 to 2006. Pollen counts were spatially interpolated using ordinary kriging. Mixed effects and generalized estimating equations incorporating daily and seasonal weather characteristics, pollen season characteristics and land-cover information were also developed to estimate daily pollen concentrations. We then compared observed values from a monitoring station to model estimates for that location. Observed counts and kriging estimates for tree pollen differed ( p = 0.04), but not when peak periods were removed ( p = 0.29). No differences between observed and kriging estimates of Acer ( p = 0.46), Quercus ( p = 0.24), grass ( p = 0.31) or weed pollen ( p = 0.29) were found. Estimates from longitudinal models also demonstrated good agreement with observed counts, except for the extremes of pollen distributions. Our results demonstrate that spatial interpolation techniques as well as regression methods incorporating both weather and land-cover characteristics can provide reliable estimates of daily pollen concentrations in areas where monitors do not exist for all but periods of extremely high pollen.

  6. a Multivariate Downscaling Model for Nonparametric Simulation of Daily Flows

    NASA Astrophysics Data System (ADS)

    Molina, J. M.; Ramirez, J. A.; Raff, D. A.

    2011-12-01

    A multivariate, stochastic nonparametric framework for stepwise disaggregation of seasonal runoff volumes to daily streamflow is presented. The downscaling process is conditional on volumes of spring runoff and large-scale ocean-atmosphere teleconnections and includes a two-level cascade scheme: seasonal-to-monthly disaggregation first followed by monthly-to-daily disaggregation. The non-parametric and assumption-free character of the framework allows consideration of the random nature and nonlinearities of daily flows, which parametric models are unable to account for adequately. This paper examines statistical links between decadal/interannual climatic variations in the Pacific Ocean and hydrologic variability in US northwest region, and includes a periodicity analysis of climate patterns to detect coherences of their cyclic behavior in the frequency domain. We explore the use of such relationships and selected signals (e.g., north Pacific gyre oscillation, southern oscillation, and Pacific decadal oscillation indices, NPGO, SOI and PDO, respectively) in the proposed data-driven framework by means of a combinatorial approach with the aim of simulating improved streamflow sequences when compared with disaggregated series generated from flows alone. A nearest neighbor time series bootstrapping approach is integrated with principal component analysis to resample from the empirical multivariate distribution. A volume-dependent scaling transformation is implemented to guarantee the summability condition. In addition, we present a new and simple algorithm, based on nonparametric resampling, that overcomes the common limitation of lack of preservation of historical correlation between daily flows across months. The downscaling framework presented here is parsimonious in parameters and model assumptions, does not generate negative values, and produces synthetic series that are statistically indistinguishable from the observations. We present evidence showing that both

  7. Statistical Modeling of Daily Stream Temperature for Mitigating Fish Mortality

    NASA Astrophysics Data System (ADS)

    Caldwell, R. J.; Rajagopalan, B.

    2011-12-01

    Water allocations in the Central Valley Project (CVP) of California require the consideration of short- and long-term needs of many socioeconomic factors including, but not limited to, agriculture, urban use, flood mitigation/control, and environmental concerns. The Endangered Species Act (ESA) ensures that the decision-making process provides sufficient water to limit the impact on protected species, such as salmon, in the Sacramento River Valley. Current decision support tools in the CVP were deemed inadequate by the National Marine Fisheries Service due to the limited temporal resolution of forecasts for monthly stream temperature and fish mortality. Finer scale temporal resolution is necessary to account for the stream temperature variations critical to salmon survival and reproduction. In addition, complementary, long-range tools are needed for monthly and seasonal management of water resources. We will present a Generalized Linear Model (GLM) framework of maximum daily stream temperatures and related attributes, such as: daily stream temperature range, exceedance/non-exceedance of critical threshold temperatures, and the number of hours of exceedance. A suite of predictors that impact stream temperatures are included in the models, including current and prior day values of streamflow, water temperatures of upstream releases from Shasta Dam, air temperature, and precipitation. Monthly models are developed for each stream temperature attribute at the Balls Ferry gauge, an EPA compliance point for meeting temperature criteria. The statistical framework is also coupled with seasonal climate forecasts using a stochastic weather generator to provide ensembles of stream temperature scenarios that can be used for seasonal scale water allocation planning and decisions. Short-term weather forecasts can also be used in the framework to provide near-term scenarios useful for making water release decisions on a daily basis. The framework can be easily translated to other

  8. Spatial interpolation schemes of daily precipitation for hydrologic modeling

    USGS Publications Warehouse

    Hwang, Y.; Clark, M.; Rajagopalan, B.; Leavesley, G.

    2012-01-01

    Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. We compare and contrast the performance of regression-based statistical methods for the spatial estimation of precipitation in two hydrologically different basins and confirmed that widely used regression-based estimation schemes fail to describe the realistic spatial variability of daily precipitation field. The methods assessed are: (1) inverse distance weighted average; (2) multiple linear regression (MLR); (3) climatological MLR; and (4) locally weighted polynomial regression (LWP). In order to improve the performance of the interpolations, the authors propose a two-step regression technique for effective daily precipitation estimation. In this simple two-step estimation process, precipitation occurrence is first generated via a logistic regression model before estimate the amount of precipitation separately on wet days. This process generated the precipitation occurrence, amount, and spatial correlation effectively. A distributed hydrologic model (PRMS) was used for the impact analysis in daily time step simulation. Multiple simulations suggested noticeable differences between the input alternatives generated by three different interpolation schemes. Differences are shown in overall simulation error against the observations, degree of explained variability, and seasonal volumes. Simulated streamflows also showed different characteristics in mean, maximum, minimum, and peak flows. Given the same parameter optimization technique, LWP input showed least streamflow error in Alapaha basin and CMLR input showed least error (still very close to LWP) in Animas basin. All of the two-step interpolation inputs resulted in lower streamflow error compared to the directly interpolated inputs. ?? 2011 Springer-Verlag.

  9. 10-daily soil erosion modelling over sub-Saharan Africa.

    PubMed

    Symeonakis, Elias; Drake, Nick

    2010-02-01

    Soil erosion is considered to be one of the greatest environmental problems of sub-Saharan Africa. This paper investigates the advantages and disadvantages of modelling soil erosion at the continental scale and suggests an operational methodology for mapping and quantifying 10-daily water runoff and soil erosion over this scale using remote sensing data in a geographical information system framework. An attempt is made to compare the estimates of this study with general data on the severity of soil erosion over Africa and with measured rates of soil loss at different locations over the continent. The results show that the measured and estimated rates of erosion are in some areas very similar and in general within the same order of magnitude. The importance and the potential of using the soil erosion estimates with simple models and easily accessible free data for various continental-scale environmental applications are also demonstrated.

  10. The modeling of daily precipitation in Costa Rica

    NASA Astrophysics Data System (ADS)

    Harrison, John Michael

    The understanding of precipitation and its underlying processes is important to many human activities. Agricultural planning, hydroelectric resource management, and industrial infrastructure development all rely heavily on being able to make reasonable predictions concerning rainfall. The lack of sufficient rainfall can have devastating social and economic consequences for developing nations that are reliant on subsistence agriculture and hydroelectric power. This study examines the means by which daily precipitation in Costa Rica can be modeled, and how the El Nino-Southern Oscillation (ENSO) affects the precipitation generating mechanisms. A selection of three meteorological stations are used to test how daily rainfall can be characterized by the occurrence and intensity of the individual rainfall events. The occurrence is modeled using a two-state first-order Markov model, which provides insight into the relative length of wet and dry spells. The intensity model uses L-moments to determine the optimum statistical distribution. These statistical parameters are used to understand the inter-annual and inter-seasonal variations in the precipitation-generating mechanisms as they are modified by the ENSO phenomenon. The parameters are also combined to create monthly rainfall simulations based on the state of the ENSO, as well as test whether accurate forecasts can be created up to one year in advance. It is found that the ENSO plays an important role in the daily rainfall process, by altering the behavior of the Inter-Tropical Convergence Zone (ITCZ), the Northeast Trade Winds, and the advance of cold air masses from North America during the winter. The eastern Caribbean slope of the country receives proportionally more rainfall during El Nino events, while the western Pacific slope receives less rainfall during the same period. Cold front (norte) intrusion is minimized by the El Nino, resulting in less winter rainfall during El Nino years. Simulations based on the

  11. Generalized linear model for estimation of missing daily rainfall data

    NASA Astrophysics Data System (ADS)

    Rahman, Nurul Aishah; Deni, Sayang Mohd; Ramli, Norazan Mohamed

    2017-04-01

    The analysis of rainfall data with no missingness is vital in various applications including climatological, hydrological and meteorological study. The issue of missing data is a serious concern since it could introduce bias and lead to misleading conclusions. In this study, five imputation methods including simple arithmetic average, normal ratio method, inverse distance weighting method, correlation coefficient weighting method and geographical coordinate were used to estimate the missing data. However, these imputation methods ignored the seasonality in rainfall dataset which could give more reliable estimation. Thus this study is aimed to estimate the missingness in daily rainfall data by using generalized linear model with gamma and Fourier series as the link function and smoothing technique, respectively. Forty years daily rainfall data for the period from 1975 until 2014 which consists of seven stations at Kelantan region were selected for the analysis. The findings indicated that the imputation methods could provide more accurate estimation values based on the least mean absolute error, root mean squared error and coefficient of variation root mean squared error when seasonality in the dataset are considered.

  12. Regional Model Nesting Within GFS Daily Forecasts Over West Africa

    NASA Technical Reports Server (NTRS)

    Druyan, Leonard M.; Fulakeza, Matthew; Lonergan, Patrick; Worrell, Ruben

    2010-01-01

    The study uses the RM3, the regional climate model at the Center for Climate Systems Research of Columbia University and the NASA/Goddard Institute for Space Studies (CCSR/GISS). The paper evaluates 30 48-hour RM3 weather forecasts over West Africa during September 2006 made on a 0.5 grid nested within 1 Global Forecast System (GFS) global forecasts. September 2006 was the Special Observing Period #3 of the African Monsoon Multidisciplinary Analysis (AMMA). Archived GFS initial conditions and lateral boundary conditions for the simulations from the US National Weather Service, National Oceanographic and Atmospheric Administration were interpolated four times daily. Results for precipitation forecasts are validated against Tropical Rainfall Measurement Mission (TRMM) satellite estimates and data from the Famine Early Warning System (FEWS), which includes rain gauge measurements, and forecasts of circulation are compared to reanalysis 2. Performance statistics for the precipitation forecasts include bias, root-mean-square errors and spatial correlation coefficients. The nested regional model forecasts are compared to GFS forecasts to gauge whether nesting provides additional realistic information. They are also compared to RM3 simulations driven by reanalysis 2, representing high potential skill forecasts, to gauge the sensitivity of results to lateral boundary conditions. Nested RM3/GFS forecasts generate excessive moisture advection toward West Africa, which in turn causes prodigious amounts of model precipitation. This problem is corrected by empirical adjustments in the preparation of lateral boundary conditions and initial conditions. The resulting modified simulations improve on the GFS precipitation forecasts, achieving time-space correlations with TRMM of 0.77 on the first day and 0.63 on the second day. One realtime RM3/GFS precipitation forecast made at and posted by the African Centre of Meteorological Application for Development (ACMAD) in Niamey, Niger

  13. Statistical models for estimating daily streamflow in Michigan

    USGS Publications Warehouse

    Holtschlag, D.J.; Salehi, Habib

    1992-01-01

    Statistical models for estimating daily streamflow were analyzed for 25 pairs of streamflow-gaging stations in Michigan. Stations were paired by randomly choosing a station operated in 1989 at which 10 or more years of continuous flow data had been collected and at which flow is virtually unregulated; a nearby station was chosen where flow characteristics are similar. Streamflow data from the 25 randomly selected stations were used as the response variables; streamflow data at the nearby stations were used to generate a set of explanatory variables. Ordinary-least squares regression (OLSR) equations, autoregressive integrated moving-average (ARIMA) equations, and transfer function-noise (TFN) equations were developed to estimate the log transform of flow for the 25 randomly selected stations. The precision of each type of equation was evaluated on the basis of the standard deviation of the estimation errors. OLSR equations produce one set of estimation errors; ARIMA and TFN models each produce l sets of estimation errors corresponding to the forecast lead. The lead-l forecast is the estimate of flow l days ahead of the most recent streamflow used as a response variable in the estimation. In this analysis, the standard deviation of lead l ARIMA and TFN forecast errors were generally lower than the standard deviation of OLSR errors for l < 2 days and l < 9 days, respectively. Composite estimates were computed as a weighted average of forecasts based on TFN equations and backcasts (forecasts of the reverse-ordered series) based on ARIMA equations. The standard deviation of composite errors varied throughout the length of the estimation interval and generally was at maximum near the center of the interval. For comparison with OLSR errors, the mean standard deviation of composite errors were computed for intervals of length 1 to 40 days. The mean standard deviation of length-l composite errors were generally less than the standard deviation of the OLSR errors for l < 32

  14. Measuring Disability: Application of the Rasch Model to Activities of Daily Living (ADL/IADL).

    ERIC Educational Resources Information Center

    Sheehan, T. Joseph; DeChello, Laurie M.; Garcia, Ramon; Fifield, Judith; Rothfield, Naomi; Reisine, Susan

    2001-01-01

    Performed a comparative analysis of Activities of Daily Living (ADL) items administered to 4,430 older adults and Instrumental Activities of Daily Living administered to 605 people with rheumatoid arthritis scoring both with Likert and Rasch measurement models. Findings show the superiority of the Rasch approach over the Likert method. (SLD)

  15. Measuring Disability: Application of the Rasch Model to Activities of Daily Living (ADL/IADL).

    ERIC Educational Resources Information Center

    Sheehan, T. Joseph; DeChello, Laurie M.; Garcia, Ramon; Fifield, Judith; Rothfield, Naomi; Reisine, Susan

    2001-01-01

    Performed a comparative analysis of Activities of Daily Living (ADL) items administered to 4,430 older adults and Instrumental Activities of Daily Living administered to 605 people with rheumatoid arthritis scoring both with Likert and Rasch measurement models. Findings show the superiority of the Rasch approach over the Likert method. (SLD)

  16. Upscaling instantaneous to daily evapotranspiration using modelled daily shortwave radiation for remote sensing applications: An artificial neural network approach

    DOE PAGES

    Wandera, Loise; Mallick, Kaniska; Kiely, Gerard; ...

    2017-01-11

    Upscaling instantaneous evapotranspiration retrieved at any specific time-of-day (ETi) to daily evapotranspiration (ETd) is a key challenge in mapping regional ET using polar orbiting sensors. Various studies have unanimously cited the shortwave incoming radiation (RS) to be the most robust reference variable explaining the ratio between ETd and ETi. This study aims to contribute in ETi upscaling for global studies using the ratio between daily and instantaneous incoming shortwave radiation (RSd/RSi) as a factor for converting ETi to ETd. This paper proposes an artificial neural network (ANN) machine-learning algorithm first to predict RSd from RSi followed by using the RSd/RSimore » ratio to convert ETi to ETd across different terrestrial ecosystems. Using RSi and RSd observations from multiple sub-networks of the FLUXNET database spread across different climates and biomes (to represent inputs that would typically be obtainable from remote sensors during the overpass time) in conjunction with some astronomical variables (e.g. solar zenith angle, day length, exoatmospheric shortwave radiation), we developed the ANN model for reproducing RSd and further used it to upscale ETi to ETd. The efficiency of the ANN is evaluated for different morning and afternoon times of day, under varying sky conditions, and also at different geographic locations. RS-based upscaled ETd produced a significant linear relation (R2 = 0.65 to 0.69), low bias (-0.31 to -0.56 MJ m-2 d-1; approx. 4 %), and good agreement (RMSE 1.55 to 1.86 MJ m-2 d-1; approx. 10 %) with the observed ETd, although a systematic overestimation of ETd was also noted under persistent cloudy sky conditions. Inclusion of soil moisture and rainfall information in ANN training reduced the systematic overestimation tendency in predominantly overcast days. An intercomparison with existing upscaling method at daily, 8-day, monthly, and yearly temporal resolution revealed a robust performance of the ANN-driven RS

  17. Upscaling instantaneous to daily evapotranspiration using modelled daily shortwave radiation for remote sensing applications: an artificial neural network approach

    NASA Astrophysics Data System (ADS)

    Wandera, Loise; Mallick, Kaniska; Kiely, Gerard; Roupsard, Olivier; Peichl, Mathias; Magliulo, Vincenzo

    2017-04-01

    Upscaling instantaneous evapotranspiration retrieved at any specific time-of-day (ETi) to daily evapotranspiration (ETd) is a key challenge in mapping regional ET using polar orbiting sensors. Various studies have unanimously cited the shortwave incoming radiation (RS) to be the most robust reference variable explaining the ratio between ETd and ETi . This study aims to contribute in ETi upscaling for global studies using the ratio between daily and instantaneous incoming shortwave radiation (RSd / RSi) as a factor for converting ETi to ETd. This paper proposes an artificial neural network (ANN) machine-learning algorithm first to predict RSd from RSi followed by using the RSd / RSi ratio to convert ETi to ETd across different terrestrial ecosystems. Using RSi and RSd observations from multiple sub-networks of the FLUXNET database spread across different climates and biomes (to represent inputs that would typically be obtainable from remote sensors during the overpass time) in conjunction with some astronomical variables (e.g. solar zenith angle, day length, exoatmospheric shortwave radiation), we developed the ANN model for reproducing RSd and further used it to upscale ETi to ETd. The efficiency of the ANN is evaluated for different morning and afternoon times of day, under varying sky conditions, and also at different geographic locations. RS-based upscaled ETd produced a significant linear relation (R 2 = 0.65 to 0.69), low bias (-0.31 to -0.56 MJ m-2 d -1 ; approx. 4 %), and good agreement (RMSE 1.55 to 1.86 MJ m-2 d -1 ; approx. 10 %) with the observed ETd, although a systematic overestimation of ETd was also noted under persistent cloudy sky conditions. Inclusion of soil moisture and rainfall information in ANN training reduced the systematic overestimation tendency in predominantly overcast days. An intercomparison with existing upscaling method at daily, 8-day, monthly, and yearly temporal resolution revealed a robust performance of the ANNdriven RS

  18. Upscaling instantaneous to daily evapotranspiration using modelled daily shortwave radiation for remote sensing applications: an artificial neural network approach

    NASA Astrophysics Data System (ADS)

    Wandera, Loise; Mallick, Kaniska; Kiely, Gerard; Roupsard, Olivier; Peichl, Matthias; Magliulo, Vincenzo

    2017-01-01

    Upscaling instantaneous evapotranspiration retrieved at any specific time-of-day (ETi) to daily evapotranspiration (ETd) is a key challenge in mapping regional ET using polar orbiting sensors. Various studies have unanimously cited the shortwave incoming radiation (RS) to be the most robust reference variable explaining the ratio between ETd and ETi. This study aims to contribute in ETi upscaling for global studies using the ratio between daily and instantaneous incoming shortwave radiation (RSd / RSi) as a factor for converting ETi to ETd.This paper proposes an artificial neural network (ANN) machine-learning algorithm first to predict RSd from RSi followed by using the RSd / RSi ratio to convert ETi to ETd across different terrestrial ecosystems. Using RSi and RSd observations from multiple sub-networks of the FLUXNET database spread across different climates and biomes (to represent inputs that would typically be obtainable from remote sensors during the overpass time) in conjunction with some astronomical variables (e.g. solar zenith angle, day length, exoatmospheric shortwave radiation), we developed the ANN model for reproducing RSd and further used it to upscale ETi to ETd. The efficiency of the ANN is evaluated for different morning and afternoon times of day, under varying sky conditions, and also at different geographic locations. RS-based upscaled ETd produced a significant linear relation (R2 = 0.65 to 0.69), low bias (-0.31 to -0.56 MJ m-2 d-1; approx. 4 %), and good agreement (RMSE 1.55 to 1.86 MJ m-2 d-1; approx. 10 %) with the observed ETd, although a systematic overestimation of ETd was also noted under persistent cloudy sky conditions. Inclusion of soil moisture and rainfall information in ANN training reduced the systematic overestimation tendency in predominantly overcast days. An intercomparison with existing upscaling method at daily, 8-day, monthly, and yearly temporal resolution revealed a robust performance of the ANN-driven RS-based ETi

  19. Modeling daily flow patterns individuals to characterize disease spread

    SciTech Connect

    Smallwood, J.; Hyman, J. M.; Mirchandani, Pitu B.

    2002-11-17

    The effect of an individual's travels throughout a day on the spread of disease is examined using a deterministic SIR model. We determine which spatial and demographic characteristics most contribute to the disease spread and whether the progression of the disease can be slowed by appropriate vaccination of people belonging to a specific location-type.

  20. Models for estimating daily rainfall erosivity in China

    USDA-ARS?s Scientific Manuscript database

    The multiplication of rainfall energy and maximum 30 minutes intensity (EI30) is the most widely used rainfall erosivity index for empirical soil loss prediction models, however its calculation requires high temporal resolution rainfall data which are often not readily available in China in most loc...

  1. Instantaneous-to-daily GPP upscaling schemes based on a coupled photosynthesis-stomatal conductance model: correcting the overestimation of GPP by directly using daily average meteorological inputs.

    PubMed

    Wang, Fumin; Gonsamo, Alemu; Chen, Jing M; Black, T Andrew; Zhou, Bin

    2014-11-01

    Daily canopy photosynthesis is usually temporally upscaled from instantaneous (i.e., seconds) photosynthesis rate. The nonlinear response of photosynthesis to meteorological variables makes the temporal scaling a significant challenge. In this study, two temporal upscaling schemes of daily photosynthesis, the integrated daily model (IDM) and the segmented daily model (SDM), are presented by considering the diurnal variations of meteorological variables based on a coupled photosynthesis-stomatal conductance model. The two models, as well as a simple average daily model (SADM) with daily average meteorological inputs, were validated using the tower-derived gross primary production (GPP) to assess their abilities in simulating daily photosynthesis. The results showed IDM closely followed the seasonal trend of the tower-derived GPP with an average RMSE of 1.63 g C m(-2) day(-1), and an average Nash-Sutcliffe model efficiency coefficient (E) of 0.87. SDM performed similarly to IDM in GPP simulation but decreased the computation time by >66%. SADM overestimated daily GPP by about 15% during the growing season compared to IDM. Both IDM and SDM greatly decreased the overestimation by SADM, and improved the simulation of daily GPP by reducing the RMSE by 34 and 30%, respectively. The results indicated that IDM and SDM are useful temporal upscaling approaches, and both are superior to SADM in daily GPP simulation because they take into account the diurnally varying responses of photosynthesis to meteorological variables. SDM is computationally more efficient, and therefore more suitable for long-term and large-scale GPP simulations.

  2. The Communal Coping Model of Pain Catastrophizing in Daily Life: A Within-Couples Daily Diary Study

    PubMed Central

    Burns, John W.; Gerhart, James I.; Post, Kristina M.; Smith, David A.; Porter, Laura S.; Schuster, Erik; Buvanendran, Asokumar; Fras, Anne Marie; Keefe, Francis J.

    2015-01-01

    The Communal Coping Model (CCM) characterizes pain catastrophizing as a coping tactic whereby pain expression elicits assistance and empathic responses from others. Married couples (N = 105 couples; one spouse with chronic low back pain) completed electronic daily diary assessments 5 times/day for 14 days. On these diaries, patients reported pain catastrophizing, pain, function, and perceived spouse support, criticism and hostility. Non-patient spouses reported on their support, criticism, and hostility directed toward patients, as well as their observations of patient pain and pain behaviors. Hierarchical linear modeling tested concurrent and lagged (3 hours later) relationships. Principal findings included: a) within-person increases in pain catastrophizing were positively associated with spouse reports of patient pain behavior in concurrent and lagged analyses; b) within-person increases in pain catastrophizing were positively associated with patient perceptions of spouse support, criticism, and hostility in concurrent analyses; c) within-person increases in pain catastrophizing were negatively associated with spouse reports of criticism and hostility in lagged analyses. Spouses reported patient behaviors that were tied to elevated pain catastrophizing, and spouses changed their behavior during and following elevated pain catastrophizing episodes. Pain catastrophizing may affect the interpersonal environment of patients and spouses in ways consistent with the CCM. PMID:26320945

  3. Comparing an annual and daily time-step model for predicting field-scale phosphorus loss

    USDA-ARS?s Scientific Manuscript database

    Numerous models exist for describing phosphorus (P) losses from agricultural fields. The complexity of these models varies considerably ranging from simple empirically-based annual time-step models to more complex process-based daily time step models. While better accuracy is often assumed with more...

  4. Modeling daily realized futures volatility with singular spectrum analysis

    NASA Astrophysics Data System (ADS)

    Thomakos, Dimitrios D.; Wang, Tao; Wille, Luc T.

    2002-09-01

    Using singular spectrum analysis (SSA), we model the realized volatility and logarithmic standard deviations of two important futures return series. The realized volatility and logarithmic standard deviations are constructed following the methodology of Andersen et al. [J. Am. Stat. Ass. 96 (2001) 42-55] using intra-day transaction data. We find that SSA decomposes the volatility series quite well and effectively captures both the market trend (accounting for about 34-38% of the total variance in the series) and, more importantly, a number of underlying market periodicities. Reliable identification of any periodicities is extremely important for options pricing and risk management and we believe that SSA can be a useful addition to the financial practitioners’ toolbox.

  5. Multilevel Factor Analysis and Structural Equation Modeling of Daily Diary Coping Data: Modeling Trait and State Variation

    ERIC Educational Resources Information Center

    Roesch, Scott C.; Aldridge, Arianna A.; Stocking, Stephanie N.; Villodas, Feion; Leung, Queenie; Bartley, Carrie E.; Black, Lisa J.

    2010-01-01

    This study used multilevel modeling of daily diary data to model within-person (state) and between-person (trait) components of coping variables. This application included the introduction of multilevel factor analysis (MFA) and a comparison of the predictive ability of these trait/state factors. Daily diary data were collected on a large (n =…

  6. One-day offset in daily hydrologic modeling: An exploration of the issue in automatic model calibration

    USDA-ARS?s Scientific Manuscript database

    The literature of daily hydrologic modelling illustrates that daily simulation models are incapable of accurately representing hydrograph timing due to relationships between precipitation and watershed hydrologic response. For watersheds with a time of concentration less than 24 hrs and a late day p...

  7. SELECTION OF CANDIDATE EUTROPHICATION MODELS FOR TOTAL MAXIMUM DAILY LOADS ANALYSES

    EPA Science Inventory

    A tiered approach was developed to evaluate candidate eutrophication models to select a common suite of models that could be used for Total Maximum Daily Loads (TMDL) analyses in estuaries, rivers, and lakes/reservoirs. Consideration for linkage to watershed models and ecologica...

  8. SELECTION OF CANDIDATE EUTROPHICATION MODELS FOR TOTAL MAXIMUM DAILY LOADS ANALYSES

    EPA Science Inventory

    A tiered approach was developed to evaluate candidate eutrophication models to select a common suite of models that could be used for Total Maximum Daily Loads (TMDL) analyses in estuaries, rivers, and lakes/reservoirs. Consideration for linkage to watershed models and ecologica...

  9. A dual pathway model of daily stressor effects on rheumatoid arthritis.

    PubMed

    Affleck, G; Urrows, S; Tennen, H; Higgins, P; Pav, D; Aloisi, R

    1997-01-01

    This study evaluated the initial promise of a dual-pathway conceptual model linking daily event stressors to rheumatoid arthritis (RA) disease activity through changes in immune system activation and mood. Fifty individuals, who were studied on five occasions two weeks apart, reported daily event stressors on the Daily Life Experience Checklist, daily mood on an abbreviated version of the Profile of Mood States-B, and daily joint pain on the Rapid Assessment of Disease Activity in Rheumatology. Serial clinical examinations comprised ratings of joint tenderness and swelling, and blood drawn during exams was analyzed for sedimentation rate (an indicator of systemic inflammation) and soluble interleukin-2 receptors (a marker of immune system activation known to correlate with RA disease activity). Across-person analyses failed to establish links from daily event stressors to either disease activity or composites of joint pain and joint inflammation when associations were adjusted for the effect of neuroticism on self-report measures. Pooled within-person analyses, however, were generally consistent with the relations predicted by the dual-pathway model. Increases in daily event stressors during the week preceding each clinical exam were associated with increased joint pain (regardless of changes in mood). At the same time, increased daily stressors were indirectly associated with decreased joint inflammation through reduction in levels of soluble interleukin-2 receptors. The dual-pathway model, which may be limited to short-term psychological and psychoimmunologic processes, underscores the importance of distinguishing potentially opposing effects of stress on pain versus inflammation in individuals with rheumatoid arthritis.

  10. Evaluation of a watershed model for estimating daily flow using limited flow measurements

    USDA-ARS?s Scientific Manuscript database

    The Soil and Water Assessment Tool (SWAT) model was evaluated for estimation of continuous daily flow based on limited flow measurements in the Upper Oyster Creek (UOC) watershed. SWAT was calibrated against limited measured flow data and then validated. The Nash-Sutcliffe model Efficiency (NSE) and...

  11. Estimation of instantaneous peak flow from simulated maximum daily flow using the HBV model

    NASA Astrophysics Data System (ADS)

    Ding, Jie; Haberlandt, Uwe

    2014-05-01

    Instantaneous peak flow (IPF) data are the foundation of the design of hydraulic structures and flood frequency analysis. However, the long discharge records published by hydrological agencies contain usually only average daily flows which are of little value for design in small catchments. In former research, statistical analysis using observed peak and daily flow data was carried out to explore the link between instantaneous peak flow (IPF) and maximum daily flow (MDF) where the multiple regression model is proved to have the best performance. The objective of this study is to further investigate the acceptability of the multiple regression model for post-processing simulated daily flows from hydrological modeling. The model based flood frequency analysis allows to consider change in the condition of the catchments and in climate for design. Here, the HBV model is calibrated on peak flow distributions and flow duration curves using two approaches. In a two -step approach the simulated MDF are corrected with a priory established regressions. In a one-step procedure the regression coefficients are calibrated together with the parameters of the model. For the analysis data from 18 mesoscale catchments in the Aller-Leine river basin in Northern Germany are used. The results show that: (1) the multiple regression model is capable to predict the peak flows with the simulated MDF data; (2) the calibrated hydrological model reproduces well the magnitude and frequency distribution of peak flows; (3) the one-step procedure outperforms the two-step procedure regarding the estimation of peak flows.

  12. Modeling turbidity and flow at daily steps in karst using ARIMA/ARFIMA-GARCH error models

    NASA Astrophysics Data System (ADS)

    Massei, N.

    2013-12-01

    Hydrological and physico-chemical variations recorded at karst springs usually reflect highly non-linear processes and the corresponding time series are then very often also highly non-linear. Among others, turbidity, as an important parameter regarding water quality and management, is a very complex response of karst systems to rain events, involving direct transfer of particles from point-source recharge as well as resuspension of particles previously deposited and stored within the system. For those reasons, turbidity modeling has not been well taken in karst hydrological models so far. Most of the time, the modeling approaches would involve stochastic linear models such ARIMA-type models and their derivatives (ARMA, ARMAX, ARIMAX, ARFIMA...). Yet, linear models usually fail to represent well the whole (stochastic) process variability, and their residuals still contain useful information that can be used to either understand the whole variability or to enhance short-term predictability and forecasting. Model residuals are actually not i.i.d., which can be identified by the fact that squared residuals still present clear and significant serial correlation. Indeed, high (low) amplitudes are followed in time by high (low) amplitudes, which can be seen on residuals time series as periods of time during which amplitudes are higher (lower) then the mean amplitude. This is known as the ARCH effet (AutoRegressive Conditional Heteroskedasticity), and the corresponding non-linear process affecting residuals of a linear model can be modeled using ARCH or generalized ARCH (GARCH) non-linear modeling, which approaches are very well known in econometrics. Here we investigated the capability of ARIMA-GARCH error models to represent a ~20-yr daily turbidity time series recorded at a karst spring used for water supply of the city of Le Havre (Upper Normandy, France). ARIMA and ARFIMA models were used to represent the mean behavior of the time series and the residuals clearly

  13. Modelling the atmospheric boundary layer for remotely sensed estimates of daily evaporation

    NASA Technical Reports Server (NTRS)

    Gurney, R. J.; Blyth, K.; Camillo, P. J.

    1984-01-01

    An energy and moisture balance model of the soil surface was used to estimate daily evaporation from wheat and barley fields in West Germany. The model was calibrated using remotely sensed surface temperature estimates. Complete atmospheric boundary layer models are difficult to use because of the number of parameters involved and a simplified model was used here. The resultant evaporation estimates were compared to eddy correlation evaporation estimates and good agreement was found.

  14. Modelling and forecasting monthly and daily river discharge data using hybrid models and considering autoregressive heteroscedasticity

    NASA Astrophysics Data System (ADS)

    Szolgayova, Elena

    2010-05-01

    hydrological models. However, the GARCH family of models proved to be suited in removing it only in daily time step. The basic GARCH model was not applicable on any of the time series. In all other investigated cases, the EGARCH(1,1) model had to be used. Unlike in econometric time series, where the so called leverage effect (i.e. the series reacts more strongly to negative changes) is present and pointed out by this model, here the data tends to react more strongly on positive changes. In this particular case it was found, that the general property of hydrological processes, that the rise of discharge is rainfall driven (a highly nonlinear chaotic intermittent process) and the decrease of discharge is ruled by the damping effects of the water storage in the driven system (catchment or river reach), is present also in the hydrological model error series. This shows, that the modelling and forecasting of floods (pulse like rising discharge) is a more demanding task than that of droughts (slowly decreasing flows). Even though the GARCH models did show partial improvements in the modelling and forecasting of flows, they still have several serious disadvantages (such as high sensitivity to the chosen fitting period) and possible further use should be further investigated. These results are of importance with respect to future attempts of modelling of error time series of hydrological models in such hybrid frameworks. They underpin the need of a non-mechanistic approach in the case based analysis of such data and the physical interpretation of statistical modelling results.

  15. Examining the Effects of Video Modeling and Prompts to Teach Activities of Daily Living Skills.

    PubMed

    Aldi, Catarina; Crigler, Alexandra; Kates-McElrath, Kelly; Long, Brian; Smith, Hillary; Rehak, Kim; Wilkinson, Lisa

    2016-12-01

    Video modeling has been shown to be effective in teaching a number of skills to learners diagnosed with autism spectrum disorders (ASD). In this study, we taught two young men diagnosed with ASD three different activities of daily living skills (ADLS) using point-of-view video modeling. Results indicated that both participants met criterion for all ADLS. Participants did not maintain mastery criterion at a 1-month follow-up, but did score above baseline at maintenance with and without video modeling. • Point-of-view video models may be an effective intervention to teach daily living skills. • Video modeling with handheld portable devices (Apple iPod or iPad) can be just as effective as video modeling with stationary viewing devices (television or computer). • The use of handheld portable devices (Apple iPod and iPad) makes video modeling accessible and possible in a wide variety of environments.

  16. Investigation of simple daily solar radiation models suitable for use in the design of solar heating systems

    SciTech Connect

    Sillman, S.

    1980-08-01

    Solar heating system simulations typically require hourly weather data and the use of a main-line computer. A simpler alternative is to use daily steps with a model for daily solar collection. This report investigates the accuracy of sinusoidal radiation models for use in solar heating simulation. Accuracy of daily radiation models is assessed in two ways: by a theoretical comparison with hourly weather data, and by analysis of results of daily simulation. Results indicate that a daily radiation model can be designed with errors of less than 2%.

  17. Modeling bulk density and snow water equivalent using daily snow depth observations

    NASA Astrophysics Data System (ADS)

    McCreight, J. L.; Small, E. E.

    2014-03-01

    Bulk density is a fundamental property of snow relating its depth and mass. Previously, two simple models of bulk density (depending on snow depth, date, and location) have been developed to convert snow depth observations to snow water equivalent (SWE) estimates. However, these models were not intended for application at the daily time step. We develop a new model of bulk density for the daily time step and demonstrate its improved skill over the existing models. Snow depth and density are negatively correlated at short (10 days) timescales while positively correlated at longer (90 days) timescales. We separate these scales of variability by modeling smoothed, daily snow depth (long timescales) and the observed positive and negative anomalies from the smoothed time series (short timescales) as separate terms. A climatology of fit is also included as a predictor variable. Over half a million daily observations of depth and SWE at 345 snowpack telemetry (SNOTEL) sites are used to fit models and evaluate their performance. For each location, we train the three models to the neighboring stations within 70 km, transfer the parameters to the location to be modeled, and evaluate modeled time series against the observations at that site. Our model exhibits improved statistics and qualitatively more-realistic behavior at the daily time step when sufficient local training data are available. We reduce density root mean square error (RMSE) by 9.9 and 4.5% compared to previous models while increasing R2 from 0.46 to 0.52 to 0.56 across models. Focusing on the 21-day window around peak SWE in each water year, our model reduces density RMSE by 24 and 17.4% relative to the previous models, with R2 increasing from 0.55 to 0.58 to 0.71 across models. Removing the challenge of parameter transfer over the full observational record increases R2 scores for both the existing and new models, but the gain is greatest for the new model (R2 = 0.75). Our model shows general improvement over

  18. Comparing flow duration curve and rainfall-runoff modelling for predicting daily runoff in ungauged catchments

    NASA Astrophysics Data System (ADS)

    Zhang, Yongqiang; Vaze, Jai; Chiew, Francis H. S.; Li, Ming

    2015-06-01

    Predicting daily runoff time series in ungauged catchments is both important and challenging. For the last few decades, the rainfall-runoff (RR) modelling approach has been the method of choice. There have been very few studies reported in literature which attempt to use flow duration curve (FDC) to predict daily runoff time series. This study comprehensively compares the two approaches using an extensive dataset (228 catchments) for a large region of south-eastern Australia and provides guidelines for choosing the suitable method. For each approach we used the nearest neighbour method and two weightings - a 5-donor simple mathematical average (SA) and a 5-donor inverse-distance weighting (5-IDW) - to predict daily runoff time series. The results show that 5-IDW was noticeably better than a single donor to predict daily runoff time series, especially for the FDC approach. The RR modelling approach calibrated against daily runoff outperformed the FDC approach for predicting high flows. The FDC approach was better at predicting medium to low flows in traditional calibration against the Nash-Sutcliffe-Efficiency or Root Mean Square Error, but when calibrated against a low flow objective function, both the FDC and rainfall-runoff models performed equally well in simulating the low flows. These results indicate that both methods can be further improved to simulate daily hydrographs describing the range of flow metrics in ungauged catchments. Further studies should be carried out for improving the accuracy of predicted FDC in ungauged catchments, including improving the FDC model structure and parameter fitting.

  19. Dynamic regression modeling of daily nitrate-nitrogen concentrations in a large agricultural watershed.

    PubMed

    Feng, Zhujing; Schilling, Keith E; Chan, Kung-Sik

    2013-06-01

    Nitrate-nitrogen concentrations in rivers represent challenges for water supplies that use surface water sources. Nitrate concentrations are often modeled using time-series approaches, but previous efforts have typically relied on monthly time steps. In this study, we developed a dynamic regression model of daily nitrate concentrations in the Raccoon River, Iowa, that incorporated contemporaneous and lags of precipitation and discharge occurring at several locations around the basin. Results suggested that 95 % of the variation in daily nitrate concentrations measured at the outlet of a large agricultural watershed can be explained by time-series patterns of precipitation and discharge occurring in the basin. Discharge was found to be a more important regression variable than precipitation in our model but both regression parameters were strongly correlated with nitrate concentrations. The time-series model was consistent with known patterns of nitrate behavior in the watershed, successfully identifying contemporaneous dilution mechanisms from higher relief and urban areas of the basin while incorporating the delayed contribution of nitrate from tile-drained regions in a lagged response. The first difference of the model errors were modeled as an AR(16) process and suggest that daily nitrate concentration changes remain temporally correlated for more than 2 weeks although temporal correlation was stronger in the first few days before tapering off. Consequently, daily nitrate concentrations are non-stationary, i.e. of strong memory. Using time-series models to reliably forecast daily nitrate concentrations in a river based on patterns of precipitation and discharge occurring in its basin may be of great interest to water suppliers.

  20. An improved hybrid data-driven model and its application in daily rainfall-runoff simulation

    NASA Astrophysics Data System (ADS)

    Kan, Guangyuan; He, Xiaoyan; Ding, Liuqian; Li, Jiren; Lei, Tianjie; Liang, Ke; Hong, Yang

    2016-11-01

    In previous literatures, a coupled data-driven rainfall-runoff (RR) model, NU-PEK, has been proposed and successfully applied in hourly RR simulation task. However, numerical experiments show that its performance for daily RR simulation is unsatisfactory. It is noticed that the poor performance is due to the inability of the original model to capture the much higher non-linear characteristics contained in the daily data. In order to improve the nonlinearity simulation capability of the original model, an improved model named NU-PKEK and its calibration methodology are developed in this paper. The improved model is constituted by adding a K-means clustering module and utilizing multiple NU-PEK modules instead of using only one NU-PEK model. This study applies the improved model, the Xinanjiang model, and the original model for daily RR simulation in Chengcun catchment for intercomparison and verification. The simulation results prove that the NU-PKEK performs best, and has better simulation and forecasting capability.

  1. MULTIVARIATE STATISTICAL MODELS FOR EFFECTS OF PM AND COPOLLUTANTS IN A DAILY TIME SERIES EPIDEMIOLOGY STUDY

    EPA Science Inventory

    Most analyses of daily time series epidemiology data relate mortality or morbidity counts to PM and other air pollutants by means of single-outcome regression models using multiple predictors, without taking into account the complex statistical structure of the predictor variable...

  2. Modeling Comparative Daily Enrollment Indicators To Aid Intelligent College Decisions. AIR 2001 Annual Forum Paper.

    ERIC Educational Resources Information Center

    Lajubutu, Oyebanjo A.

    This paper shows how three critical enrollment indicators drawn from a relationship database were used to guide planning and management decisions. The paper discusses the guidelines for the development of the model, attributes needed, variables to be calculated, and other issues that may improve the effectiveness and efficiency of daily enrollment…

  3. Application of Markov chain model to daily maximum temperature for thermal comfort in Malaysia

    SciTech Connect

    Nordin, Muhamad Asyraf bin Che; Hassan, Husna

    2015-10-22

    The Markov chain’s first order principle has been widely used to model various meteorological fields, for prediction purposes. In this study, a 14-year (2000-2013) data of daily maximum temperatures in Bayan Lepas were used. Earlier studies showed that the outdoor thermal comfort range based on physiologically equivalent temperature (PET) index in Malaysia is less than 34°C, thus the data obtained were classified into two state: normal state (within thermal comfort range) and hot state (above thermal comfort range). The long-run results show the probability of daily temperature exceed TCR will be only 2.2%. On the other hand, the probability daily temperature within TCR will be 97.8%.

  4. Application of Markov chain model to daily maximum temperature for thermal comfort in Malaysia

    NASA Astrophysics Data System (ADS)

    Nordin, Muhamad Asyraf bin Che; Hassan, Husna

    2015-10-01

    The Markov chain's first order principle has been widely used to model various meteorological fields, for prediction purposes. In this study, a 14-year (2000-2013) data of daily maximum temperatures in Bayan Lepas were used. Earlier studies showed that the outdoor thermal comfort range based on physiologically equivalent temperature (PET) index in Malaysia is less than 34°C, thus the data obtained were classified into two state: normal state (within thermal comfort range) and hot state (above thermal comfort range). The long-run results show the probability of daily temperature exceed TCR will be only 2.2%. On the other hand, the probability daily temperature within TCR will be 97.8%.

  5. The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data

    SciTech Connect

    Behrang, M.A.; Assareh, E.; Ghanbarzadeh, A.; Noghrehabadi, A.R.

    2010-08-15

    The main objective of present study is to predict daily global solar radiation (GSR) on a horizontal surface, based on meteorological variables, using different artificial neural network (ANN) techniques. Daily mean air temperature, relative humidity, sunshine hours, evaporation, and wind speed values between 2002 and 2006 for Dezful city in Iran (32 16'N, 48 25'E), are used in this study. In order to consider the effect of each meteorological variable on daily GSR prediction, six following combinations of input variables are considered: (I)Day of the year, daily mean air temperature and relative humidity as inputs and daily GSR as output. (II)Day of the year, daily mean air temperature and sunshine hours as inputs and daily GSR as output. (III)Day of the year, daily mean air temperature, relative humidity and sunshine hours as inputs and daily GSR as output. (IV)Day of the year, daily mean air temperature, relative humidity, sunshine hours and evaporation as inputs and daily GSR as output. (V)Day of the year, daily mean air temperature, relative humidity, sunshine hours and wind speed as inputs and daily GSR as output. (VI)Day of the year, daily mean air temperature, relative humidity, sunshine hours, evaporation and wind speed as inputs and daily GSR as output. Multi-layer perceptron (MLP) and radial basis function (RBF) neural networks are applied for daily GSR modeling based on six proposed combinations. The measured data between 2002 and 2005 are used to train the neural networks while the data for 214 days from 2006 are used as testing data. The comparison of obtained results from ANNs and different conventional GSR prediction (CGSRP) models shows very good improvements (i.e. the predicted values of best ANN model (MLP-V) has a mean absolute percentage error (MAPE) about 5.21% versus 10.02% for best CGSRP model (CGSRP 5)). (author)

  6. Artificial neural networks modeling for forecasting the maximum daily total precipitation at Athens, Greece

    NASA Astrophysics Data System (ADS)

    Nastos, P. T.; Paliatsos, A. G.; Koukouletsos, K. V.; Larissi, I. K.; Moustris, K. P.

    2014-07-01

    Extreme daily precipitation events are involved in significant environmental damages, even in life loss, because of causing adverse impacts, such as flash floods, in urban and sometimes in rural areas. Thus, long-term forecast of such events is of great importance for the preparation of local authorities in order to confront and mitigate the adverse consequences. The objective of this study is to estimate the possibility of forecasting the maximum daily precipitation for the next coming year. For this reason, appropriate prognostic models, such as Artificial Neural Networks (ANNs) were developed and applied. The data used for the analysis concern annual maximum daily precipitation totals, which have been recorded at the National Observatory of Athens (NOA), during the long term period 1891-2009. To evaluate the potential of daily extreme precipitation forecast by the applied ANNs, a different period for validation was considered than the one used for the ANNs training. Thus, the datasets of the period 1891-1980 were used as training datasets, while the datasets of the period 1981-2009 as validation datasets. Appropriate statistical indices, such as the coefficient of determination (R2), the index of agreement (IA), the Root Mean Square Error (RMSE) and the Mean Bias Error (MBE), were applied to test the reliability of the models. The findings of the analysis showed that, a quite satisfactory relationship (R2 = 0.482, IA = 0.817, RMSE = 16.4 mm and MBE = + 5.2 mm) appears between the forecasted and the respective observed maximum daily precipitation totals one year ahead. The developed ANN seems to overestimate the maximum daily precipitation totals appeared in 1988 while underestimate the maximum in 1999, which could be attributed to the relatively low frequency of occurrence of these extreme events within GAA having impact on the optimum training of ANN.

  7. A predictive model relating daily fluctuations in summer temperatures and mortality rates.

    PubMed

    Fouillet, Anne; Rey, Grégoire; Jougla, Eric; Frayssinet, Philippe; Bessemoulin, Pierre; Hémon, Denis

    2007-06-19

    In the context of climate change, an efficient alert system to prevent the risk associated with summer heat is necessary. The authors' objective was to describe the temperature-mortality relationship in France over a 29-year period and to define and validate a combination of temperature factors enabling optimum prediction of the daily fluctuations in summer mortality. The study addressed the daily mortality rates of subjects aged over 55 years, in France as a whole, from 1975 to 2003. The daily minimum and maximum temperatures consisted in the average values recorded by 97 meteorological stations. For each day, a cumulative variable for the maximum temperature over the preceding 10 days was defined. The mortality rate was modelled using a Poisson regression with over-dispersion and a first-order autoregressive structure and with control for long-term and within-summer seasonal trends. The lag effects of temperature were accounted for by including the preceding 5 days. A "backward" method was used to select the most significant climatic variables. The predictive performance of the model was assessed by comparing the observed and predicted daily mortality rates on a validation period (summer 2003), which was distinct from the calibration period (1975-2002) used to estimate the model. The temperature indicators explained 76% of the total over-dispersion. The greater part of the daily fluctuations in mortality was explained by the interaction between minimum and maximum temperatures, for a day t and the day preceding it. The prediction of mortality during extreme events was greatly improved by including the cumulative variables for maximum temperature, in interaction with the maximum temperatures. The correlation between the observed and estimated mortality ratios was 0.88 in the final model. Although France is a large country with geographic heterogeneity in both mortality and temperatures, a strong correlation between the daily fluctuations in mortality and the

  8. Daily Spillover From Family to Work: A Test of the Work-Home Resources Model.

    PubMed

    Du, Danyang; Derks, Daantje; Bakker, Arnold B

    2017-02-02

    The present study examines a mediated moderation model of the day-level effects of family hassles and family-work spillover (affect and cognition) on the relationship between job resources and employees' flourishing at work. Based on the work-home resources model, the authors hypothesized that demands from one domain (family) induce repetitive thoughts or negative feelings about those problems, so that individuals are not able to function optimally and to make full use of contextual resources in the other domain (work). Multilevel analyses of 108 Chinese working parents' 366 daily surveys revealed that the relationship between morning job resources and afternoon flourishing was significantly positive when previous day family hassles were low; the relationship became nonsignificant when previous day family hassles were high. In addition, as predicted, daily rumination also attenuated the relationship between morning job resources and afternoon flourishing, whereas daily affect did not. Finally, the moderating effect of previous day family hassles was mediated by daily rumination. The findings contribute to spillover theories by revealing the roles of affective and cognitive spillover from family to work. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  9. Estimating daily minimum, maximum, and mean near surface air temperature using hybrid satellite models across Israel.

    PubMed

    Rosenfeld, Adar; Dorman, Michael; Schwartz, Joel; Novack, Victor; Just, Allan C; Kloog, Itai

    2017-08-21

    Meteorological stations measure air temperature (Ta) accurately with high temporal resolution, but usually suffer from limited spatial resolution due to their sparse distribution across rural, undeveloped or less populated areas. Remote sensing satellite-based measurements provide daily surface temperature (Ts) data in high spatial and temporal resolution and can improve the estimation of daily Ta. In this study we developed spatiotemporally resolved models which allow us to predict three daily parameters: Ta Max (day time), 24h mean, and Ta Min (night time) on a fine 1km grid across the state of Israel. We used and compared both the Aqua and Terra MODIS satellites. We used linear mixed effect models, IDW (inverse distance weighted) interpolations and thin plate splines (using a smooth nonparametric function of longitude and latitude) to first calibrate between Ts and Ta in those locations where we have available data for both and used that calibration to fill in neighboring cells without surface monitors or missing Ts. Out-of-sample ten-fold cross validation (CV) was used to quantify the accuracy of our predictions. Our model performance was excellent for both days with and without available Ts observations for both Aqua and Terra (CV Aqua R(2) results for min 0.966, mean 0.986, and max 0.967; CV Terra R(2) results for min 0.965, mean 0.987, and max 0.968). Our research shows that daily min, mean and max Ta can be reliably predicted using daily MODIS Ts data even across Israel, with high accuracy even for days without Ta or Ts data. These predictions can be used as three separate Ta exposures in epidemiology studies for better diurnal exposure assessment. Copyright © 2017 Elsevier Inc. All rights reserved.

  10. Modeling of daily body weights and body weight changes of Nordic Red cows.

    PubMed

    Mäntysaari, P; Mäntysaari, E A

    2015-10-01

    Increased availability of automated weighing systems have made it possible to record massive amounts of body weight (BW) data in a short time. If the BW measurement is unbiased, the changes in BW reflect the energy status of the cow and can be used for management or breeding purposes. The usefulness of the BW data depends on the reliability of the measures. The noise in BW measurements can be smoothed by fitting a parametric or time series model into the BW measurements. This study examined the accuracy of different models to predict BW of the cows based on daily BW measurements and investigated the usefulness of modeling in increasing the value of BW measurements as management and breeding tools. Data included daily BW measurements, production, and intake from 230 Nordic Red dairy cows. The BW of the cows was recorded twice a day on their return from milking. In total, the data included 50,594 daily observations with 98,418 BW measurements. A clear diurnal change was present in the BW of the cows even if they had feed available 24 h. The daily average BW were used in the modeling. Five different models were tested: (1) a cow-wise fixed second-order polynomial regression model (FiX) including the exponential Wilmink term, (2) a random regression model with fixed and random animal lactation stage functions (MiX), (3) MiX with 13 periods of weighing added (PER), (4) natural cubic smoothing splines with 8 equally spaced knots (SPk8), and (5) spline model with no restriction on knots but a smoothing parameter corresponding to a fit of 5 degrees of freedom (SPdf5). In the original measured BW data, the within-animal variation was 6.4% of the total variance. Modeling decreased the within animal variation to levels of 2.9 to 5.1%. The smallest day-to-day variation and thereafter highest day-to-day repeatabilities were with PER and MiX models. The usability of modeled BW as energy balance (EB) indicator were evaluated by estimating relationships between EB, or EB

  11. The benefits of daily data and scale up issues in hydrologic models-SWAT and CRAFT

    NASA Astrophysics Data System (ADS)

    Huang, Yumei; Quinn, Paul; Liang, Qiuhua; Adams, Russell

    2017-04-01

    When modelling the flow pathways for nutrient transport, the lack of good data and limitation of data resolution become the key cause of low quality output in various hydrologic models. The scale of catchment being studied would present the main issues of the sensitivity and uncertainty expected on the hydrologic modelling. Equally, the time step chosen is also important to nutrient dynamics. This study aims to evaluate the benefits of using both monthly and daily data in hydrologic models, and to address the issues of catchment scale when using the two hydrologic models, the Soil and Water Assessment Tool (SWAT), and Catchment Runoff Attenuation Flux Tool (CRAFT), by comparing the difference between SWAT and CRAFT in flow pathways and sediment transport. The models are different in terms of complexity, therefore the poster will discuss the strengths and weakness of the models. Also we can show the problems of calibration and how the models can be used to support catchment modelling.

  12. A temperature-based model for estimating monthly average daily global solar radiation in China.

    PubMed

    Li, Huashan; Cao, Fei; Wang, Xianlong; Ma, Weibin

    2014-01-01

    Since air temperature records are readily available around the world, the models based on air temperature for estimating solar radiation have been widely accepted. In this paper, a new model based on Hargreaves and Samani (HS) method for estimating monthly average daily global solar radiation is proposed. With statistical error tests, the performance of the new model is validated by comparing with the HS model and its two modifications (Samani model and Chen model) against the measured data at 65 meteorological stations in China. Results show that the new model is more accurate and robust than the HS, Samani, and Chen models in all climatic regions, especially in the humid regions. Hence, the new model can be recommended for estimating solar radiation in areas where only air temperature data are available in China.

  13. Characteristics of sub-daily precipitation extremes in observed data and regional climate model simulations

    NASA Astrophysics Data System (ADS)

    Beranová, Romana; Kyselý, Jan; Hanel, Martin

    2017-03-01

    The study compares characteristics of observed sub-daily precipitation extremes in the Czech Republic with those simulated by Hadley Centre Regional Model version 3 (HadRM3) and Rossby Centre Regional Atmospheric Model version 4 (RCA4) regional climate models (RCMs) driven by reanalyses and examines diurnal cycles of hourly precipitation and their dependence on intensity and surface temperature. The observed warm-season (May-September) maxima of short-duration (1, 2 and 3 h) amounts show one diurnal peak in the afternoon, which is simulated reasonably well by RCA4, although the peak occurs too early in the model. HadRM3 provides an unrealistic diurnal cycle with a nighttime peak and an afternoon minimum coinciding with the observed maximum for all three ensemble members, which suggests that convection is not captured realistically. Distorted relationships of the diurnal cycles of hourly precipitation to daily maximum temperature in HadRM3 further evidence that underlying physical mechanisms are misrepresented in this RCM. Goodness-of-fit tests indicate that generalised extreme value distribution is an applicable model for both observed and RCM-simulated precipitation maxima. However, the RCMs are not able to capture the range of the shape parameter estimates of distributions of short-duration precipitation maxima realistically, leading to either too many (nearly all for HadRM3) or too few (RCA4) grid boxes in which the shape parameter corresponds to a heavy tail. This means that the distributions of maxima of sub-daily amounts are distorted in the RCM-simulated data and do not match reality well. Therefore, projected changes of sub-daily precipitation extremes in climate change scenarios based on RCMs not resolving convection need to be interpreted with caution.

  14. Semiparametric Modeling of Daily Ammonia Levels in Naturally Ventilated Caged-Egg Facilities

    PubMed Central

    Gutiérrez-Zapata, Diana María; Galeano-Vasco, Luis Fernando; Cerón-Muñoz, Mario Fernando

    2016-01-01

    Ammonia concentration (AMC) in poultry facilities varies depending on different environmental conditions and management; however, this is a relatively unexplored subject in Colombia (South America). The objective of this study was to model daily AMC variations in a naturally ventilated caged-egg facility using generalized additive models. Four sensor nodes were used to record AMC, temperature, relative humidity and wind speed on a daily basis, with 10 minute intervals for 12 weeks. The following variables were included in the model: Heat index, Wind, Hour, Location, Height of the sensor to the ground level, and Period of manure accumulation. All effects included in the model were highly significant (p<0.001). The AMC was higher during the night and early morning when the wind was not blowing (0.0 m/s) and the heat index was extreme. The average and maximum AMC were 5.94±3.83 and 31.70 ppm, respectively. Temperatures above 25°C and humidity greater than 80% increased AMC levels. In naturally ventilated caged-egg facilities the daily variations observed in AMC primarily depend on cyclic variations of the environmental conditions and are also affected by litter handling (i.e., removal of the bedding material). PMID:26812150

  15. Semiparametric Modeling of Daily Ammonia Levels in Naturally Ventilated Caged-Egg Facilities.

    PubMed

    Gutiérrez-Zapata, Diana María; Galeano-Vasco, Luis Fernando; Cerón-Muñoz, Mario Fernando

    2016-01-01

    Ammonia concentration (AMC) in poultry facilities varies depending on different environmental conditions and management; however, this is a relatively unexplored subject in Colombia (South America). The objective of this study was to model daily AMC variations in a naturally ventilated caged-egg facility using generalized additive models. Four sensor nodes were used to record AMC, temperature, relative humidity and wind speed on a daily basis, with 10 minute intervals for 12 weeks. The following variables were included in the model: Heat index, Wind, Hour, Location, Height of the sensor to the ground level, and Period of manure accumulation. All effects included in the model were highly significant (p<0.001). The AMC was higher during the night and early morning when the wind was not blowing (0.0 m/s) and the heat index was extreme. The average and maximum AMC were 5.94±3.83 and 31.70 ppm, respectively. Temperatures above 25°C and humidity greater than 80% increased AMC levels. In naturally ventilated caged-egg facilities the daily variations observed in AMC primarily depend on cyclic variations of the environmental conditions and are also affected by litter handling (i.e., removal of the bedding material).

  16. Effects of temperature seasonality on tundra vegetation productivity using a daily vegetation dynamics model

    NASA Astrophysics Data System (ADS)

    Epstein, H. E.; Erler, A.; Frazier, J.; Bhatt, U. S.

    2011-12-01

    Changes in the seasonality of air temperature will elicit interacting effects on the dynamics of snow cover, nutrient availability, vegetation growth, and other ecosystem properties and processes in arctic tundra. Simulation models often do not have the fine temporal resolution necessary to develop theory and propose hypotheses for the effects of daily and weekly timescale changes on ecosystem dynamics. We therefore developed a daily version of an arctic tundra vegetation dynamics model (ArcVeg) to simulate how changes in the seasonality of air temperatures influences the dynamics of vegetation growth and carbon sequestration across regions of arctic tundra. High temporal-resolution air and soil temperature data collected from field sites across the five arctic tundra bioclimate subzones were used to develop a daily weather generator operable for sites throughout the arctic tundra. Empirical relationships between temperature and soil nitrogen were used to generate daily dynamics of soil nitrogen availability, which drive the daily uptake of nitrogen and growth among twelve tundra plant functional types. Seasonal dynamics of the remotely sensed normalized difference vegetation index (NDVI) and remotely sensed land surface temperature from the Advanced Very High Resolution Radiometer (AVHRR) GIMMS 3g dataset were used to investigate constraints on the start of the growing season, although there was no indication of any spatially consistent temperature or day-length controls on greening onset. Because of the exponential nature of the relationship between soil temperature and nitrogen mineralization, temperature changes during the peak of the growing season had greater effects on vegetation productivity than changes earlier in the growing season. However, early season changes in temperature had a greater effect on the relative productivities of different plant functional types, with potential influences on species composition.

  17. Stressor Diversity: Introduction and Empirical Integration into the Daily Stress Model

    PubMed Central

    Koffer, Rachel E.; Ram, Nilam; Conroy, David E.; Pincus, Aaron L.; Almeida, David M.

    2016-01-01

    The present study examined if and how stressor diversity, the extent to which one’s stressor events are spread across multiple types of stressors, contributes to daily affective well-being through the adult life span. Stressor diversity was examined as a unique predictor of daily affect and as a moderator of stressor exposure and stressor reactivity effects. Analyses span two independent studies of daily stress: the National Study of Daily Experiences with N=2,022 adults, aged 33–85, assessed over T=8 days, and the Intraindividual Study of Affect, Health, and Interpersonal Behavior with N=150 adults, aged 18–89, assessed over T=63 days. Across both studies, older age was associated with less stressor diversity. Additionally, multivariate multilevel models indicated higher stressor diversity was linked with better affective well-being. Age, however, was not a consistent moderator of such associations. The combination of low stressor diversity and high stressor exposure is discussed as an operationalization of chronic stressors, and this combination was associated with particularly high negative affect and low positive affect. We believe further work will benefit from including both the frequency and diversity of stressor experiences in analyses in order to better characterize individuals’ stressor experiences. PMID:27294713

  18. Stressor diversity: Introduction and empirical integration into the daily stress model.

    PubMed

    Koffer, Rachel E; Ram, Nilam; Conroy, David E; Pincus, Aaron L; Almeida, David M

    2016-06-01

    The present study examined whether and how stressor diversity, the extent to which stressor events are spread across multiple types of stressors, contributes to daily affective well-being through the adult life span. Stressor diversity was examined as a unique predictor of daily affect and as a moderator of stressor exposure and stressor reactivity effects. Analyses span 2 independent studies of daily stress: the National Study of Daily Experiences with N = 2,022 adults, aged 33 to 85 years, assessed over T = 8 days, and the Intraindividual Study of Affect, Health, and Interpersonal Behavior with N = 150 adults, aged 18 to 89 years, assessed over T = 63 days. Across both studies, older age was associated with less stressor diversity. Additionally, multivariate multilevel models indicated higher stressor diversity was linked with better affective well-being. Age, however, was not a consistent moderator of such associations. The combination of low stressor diversity and high stressor exposure is discussed as an operationalization of chronic stressors, and this combination was associated with particularly high negative affect and low positive affect. We believe further work will benefit from including both the frequency and diversity of stressor experiences in analyses in order to better characterize individuals' stressor experiences. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  19. Quantum spatial-periodic harmonic model for daily price-limited stock markets

    NASA Astrophysics Data System (ADS)

    Meng, Xiangyi; Zhang, Jian-Wei; Xu, Jingjing; Guo, Hong

    2015-11-01

    We investigate the behaviors of stocks in daily price-limited stock markets by purposing a quantum spatial-periodic harmonic model. The stock price is considered to be oscillating and damping in a quantum spatial-periodic harmonic oscillator potential well. A complicated non-linear relation including inter-band positive correlation and intra-band negative correlation between the volatility and trading volume of a stock is numerically derived with the energy band structure of the model concerned. The effectiveness of price limit is re-examined, with some observed characteristics of price-limited stock markets in China studied by applying our quantum model.

  20. Development of Daily Maximum Flare-Flux Forecast Models for Strong Solar Flares

    NASA Astrophysics Data System (ADS)

    Shin, Seulki; Lee, Jin-Yi; Moon, Yong-Jae; Chu, Hyoungseok; Park, Jongyeob

    2016-03-01

    We have developed a set of daily maximum flare-flux forecast models for strong flares (M- and X-class) using multiple linear regression (MLR) and artificial neural network (ANN) methods. Our input parameters are solar-activity data from January 1996 to December 2013 such as sunspot area, X-ray maximum, and weighted total flare flux of the previous day, as well as mean flare rates of McIntosh sunspot group (Zpc) and Mount Wilson magnetic classifications. For a training dataset, we used 61 events each of C-, M-, and X-class from January 1996 to December 2004. For a testing dataset, we used all events from January 2005 to November 2013. A comparison between our maximum flare-flux models and NOAA model based on true skill statistics (TSS) shows that the MLR model for X-class and the average of all flares (M{+}X-class) are much better than the NOAA model. According to the hitting fraction (HF), which is defined as a fraction of events satisfying the condition that the absolute differences of predicted and observed flare flux on a logarithm scale are smaller than or equal to 0.5, our models successfully forecast the maximum flare flux of about two-thirds of the events for strong flares. Since all input parameters for our models are easily available, the models can be operated steadily and automatically on a daily basis for space-weather services.

  1. A regional neural network model for predicting mean daily river water temperature

    USGS Publications Warehouse

    Wagner, Tyler; DeWeber, Jefferson Tyrell

    2014-01-01

    Water temperature is a fundamental property of river habitat and often a key aspect of river resource management, but measurements to characterize thermal regimes are not available for most streams and rivers. As such, we developed an artificial neural network (ANN) ensemble model to predict mean daily water temperature in 197,402 individual stream reaches during the warm season (May–October) throughout the native range of brook trout Salvelinus fontinalis in the eastern U.S. We compared four models with different groups of predictors to determine how well water temperature could be predicted by climatic, landform, and land cover attributes, and used the median prediction from an ensemble of 100 ANNs as our final prediction for each model. The final model included air temperature, landform attributes and forested land cover and predicted mean daily water temperatures with moderate accuracy as determined by root mean squared error (RMSE) at 886 training sites with data from 1980 to 2009 (RMSE = 1.91 °C). Based on validation at 96 sites (RMSE = 1.82) and separately for data from 2010 (RMSE = 1.93), a year with relatively warmer conditions, the model was able to generalize to new stream reaches and years. The most important predictors were mean daily air temperature, prior 7 day mean air temperature, and network catchment area according to sensitivity analyses. Forest land cover at both riparian and catchment extents had relatively weak but clear negative effects. Predicted daily water temperature averaged for the month of July matched expected spatial trends with cooler temperatures in headwaters and at higher elevations and latitudes. Our ANN ensemble is unique in predicting daily temperatures throughout a large region, while other regional efforts have predicted at relatively coarse time steps. The model may prove a useful tool for predicting water temperatures in sampled and unsampled rivers under current conditions and future projections of climate

  2. Rainfall variability and extremes over southern Africa: assessment of a climate model to reproduce daily extremes

    NASA Astrophysics Data System (ADS)

    Williams, C.; Kniveton, D.; Layberry, R.

    2009-04-01

    It is increasingly accepted that that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The subcontinent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability and the identification of rainfall extremes is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. The majority of previous climate model verification studies have compared model output with observational data at monthly timescales. In this research, the assessment of ability of a state of the art climate model to simulate climate at daily timescales is carried out using satellite derived rainfall data from the Microwave Infra-Red Algorithm (MIRA). This dataset covers the period from 1993-2002 and the whole of southern Africa at a spatial resolution of 0.1 degree longitude/latitude. The ability of a climate model to simulate current climate provides some indication of how much confidence can be applied to its future predictions. In this paper, simulations of current climate from the UK Meteorological Office Hadley Centre's climate model, in both regional and global mode, are firstly compared to the MIRA dataset at daily timescales. This concentrates primarily on the ability of the model to simulate the spatial and temporal patterns of rainfall variability over southern Africa. Secondly, the ability of the model to reproduce daily rainfall extremes will

  3. Daily Dosing of Rifapentine Cures Tuberculosis in Three Months or Less in the Murine Model

    PubMed Central

    Rosenthal, Ian M; Zhang, Ming; Williams, Kathy N; Peloquin, Charles A; Tyagi, Sandeep; Vernon, Andrew A; Bishai, William R; Chaisson, Richard E; Grosset, Jacques H; Nuermberger, Eric L

    2007-01-01

    Background Availability of an ultra-short-course drug regimen capable of curing patients with tuberculosis in 2 to 3 mo would significantly improve global control efforts. Because immediate prospects for novel treatment-shortening drugs remain uncertain, we examined whether better use of existing drugs could shorten the duration of treatment. Rifapentine is a long-lived rifamycin derivative currently recommended only in once-weekly continuation-phase regimens. Moxifloxacin is an 8-methoxyfluoroquinolone currently used in second-line regimens. Methods and Findings Using a well-established mouse model with a high bacterial burden and human-equivalent drug dosing, we compared the efficacy of rifapentine- and moxifloxacin-containing regimens with that of the standard daily short-course regimen based on rifampin, isoniazid, and pyrazinamide. Bactericidal activity was assessed by lung colony-forming unit counts, and sterilizing activity was assessed by the proportion of mice with culture-positive relapse after 2, 3, 4, and 6 mo of treatment. Here, we demonstrate that replacing rifampin with rifapentine and isoniazid with moxifloxacin dramatically increased the activity of the standard daily regimen. After just 2 mo of treatment, mice receiving rifapentine- and moxifloxacin-containing regimens were found to have negative lung cultures, while those given the standard regimen still harbored 3.17 log10 colony-forming units in the lungs (p < 0.01). No relapse was observed after just 3 mo of treatment with daily and thrice-weekly administered rifapentine- and moxifloxacin-containing regimens, whereas the standard daily regimen required 6 mo to prevent relapse in all mice. Conclusions Rifapentine should no longer be viewed solely as a rifamycin for once-weekly administration. Our results suggest that treatment regimens based on daily and thrice-weekly administration of rifapentine and moxifloxacin may permit shortening the current 6 mo duration of treatment to 3 mo or less

  4. Impact of intra-daily SST variability on ENSO characteristics in a coupled model

    NASA Astrophysics Data System (ADS)

    Masson, Sébastien; Terray, Pascal; Madec, Gurvan; Luo, Jing-Jia; Yamagata, Toshio; Takahashi, Keiko

    2012-08-01

    This paper explores the impact of intra-daily Sea Surface Temperature (SST) variability on the tropical large-scale climate variability and differentiates it from the response of the system to the forcing of the solar diurnal cycle. Our methodology is based on a set of numerical experiments based on a fully global coupled ocean-atmosphere general circulation in which we alter (1) the frequency at which the atmosphere sees the SST variations and (2) the amplitude of the SST diurnal cycle. Our results highlight the complexity of the scale interactions existing between the intra-daily and inter-annual variability of the tropical climate system. Neglecting the SST intra-daily variability results, in our CGCM, to a systematic decrease of 15% of El Niño—Southern Oscillation (ENSO) amplitude. Furthermore, ENSO frequency and skewness are also significantly modified and are in better agreement with observations when SST intra-daily variability is directly taken into account in the coupling interface of our CGCM. These significant modifications of the SST interannual variability are not associated with any remarkable changes in the mean state or the seasonal variability. They can therefore not be explained by a rectification of the mean state as usually advocated in recent studies focusing on the diurnal cycle and its impact. Furthermore, we demonstrate that SST high frequency coupling is systematically associated with a strengthening of the air-sea feedbacks involved in ENSO physics: SST/sea level pressure (or Bjerknes) feedback, zonal wind/heat content (or Wyrtki) feedback, but also negative surface heat flux feedbacks. In our model, nearly all these results (excepted for SST skewness) are independent of the amplitude of the SST diurnal cycle suggesting that the systematic deterioration of the air-sea coupling by a daily exchange of SST information is cascading toward the major mode of tropical variability, i.e. ENSO.

  5. Modelling daily soil respiration in lowland oak forest during and after soil water saturation events

    NASA Astrophysics Data System (ADS)

    Marjanovic, Hrvoje; Zorana Ostrogovic, Masa; Alberti, Giorgio; Peressotti, Alessandro

    2013-04-01

    Lowland forests of pedunculate oak (Quercus robur L.) in Croatia are acclimated to high soil water content and flooding during the cold part of the year (November - March). Changes in weather pattern and increasing frequency of extreme events, like high precipitation episodes or flooding events out of dormancy period, are becoming more likely. However, the response of these forest ecosystems to flooding during vegetation period is not well investigated. It is well known that soil respiration (SR) depends on soil water content. Nevertheless, some of the most popular daily time-step SR models, like the one of Reichstein et al (2003), do not take into account the effects of soil water saturation which leads to hypoxia in soil and decline of SR. Therefore, we propose a modification of the SR model of Reichstein et. al (2003) that takes into account the effects of high soil water content on SR. In a 37 years old forest of pedunculate oak, located in Jastrebarsko forest (N45.619, E15.688), we measured soil CO2 efflux, every four hours during years 2009 and 2010, using a closed dynamic system with 2-4 chambers. Measured effluxes were averaged to obtain a daily average CO2 efflux. Measurements have shown that high soil water content (i.e. greater that field capacity) strongly decreases soil CO2 efflux, while subsequent soil draining produces bursts in efflux, particularly in spring. Assuming that the measured CO2 efflux corresponds to the total SR, we parameterized the original Reichstein et al (2003) daily time-step SR model and models based on the original but with different modifications (added seasonality in LAI term, modification in soil water status term, addition of new pulse term due to soil draining) and their combinations. Performance of each model was assessed using standard statistical measures (R2, RMSE, Mean Absolute Error, Nash-Sutcliffe Efficiency). Modification of soil water status term significantly improved daily SR estimate (RMSE 0.67) compared to

  6. Spatial modeling of the highest daily maximum temperature in Korea via max-stable processes

    NASA Astrophysics Data System (ADS)

    Lee, Youngsaeng; Yoon, Sanghoo; Murshed, Md. Sharwar; Kim, Maeng-Ki; Cho, ChunHo; Baek, Hee-Jeong; Park, Jeong-Soo

    2013-11-01

    This paper examines the annual highest daily maximum temperature (DMT) in Korea by using data from 56 weather stations and employing spatial extreme modeling. Our approach is based on max-stable processes (MSP) with Schlather’s characterization. We divide the country into four regions for a better model fit and identify the best model for each region. We show that regional MSP modeling is more suitable than MSP modeling for the entire region and the pointwise generalized extreme value distribution approach. The advantage of spatial extreme modeling is that more precise and robust return levels and some indices of the highest temperatures can be obtained for observation stations and for locations with no observed data, and so help to determine the effects and assessment of vulnerability as well as to downscale extreme events.

  7. Daily diaries and minority adolescents: random coefficient regression modeling of attributional style, coping, and affect.

    PubMed

    Roesch, Scott C; Vaughn, Allison A; Aldridge, Arianna A; Villodas, Feion

    2009-10-01

    Many researchers underscore the importance of coping in the daily lives of adolescents, yet very few studies measure this and related constructs at this level. Using a daily diary approach to stress and coping, the current study evaluated a series of mediational coping models in a sample of low-income minority adolescents (N = 89). Specifically, coping was hypothesized to mediate the relationship between attributional style (and dimensions) and daily affect. Using random coefficient regression modeling, the relationship between (a) the locus of causality dimension and positive affect was completely mediated by the use of acceptance and humor as coping strategies; (b) the stability dimension and positive affect was completely mediated by the use of both problem-solving and positive thinking; and (c) the stability dimension and negative affect was partially mediated by the use of religious coping. In addition, the locus of causality and stability (but not globality) dimensions were also directly related to affect. However, the relationship between pessimistic explanatory style and affect was not mediated by coping. Consistent with previous research, these findings suggest that attributions are both directly and indirectly related to indices of affect or adjustment. Thus, attributions may not only influence the type of coping strategy employed, but may also serve as coping strategies themselves.

  8. Climate change uncertainty for daily minimum and maximum temperatures: a model inter-comparison

    SciTech Connect

    Lobell, D; Bonfils, C; Duffy, P

    2006-11-09

    Several impacts of climate change may depend more on changes in mean daily minimum (T{sub min}) or maximum (T{sub max}) temperatures than daily averages. To evaluate uncertainties in these variables, we compared projections of T{sub min} and T{sub max} changes by 2046-2065 for 12 climate models under an A2 emission scenario. Average modeled changes in T{sub max} were slightly lower in most locations than T{sub min}, consistent with historical trends exhibiting a reduction in diurnal temperature ranges. However, while average changes in T{sub min} and T{sub max} were similar, the inter-model variability of T{sub min} and T{sub max} projections exhibited substantial differences. For example, inter-model standard deviations of June-August T{sub max} changes were more than 50% greater than for T{sub min} throughout much of North America, Europe, and Asia. Model differences in cloud changes, which exert relatively greater influence on T{sub max} during summer and T{sub min} during winter, were identified as the main source of uncertainty disparities. These results highlight the importance of considering separately projections for T{sub max} and T{sub min} when assessing climate change impacts, even in cases where average projected changes are similar. In addition, impacts that are most sensitive to summertime T{sub min} or wintertime T{sub max} may be more predictable than suggested by analyses using only projections of daily average temperatures.

  9. The role of patients' explanatory models and daily-lived experience in hypertension self-management.

    PubMed

    Bokhour, Barbara G; Cohn, Ellen S; Cortés, Dharma E; Solomon, Jeffrey L; Fix, Gemmae M; Elwy, A Rani; Mueller, Nora; Katz, Lois A; Haidet, Paul; Green, Alexander R; Borzecki, Ann M; Kressin, Nancy R

    2012-12-01

    Uncontrolled hypertension remains a significant problem for many patients. Few interventions to improve patients' hypertension self-management have had lasting effects. Previous work has focused largely on patients' beliefs as predictors of behavior, but little is understood about beliefs as they are embedded in patients' social contexts. This study aims to explore how patients' "explanatory models" of hypertension (understandings of the causes, mechanisms or pathophysiology, course of illness, symptoms and effects of treatment) and social context relate to their reported daily hypertension self-management behaviors. Semi-structured qualitative interviews with a diverse group of patients at two large urban Veterans Administration Medical centers. PARTICIPANTS (OR PATIENTS OR SUBJECTS): African-American, white and Latino Veterans Affairs (VA) primary care patients with uncontrolled blood pressure. We conducted thematic analysis using tools of grounded theory to identify key themes surrounding patients' explanatory models, social context and hypertension management behaviors. Patients' perceptions of the cause and course of hypertension, experiences of hypertension symptoms, and beliefs about the effectiveness of treatment were related to different hypertension self-management behaviors. Moreover, patients' daily-lived experiences, such as an isolated lifestyle, serious competing health problems, a lack of habits and routines, barriers to exercise and prioritizing lifestyle choices, also interfered with optimal hypertension self-management. Designing interventions to improve patients' hypertension self-management requires consideration of patients' explanatory models and their daily-lived experience. We propose a new conceptual model - the dynamic model of hypertension self-management behavior - which incorporates these key elements of patients' experiences.

  10. The effect of reproductive performance on the dairy cattle herd value assessed by integrating a daily dynamic programming model with a daily Markov chain model.

    PubMed

    Kalantari, A S; Cabrera, V E

    2012-10-01

    The objective of this study was to determine the effect of reproductive performance on dairy cattle herd value. Herd value was defined as the herd's average retention payoff (RPO). Individual cow RPO is the expected profit from keeping the cow compared with immediate replacement. First, a daily dynamic programming model was developed to calculate the RPO of all cow states in a herd. Second, a daily Markov chain model was applied to estimate the herd demographics. Finally, the herd value was calculated by aggregating the RPO of all cows in the herd. Cow states were described by 5 milk yield classes (76, 88, 100, 112, and 124% with respect to the average), 9 lactations, 750 d in milk, and 282 d in pregnancy. Five different reproductive programs were studied (RP1 to RP5). Reproductive program 1 used 100% timed artificial insemination (TAI; 42% conception rate for first TAI and 30% for second and later services) and the other programs combined TAI with estrus detection. The proportion of cows receiving artificial insemination after estrus detection ranged from 30 to 80%, and conception rate ranged from 25 to 35%. These 5 reproductive programs were categorized according to their 21-d pregnancy rate (21-d PR), which is an indication of the rate that eligible cows become pregnant every 21 d. The 21-d PR was 17% for RP1, 14% for RP2, 16% for RP3, 18% for RP4, and 20% for RP5. Results showed a positive relationship between 21-d PR and herd value. The most extreme herd value difference between 2 reproductive programs was $77/cow per yr for average milk yield (RP5 - RP2), $13/cow per yr for lowest milk yield (RP5 - RP1), and $160/cow per yr for highest milk yield (RP5 - RP2). Reproductive programs were ranked based on their calculated herd value. With the exception of the best reproductive program (RP5), all other programs showed some level of ranking change according to milk yield. The most dramatic ranking change was observed in RP1, which moved from being the worst ranked

  11. Selection of a mathematical model to generate lactation curves using daily milk yields of Holstein cows.

    PubMed

    Sherchand, L; McNew, R W; Kellogg, D W; Johnson, Z B

    1995-11-01

    Mathematical descriptions of early stages of lactation were investigated using daily milk yields of 117 first, 78 second, 57 third, and 36 fourth lactations of 120 Holstein cows fitted by 10 models. The measure of fit was the error mean squares, which were replaced by ranks to perform an analysis of variance with lactation number, model, and period as factors and with cows as replicates. The interaction of model and lactation number was significant for the fit of the entire lactation. A significant interaction of model and period was obtained for the fit of three 30-d intervals. For the entire lactation, the best fit for all four lactations occurred from the diphasic logistic function, y = d1(1-tanh2(b1(nk-c1))) + d2(1-tanh2(b2(n-c2))). For the first 30 d, a modified gamma function gave the best fit for the first lactation, the inverse polynomial function for the second lactation, and the quadratic log function for the third lactation. The diphasic logistic function gave the best fit for the remaining two periods and was not significantly different from the best fitting models for the first 30-d period. Hence, this function may be useful to describe the lactation curve of Holstein cows for dairy herds in which the daily milk yield of individual cows is constantly monitored with a computer.

  12. Regression model for generating time series of daily precipitation amounts for climate change impact studies

    NASA Astrophysics Data System (ADS)

    Buishand, T. A.; Klein Tank, A. M. G.

    1996-05-01

    The precipitation amounts on wet days at De Bilt (the Netherlands) are linked to temperature and surface air pressure through advanced regression techniques. Temperature is chosen as a covariate to use the model for generating synthetic time series of daily precipitation in a CO2 induced warmer climate. The precipitation-temperature dependence can partly be ascribed to the phenomenon that warmer air can contain more moisture. Spline functions are introduced to reproduce the non-monotonous change of the mean daily precipitation amount with temperature. Because the model is non-linear and the variance of the errors depends on the expected response, an iteratively reweighted least-squares technique is needed to estimate the regression coefficients. A representative rainfall sequence for the situation of a systematic temperature rise is obtained by multiplying the precipitation amounts in the observed record with a temperature dependent factor based on a fitted regression model. For a temperature change of 3°C (reasonable guess for a doubled CO2 climate according to the present-day general circulation models) this results in an increase in the annual average amount of 9% (20% in winter and 4% in summer). An extended model with both temperature and surface air pressure is presented which makes it possible to study the additional effects of a potential systematic change in surface air pressure on precipitation.

  13. Daily reservoir inflow forecasting using multiscale deep feature learning with hybrid models

    NASA Astrophysics Data System (ADS)

    Bai, Yun; Chen, Zhiqiang; Xie, Jingjing; Li, Chuan

    2016-01-01

    Inflow forecasting applies data supports for the operations and managements of reservoirs. A multiscale deep feature learning (MDFL) method with hybrid models is proposed in this paper to deal with the daily reservoir inflow forecasting. Ensemble empirical mode decomposition and Fourier spectrum are first employed to extract multiscale (trend, period and random) features, which are then represented by three deep belief networks (DBNs), respectively. The weights of each DBN are subsequently applied to initialize a neural network (D-NN). The outputs of the three-scale D-NNs are finally reconstructed using a sum-up strategy toward the forecasting results. A historical daily inflow series (from 1/1/2000 to 31/12/2012) of the Three Gorges reservoir, China, is investigated by the proposed MDFL with hybrid models. For comparison, four peer models are adopted for the same task. The results show that, the present model overwhelms all the peer models in terms of mean absolute percentage error (MAPE = 11.2896%), normalized root-mean-square error (NRMSE = 0.2292), determination coefficient criteria (R2 = 0.8905), and peak percent threshold statistics (PPTS(5) = 10.0229%). The addressed method integrates the deep framework with multiscale and hybrid observations, and therefore being good at exploring sophisticated natures in the reservoir inflow forecasting.

  14. LiveOcean: A Daily Forecast Model of Ocean Acidification for Shellfish Growers

    NASA Astrophysics Data System (ADS)

    MacCready, P.; Siedlecki, S. A.; McCabe, R. M.

    2016-12-01

    The coastal estuaries of the NE Pacific host a highly productive shellfish industry, but in the past decade they have suffered from many years in which no natural set of oysters occurred. It appears that coastal waters with low Aragonite saturation state may be the cause. This "acidified" water is the result of (i) upwelling of NE Pacific water from near the shelf break that is already low in pH, and (ii) further acidification of that water by productivity and remineralization on the shelf, and (iii) increasing atmospheric CO2. As part of a coordinated research response to this issue, we have developed the LiveOcean modeling system, which creates daily three-day forecasts of circulation and biogeochemical properties in Oregon-Washington-British Columbia coastal and estuarine waters. The system includes realistic tides, atmospheric forcing (from a regional WRF model), ocean boundary conditions (from HYCOM), and rivers (from USGS and Environment Canada). The model is also used for Harmful Algal Bloom prediction. There has been extensive validation of hindcast runs for currents and hydrography, and more limited validation of biogeochemical variables. Model results are pushed daily to the cloud, and made available to the public through the NANOOS Visualization System (NVS). NVS also includes automated model-data comparisons with real-time NDBC and OOI moorings. Future work will focus on optimizing the utility of this system for regional shellfish growers.

  15. Development of Daily Solar Maximum Flare Flux Forecast Models for Strong Flares

    NASA Astrophysics Data System (ADS)

    Shin, Seulki; Chu, Hyoungseok

    2015-08-01

    We have developed a set of daily solar maximum flare flux forecast models for strong flares using Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) methods. We consider input parameters as solar activity data from January 1996 to December 2013 such as sunspot area, X-ray maximum flare flux and weighted total flux of the previous day, and mean flare rates of McIntosh sunspot group (Zpc) and Mount Wilson magnetic classification. For a training data set, we use the same number of 61 events for each C-, M-, and X-class from Jan. 1996 to Dec. 2004, while other previous models use all flares. For a testing data set, we use all flares from Jan. 2005 to Nov. 2013. The statistical parameters from contingency tables show that the ANN models are better for maximum flare flux forecasting than the MLR models. A comparison between our maximum flare flux models and the previous ones based on Heidke Skill Score (HSS) shows that our all models for X-class flare are much better than the other models. According to the Hitting Fraction (HF), which is defined as a fraction of events satisfying that the absolute differences of predicted and observed flare flux in logarithm scale are less than equal to 0.5, our models successfully forecast the maximum flare flux of about two-third events for strong flares. Since all input parameters for our models are easily available, the models can be operated steadily and automatically on daily basis for space weather service.

  16. A Multivariate model for Monte-Carlo Simulation of Spatially and Temporally Correlated Daily Rainfall Intensities

    NASA Astrophysics Data System (ADS)

    Mok, C. M.; Suribhatla, R. M.; Wanakule, N.; Zhang, M.

    2009-12-01

    A reliability-based water resources management framework has been developed by AMEC Geomatrix over the last few years to optimally manage a water supply system that serves over two million people in the northern Tampa Bay region in Florida, USA, while protecting wetland health and preventing seawater intrusion. The framework utilizes stochastic optimization techniques to account for uncertainties associated with the prediction of water demand, surface water availability, baseline groundwater levels, a non-anthropogenic reservoir water budget, and hydrological/hydrogeological properties. Except for the hydro¬geological properties, these uncertainties are partially caused by uncertainties in future rainfall patterns in the region. We present here a novel multivariate statistical model of rainfall and a methodology for generating Monte-Carlo realizations based on the statistical model. The model is intended to capture spatial-temporal characteristics of daily rainfall intensity in 172 basins in the northern Tampa Bay region and is characterized by its high dimensionality. Daily rainfall intensity in each basin is expressed as product of a binary random variable (RV) corresponding to the event of rain and a continuous RV representing the amount of rain. For the binary RVs we use a bivariate transformation technique to generate the Monte-Carlo realizations that form the basis for sequential simulation of the continuous RVs. A non-parametric Gaussian copula is used to develop the multivariate model for continuous RVs. This methodology captures key spatial and temporal characteristics of daily rainfall intensities and overcomes numerical issues posed by high-dimensionality of the Gaussian copula.

  17. Prospective Measurement of Daily Health Behaviors: Modeling Temporal Patterns in Missing Data, Sexual Behavior, and Substance Use in an Online Daily Diary Study of Gay and Bisexual Men.

    PubMed

    Rendina, H Jonathon; Ventuneac, Ana; Mustanski, Brian; Grov, Christian; Parsons, Jeffrey T

    2016-08-01

    Daily diary and other intensive longitudinal methods are increasingly being used to investigate fluctuations in psychological and behavioral processes. To inform the development of this methodology, we sought to explore predictors of and patterns in diary compliance and behavioral reports. We used multilevel modeling to analyze data from an online daily diary study of 371 gay and bisexual men focused on sexual behavior and substance use. We found that greater education and older age as well as lower frequency of substance use were associated with higher compliance. Using polynomial and trigonometric functions, we found evidence for circaseptan patterns in compliance, sexual behavior, and substance use, as well as linear declines in compliance and behavior over time. The results suggest potential sources of non-random patterns of missing data and suggest that trigonometric terms provide a similar but more parsimonious investigation of circaseptan rhythms than do third-order polynomial terms.

  18. Modeling and estimation of stage-specific daily survival probabilities of nests

    USGS Publications Warehouse

    Stanley, T.R.

    2000-01-01

    In studies of avian nesting success, it is often of interest to estimate stage-specific daily survival probabilities of nests. When data can be partitioned by nesting stage (e.g., incubation stage, nestling stage), piecewise application of the Mayfield method or Johnsona??s method is appropriate. However, when the data contain nests where the transition from one stage to the next occurred during the interval between visits, piecewise approaches are inappropriate. In this paper, I present a model that allows joint estimation of stage-specific daily survival probabilities even when the time of transition between stages is unknown. The model allows interval lengths between visits to nests to vary, and the exact time of failure of nests does not need to be known. The performance of the model at various sample sizes and interval lengths between visits was investigated using Monte Carlo simulations, and it was found that the model performed quite well: bias was small and confidence-interval coverage was at the nominal 95% rate. A SAS program for obtaining maximum likelihood estimates of parameters, and their standard errors, is provided in the Appendix.

  19. Incorporating Daily Flood Control Objectives Into a Monthly Stochastic Dynamic Programing Model for a Hydroelectric Complex

    NASA Astrophysics Data System (ADS)

    Druce, Donald J.

    1990-01-01

    A monthly stochastic dynamic programing model was recently developed and implemented at British Columbia (B.C.) Hydro to provide decision support for short-term energy exports and, if necessary, for flood control on the Peace River in northern British Columbia. The model establishes the marginal cost of supplying energy from the B.C. Hydro system, as well as a monthly operating policy for the G.M. Shrum and Peace Canyon hydroelectric plants and the Williston Lake storage reservoir. A simulation model capable of following the operating policy then determines the probability of refilling Williston Lake and possible spill rates and volumes. Reservoir inflows are input to both models in daily and monthly formats. The results indicate that flood control can be accommodated without sacrificing significant export revenue.

  20. Modeling daily variation of trihalomethane compounds in drinking water system, Houston, Texas.

    PubMed

    Chaib, Embarka; Moschandreas, Demetrios

    2008-03-01

    Total trihalomethanes (TTHM) concentrations vary widely and periodically between 70 and 130 ppb. Data from the National Environmental Services Laboratory, Houston, Texas indicate that pH and free residual chlorine contribute minimally to the wide variability of TTHM levels. Temperature variation in drinking fluctuates from 11 to 27 degrees C. The objective of this research is to formulate a model that delineates more clearly the daily variations of the most prevalent volatile trihalomethane by-products: chloroform (CHCl3), bromodichloromethane (CHBr2Cl), and bromoform (CHBr3) levels from drinking water. This model simulates the daily fluctuation of THM at a single location and at any time during the day as a function of the water temperature and the average concentration of TTHM, which can be estimated. The hypothesis of this study is that observed daily fluctuations of TTHM, CHCl3, CHCl2Br, CHClBr2, and CHBr3 are periodic. This hypothesis is tested using autocorrelation functions and it is shown that for the series of pH the correlation coefficient is maximal at zero lags, rapidly decreases to zero, and increases again between 4- and 6-h period. Such pattern suggests random fluctuation unrelated to time. However, the series of free residual chlorine, temperature, TTHM, CHCl3, CHCl2Br, CHClBr2, and CHBr3 suggest a different pattern. The correlation coefficient increases when the time-shift approaches 24 h. These repetitions in fluctuation of content over a 24-h period are statistically significant. The model formulated in this study provides insights in TTHM variation and is a necessary tool to reduce the error when estimating potential risk from exposure to trihalomethane compounds in drinking water system. In general, calculation of potential risk by using a value measured early morning or late afternoon concentrations were found minimal lead to an underestimation of the population risk.

  1. Daily stress, coping, and well-being in parents of children with autism: a multilevel modeling approach.

    PubMed

    Pottie, Colin G; Ingram, Kathleen M

    2008-12-01

    This study used a repeated daily measurement design to examine the direct and moderating effects of coping on daily psychological distress and well-being in parents of children with Autism Spectrum Disorders (ASD). Twice weekly over a 12-week period, 93 parents provided reports of their daily stress, coping responses, and end-of-day mood. Multilevel modeling analyses identified 5 coping responses (e.g., seeking support, positive reframing) that predicted increased daily positive mood and 4 (e.g., escape, withdrawal) that were associated with decreased positive mood. Similarly, 2 coping responses were associated with decreased daily negative mood and 5 predicted increased negative mood. The moderating effects of gender and the 11 coping responses were also examined. Gender did not moderate the daily coping?mood relationship, however 3 coping responses (emotional regulation, social support, and worrying) were found to moderate the daily stress?mood relationship. Additionally, ASD symptomatology, and time since an ASD diagnosis were not found to predict daily parental mood. This study is perhaps the first to identify coping responses that enhance daily well-being and mitigate daily distress in parents of children with ASD.

  2. Effect of Estimated Daily Global Solar Radiation Data on the Results of Crop Growth Models

    PubMed Central

    Trnka, Miroslav; Eitzinger, Josef; Kapler, Pavel; Dubrovský, Martin; Semerádová, Daniela; Žalud, Zden ěk; Formayer, Herbert

    2007-01-01

    The results of previous studies have suggested that estimated daily global radiation (RG) values contain an error that could compromise the precision of subsequent crop model applications. The following study presents a detailed site and spatial analysis of the RG error propagation in CERES and WOFOST crop growth models in Central European climate conditions. The research was conducted i) at the eight individual sites in Austria and the Czech Republic where measured daily RG values were available as a reference, with seven methods for RG estimation being tested, and ii) for the agricultural areas of the Czech Republic using daily data from 52 weather stations, with five RG estimation methods. In the latter case the RG values estimated from the hours of sunshine using the Ångström-Prescott formula were used as the standard method because of the lack of measured RG data. At the site level we found that even the use of methods based on hours of sunshine, which showed the lowest bias in RG estimates, led to a significant distortion of the key crop model outputs. When the Ångström-Prescott method was used to estimate RG, for example, deviations greater than ±10 per cent in winter wheat and spring barley yields were noted in 5 to 6 per cent of cases. The precision of the yield estimates and other crop model outputs was lower when RG estimates based on the diurnal temperature range and cloud cover were used (mean bias error 2.0 to 4.1 per cent). The methods for estimating RG from the diurnal temperature range produced a wheat yield bias of more than 25 per cent in 12 to 16 per cent of the seasons. Such uncertainty in the crop model outputs makes the reliability of any seasonal yield forecasts or climate change impact assessments questionable if they are based on this type of data. The spatial assessment of the RG data uncertainty propagation over the winter wheat yields also revealed significant differences within the study area. We found that RG estimates based on

  3. Effect of Estimated Daily Global Solar Radiation Data on the Results of Crop Growth Models.

    PubMed

    Trnka, Miroslav; Eitzinger, Josef; Kapler, Pavel; Dubrovský, Martin; Semerádová, Daniela; Žalud, Zdeněk; Formayer, Herbert

    2007-10-16

    The results of previous studies have suggested that estimated daily globalradiation (RG) values contain an error that could compromise the precision of subsequentcrop model applications. The following study presents a detailed site and spatial analysis ofthe RG error propagation in CERES and WOFOST crop growth models in Central Europeanclimate conditions. The research was conducted i) at the eight individual sites in Austria andthe Czech Republic where measured daily RG values were available as a reference, withseven methods for RG estimation being tested, and ii) for the agricultural areas of the CzechRepublic using daily data from 52 weather stations, with five RG estimation methods. In thelatter case the RG values estimated from the hours of sunshine using the ångström-Prescottformula were used as the standard method because of the lack of measured RG data. At thesite level we found that even the use of methods based on hours of sunshine, which showedthe lowest bias in RG estimates, led to a significant distortion of the key crop model outputs.When the ångström-Prescott method was used to estimate RG, for example, deviationsgreater than ±10 per cent in winter wheat and spring barley yields were noted in 5 to 6 percent of cases. The precision of the yield estimates and other crop model outputs was lowerwhen RG estimates based on the diurnal temperature range and cloud cover were used (mean bias error 2.0 to 4.1 per cent). The methods for estimating RG from the diurnal temperature range produced a wheat yield bias of more than 25 per cent in 12 to 16 per cent of the seasons. Such uncertainty in the crop model outputs makes the reliability of any seasonal yield forecasts or climate change impact assessments questionable if they are based on this type of data. The spatial assessment of the RG data uncertainty propagation over the winter wheat yields also revealed significant differences within the study area. We found that RG estimates based on diurnal

  4. Rainfall variability and extremes over southern Africa: Assessment of a climate model to reproduce daily extremes

    NASA Astrophysics Data System (ADS)

    Williams, C. J. R.; Kniveton, D. R.; Layberry, R.

    2009-04-01

    It is increasingly accepted that that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The subcontinent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability and the identification of rainfall extremes is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. The majority of previous climate model verification studies have compared model output with observational data at monthly timescales. In this research, the assessment of ability of a state of the art climate model to simulate climate at daily timescales is carried out using satellite derived rainfall data from the Microwave Infra-Red Algorithm (MIRA). This dataset covers the period from 1993-2002 and the whole of southern Africa at a spatial resolution of 0.1 degree longitude/latitude. The ability of a climate model to simulate current climate provides some indication of how much confidence can be applied to its future predictions. In this paper, simulations of current climate from the UK Meteorological Office Hadley Centre's climate model, in both regional and global mode, are firstly compared to the MIRA dataset at daily timescales. This concentrates primarily on the ability of the model to simulate the spatial and temporal patterns of rainfall variability over southern Africa. Secondly, the ability of the model to reproduce daily rainfall extremes will

  5. Simulation And Forecasting of Daily Pm10 Concentrations Using Autoregressive Models In Kagithane Creek Valley, Istanbul

    NASA Astrophysics Data System (ADS)

    Ağaç, Kübra; Koçak, Kasım; Deniz, Ali

    2015-04-01

    A time series approach using autoregressive model (AR), moving average model (MA) and seasonal autoregressive integrated moving average model (SARIMA) were used in this study to simulate and forecast daily PM10 concentrations in Kagithane Creek Valley, Istanbul. Hourly PM10 concentrations have been measured in Kagithane Creek Valley between 2010 and 2014 periods. Bosphorus divides the city in two parts as European and Asian parts. The historical part of the city takes place in Golden Horn. Our study area Kagithane Creek Valley is connected with this historical part. The study area is highly polluted because of its topographical structure and industrial activities. Also population density is extremely high in this site. The dispersion conditions are highly poor in this creek valley so it is necessary to calculate PM10 levels for air quality and human health. For given period there were some missing PM10 concentration values so to make an accurate calculations and to obtain exact results gap filling method was applied by Singular Spectrum Analysis (SSA). SSA is a new and efficient method for gap filling and it is an state-of-art modeling. SSA-MTM Toolkit was used for our study. SSA is considered as a noise reduction algorithm because it decomposes an original time series to trend (if exists), oscillatory and noise components by way of a singular value decomposition. The basic SSA algorithm has stages of decomposition and reconstruction. For given period daily and monthly PM10 concentrations were calculated and episodic periods are determined. Long term and short term PM10 concentrations were analyzed according to European Union (EU) standards. For simulation and forecasting of high level PM10 concentrations, meteorological data (wind speed, pressure and temperature) were used to see the relationship between daily PM10 concentrations. Fast Fourier Transformation (FFT) was also applied to the data to see the periodicity and according to these periods models were built

  6. A hybrid orographic plus statistical model for downscaling daily precipitation in Northern California

    USGS Publications Warehouse

    Pandey, G.R.; Cayan, D.R.; Dettinger, M.D.; Georgakakos, K.P.

    2000-01-01

    A hybrid (physical-statistical) scheme is developed to resolve the finescale distribution of daily precipitation over complex terrain. The scheme generates precipitation by combining information from the upper-air conditions and from sparsely distributed station measurements; thus, it proceeds in two steps. First, an initial estimate of the precipitation is made using a simplified orographic precipitation model. It is a steady-state, multilayer, and two-dimensional model following the concepts of Rhea. The model is driven by the 2.5?? ?? 2.5?? gridded National Oceanic and Atmospheric Administration-National Centers for Environmental Prediction upper-air profiles, and its parameters are tuned using the observed precipitation structure of the region. Precipitation is generated assuming a forced lifting of the air parcels as they cross the mountain barrier following a straight trajectory. Second, the precipitation is adjusted using errors between derived precipitation and observations from nearby sites. The study area covers the northern half of California, including coastal mountains, central valley, and the Sierra Nevada. The model is run for a 5-km rendition of terrain for days of January-March over the period of 1988-95. A jackknife analysis demonstrates the validity of the approach. The spatial and temporal distributions of the simulated precipitation field agree well with the observed precipitation. Further, a mapping of model performance indices (correlation coefficients, model bias, root-mean-square error, and threat scores) from an array of stations from the region indicates that the model performs satisfactorily in resolving daily precipitation at 5-km resolution.

  7. Modeling and forecasting foreign exchange daily closing prices with normal inverse Gaussian

    NASA Astrophysics Data System (ADS)

    Teneng, Dean

    2013-09-01

    We fit the normal inverse Gaussian(NIG) distribution to foreign exchange closing prices using the open software package R and select best models by Käärik and Umbleja (2011) proposed strategy. We observe that daily closing prices (12/04/2008 - 07/08/2012) of CHF/JPY, AUD/JPY, GBP/JPY, NZD/USD, QAR/CHF, QAR/EUR, SAR/CHF, SAR/EUR, TND/CHF and TND/EUR are excellent fits while EGP/EUR and EUR/GBP are good fits with a Kolmogorov-Smirnov test p-value of 0.062 and 0.08 respectively. It was impossible to estimate normal inverse Gaussian parameters (by maximum likelihood; computational problem) for JPY/CHF but CHF/JPY was an excellent fit. Thus, while the stochastic properties of an exchange rate can be completely modeled with a probability distribution in one direction, it may be impossible the other way around. We also demonstrate that foreign exchange closing prices can be forecasted with the normal inverse Gaussian (NIG) Lévy process, both in cases where the daily closing prices can and cannot be modeled by NIG distribution.

  8. Validation of Daily Rainfall Simulations from the Canadian Regional Climate Model over Indian Region

    NASA Astrophysics Data System (ADS)

    D, N.; C T, D.; Sushama, L.

    2011-12-01

    The impact of climate change on water resources is significant due to the close interactions between various climate variables and the hydrologic cycle. While global climate models (GCMs) are widely used tools for understanding of climate, regional climate models (RCMs) with their complete closed budget including both the atmospheric and land surface branches provide more detailed simulations of regional and local conditions. In the present study, a fifth generation of Canadian Regional Climate Model (CRCM5) is employed to obtain high-resolution (0.44°×0.44°) daily rainfall simulations over Indian region. Three simulations are obtained from CRCM5 with different lateral boundary conditions (LBC), the driving/ pilot data and soil layers. Results from these three simulations are compared with the 0.5°×0.5° resolution daily rainfall data over Indian region prepared by Indian Meteorological Department (IMD). Different skill scores are employed for the comparison. RCM simulations match reasonably well with the observed data for major part of the country, except for the high rainfall regions such as south-western and north-eastern parts of India.

  9. High-resolution spatial modeling of daily weather elements for a catchment in the Oregon Cascade Mountains, United States

    Treesearch

    Christopher Daly; Jonathan W. Smith; Joseph I. Smith; Robert B. McKane

    2007-01-01

    High-quality daily meteorological data at high spatial resolution are essential for a variety of hydrologic and ecological modeling applications that support environmental risk assessments and decisionmaking. This paper describes the development. application. and assessment of methods to construct daily high resolution (~50-m cell size) meteorological grids for the...

  10. [Estimation model for daily transpiration of greenhouse muskmelon in its vegetative growth period].

    PubMed

    Zhang, Da-Long; Li, Jian-Ming; Wu, Pu-Te; Li, Wei-Li; Zhao, Zhi-Hua; Xu, Fei; Li, Jun

    2013-07-01

    For developing an estimation method of muskmelon transpiration in greenhouse, an estimation model for the daily transpiration of greenhouse muskmelon in its vegetative growth period was established, based on the greenhouse environmental parameters, muskmelon growth and development parameters, and soil moisture parameters. According to the specific environment in greenhouse, the item of aerodynamics in Penman-Monteith equation was modified, and the greenhouse environmental sub-model suitable for calculating the reference crop evapotranspiration in greenhouse was deduced. The crop factor sub-model was established with the leaf area index as independent variable, and the form of the model was linear function. The soil moisture sub-model was established with the soil relative effective moisture content as independent variable, and the form of the model was logarithmic function. With interval sowing, the model parameters were estimated and analyzed, according to the measurement data of different sowing dates in a year. The prediction accuracy of the model for sufficient irrigation and water-saving irrigation was verified, according to measurement data when the relative soil moisture content was 80%, 70%, and 60%, and the mean relative error was 11.5%, 16.2% , and 16.9% respectively. The model was a beneficial exploration for the application of Penman-Monteith equation under greenhouse environment and water-saving irrigation, having good application foreground and popularization value.

  11. R-vine models for spatial time series with an application to daily mean temperature.

    PubMed

    Erhardt, Tobias Michael; Czado, Claudia; Schepsmeier, Ulf

    2015-06-01

    We introduce an extension of R-vine copula models to allow for spatial dependencies and model based prediction at unobserved locations. The proposed spatial R-vine model combines the flexibility of vine copulas with the classical geostatistical idea of modeling spatial dependencies using the distances between the variable locations. In particular, the model is able to capture non-Gaussian spatial dependencies. To develop and illustrate our approach, we consider daily mean temperature data observed at 54 monitoring stations in Germany. We identify relationships between the vine copula parameters and the station distances and exploit these in order to reduce the huge number of parameters needed to parametrize a 54-dimensional R-vine model fitted to the data. The new distance based model parametrization results in a distinct reduction in the number of parameters and makes parameter estimation and prediction at unobserved locations feasible. The prediction capabilities are validated using adequate scoring techniques, showing a better performance of the spatial R-vine copula model compared to a Gaussian spatial model.

  12. Stochastic Modeling based on Dictionary Approach for the Generation of Daily Precipitation Occurrences

    NASA Astrophysics Data System (ADS)

    Panu, U. S.; Ng, W.; Rasmussen, P. F.

    2009-12-01

    The modeling of weather states (i.e., precipitation occurrences) is critical when the historical data are not long enough for the desired analysis. Stochastic models (e.g., Markov Chain and Alternating Renewal Process (ARP)) of the precipitation occurrence processes generally assume the existence of short-term temporal-dependency between the neighboring states while implying the existence of long-term independency (randomness) of states in precipitation records. Existing temporal-dependent models for the generation of precipitation occurrences are restricted either by the fixed-length memory (e.g., the order of a Markov chain model), or by the reining states in segments (e.g., persistency of homogenous states within dry/wet-spell lengths of an ARP). The modeling of variable segment lengths and states could be an arduous task and a flexible modeling approach is required for the preservation of various segmented patterns of precipitation data series. An innovative Dictionary approach has been developed in the field of genome pattern recognition for the identification of frequently occurring genome segments in DNA sequences. The genome segments delineate the biologically meaningful ``words" (i.e., segments with a specific patterns in a series of discrete states) that can be jointly modeled with variable lengths and states. A meaningful “word”, in hydrology, can be referred to a segment of precipitation occurrence comprising of wet or dry states. Such flexibility would provide a unique advantage over the traditional stochastic models for the generation of precipitation occurrences. Three stochastic models, namely, the alternating renewal process using Geometric distribution, the second-order Markov chain model, and the Dictionary approach have been assessed to evaluate their efficacy for the generation of daily precipitation sequences. Comparisons involved three guiding principles namely (i) the ability of models to preserve the short-term temporal-dependency in

  13. Short-Course Therapy with Daily Rifapentine in a Murine Model of Latent Tuberculosis Infection

    PubMed Central

    Zhang, Tianyu; Zhang, Ming; Rosenthal, Ian M.; Grosset, Jacques H.; Nuermberger, Eric L.

    2009-01-01

    Rationale: Regimens recommended to treat latent tuberculosis infection (LTBI) are 3 to 9 months long. A 2-month rifampin+pyrazinamide regimen is no longer recommended. Shorter regimens are highly desirable. Because substituting rifapentine for rifampin in the standard regimen for active tuberculosis halves the treatment duration needed to prevent relapse in mice, we hypothesized daily rifapentine-based regimens could shorten LTBI treatment to 2 months or less. Objectives: To improve an existing model of LTBI chemotherapy and evaluate the efficacy of daily rifapentine-based regimens. Methods: Mice were immunized with a more immunogenic recombinant Bacille Calmette-Guérin strain (rBCG30) and received very low-dose aerosol infection with Mycobacterium tuberculosis to establish a stable lung bacterial burden below 104 CFU without drug treatment. Mice received a control (isoniazid alone, rifampin alone, rifampin+isoniazid, rifampin+pyrazinamide) or test (rifapentine alone, rifapentine+isoniazid, rifapentine+pyrazinamide, rifapentine+isoniazid+pyrazinamide) regimen for 8 weeks. Rifamycin doses were 10 mg/kg/d, analogous to the same human doses. Outcomes were biweekly lung CFU counts and relapse after 4 to 8 weeks of treatment. Measurements and Main Results: M. tuberculosis CFU counts remained stable around 3.65 log10 in immunized, untreated mice. Isoniazid or rifampin left all or most mice culture-positive at week 8. Rifampin+isoniazid cured 0 and 53% of mice and rifampin+pyrazinamide cured 47 and 100% of mice in 4 and 8 weeks, respectively. Rifapentine-based regimens were more active than rifampin+isoniazid and indistinguishable from rifampin+pyrazinamide. Conclusions: In this improved murine model of LTBI chemotherapy with very low lung burden, existing regimens were well represented. Daily rifapentine-based regimens were at least as active as rifampin+pyrazinamide, suggesting they could effectively treat LTBI in 6 to 8 weeks. PMID:19729664

  14. Temporal and spatial intermittency of sub-daily precipitation in general circulation models

    NASA Astrophysics Data System (ADS)

    Klingaman, Nicholas; Martin, Gill; Moise, Aurel

    2015-04-01

    General circulation models often fail to reproduce the observed spatial and temporal distributions of tropical precipitation (e.g. Stephens et al. 2010). The need for improved understanding of how a warming climate may change precipitation variability and extremes has focused model developers' attention on the inability of convection parameterizations to represent the observed range of deep convective processes (e.g. Rossow et al. 2013). As climate-model resolutions increase towards scales previously used for short-term forecasting, the benefits of seamless modelling are being balanced by increasingly apparent deficiencies in convection parameterizations. Under particular scrutiny are the consequences of poorly simulated sub-daily, gridpoint precipitation variability on rainfall distributions at longer (e.g., daily, seasonal, decadal) and larger scales. We present the behaviour of tropical convection in the MetUM in a hierarchy of global configurations from ~10km to ~100km resolution, and in ten climate models from the "Vertical Structure and Diabatic Processes of the Madden-Julian Oscillation" project. We establish new methods of analysing timestep precipitation that allow comparisons between resolutions and physical parameterizations. We first investigate the relationship between timestep-to-timestep variations of modelled convection at the gridbox scale and its variability on longer and larger scales, and compare simulated and observed rainfall variability. We demonstrate that convection parameterization changes that alter timestep variability (e.g., entrainment and detrainment rates and closure timescales) also affect longer-scale variability. For example, we show that ~100 km configurations exhibit coherent timestep intermittency at large spatial scales, which reduce at finer resolutions and with parameterisation changes that suppress the depth and intensity of convection. Despite a wide variety of timestep behaviour, the models from the "Vertical Structure

  15. Stochastic modelling of spatially and temporally consistent daily precipitation time-series over complex topography

    NASA Astrophysics Data System (ADS)

    Keller, D. E.; Fischer, A. M.; Frei, C.; Liniger, M. A.; Appenzeller, C.; Knutti, R.

    2014-07-01

    Many climate impact assessments over topographically complex terrain require high-resolution precipitation time-series that have a spatio-temporal correlation structure consistent with observations. This consistency is essential for spatially distributed modelling of processes with non-linear responses to precipitation input (e.g. soil water and river runoff modelling). In this regard, weather generators (WGs) designed and calibrated for multiple sites are an appealing technique to stochastically simulate time-series that approximate the observed temporal and spatial dependencies. In this study, we present a stochastic multi-site precipitation generator and validate it over the hydrological catchment Thur in the Swiss Alps. The model consists of several Richardson-type WGs that are run with correlated random number streams reflecting the observed correlation structure among all possible station pairs. A first-order two-state Markov process simulates intermittence of daily precipitation, while precipitation amounts are simulated from a mixture model of two exponential distributions. The model is calibrated separately for each month over the time-period 1961-2011. The WG is skilful at individual sites in representing the annual cycle of the precipitation statistics, such as mean wet day frequency and intensity as well as monthly precipitation sums. It reproduces realistically the multi-day statistics such as the frequencies of dry and wet spell lengths and precipitation sums over consecutive wet days. Substantial added value is demonstrated in simulating daily areal precipitation sums in comparison to multiple WGs that lack the spatial dependency in the stochastic process: the multi-site WG is capable to capture about 95% of the observed variability in daily area sums, while the summed time-series from multiple single-site WGs only explains about 13%. Limitation of the WG have been detected in reproducing observed variability from year to year, a component that has

  16. Modeling daily average stream temperature from air temperature and watershed area

    NASA Astrophysics Data System (ADS)

    Butler, N. L.; Hunt, J. R.

    2012-12-01

    Habitat restoration efforts within watersheds require spatial and temporal estimates of water temperature for aquatic species especially species that migrate within watersheds at different life stages. Monitoring programs are not able to fully sample all aquatic environments within watersheds under the extreme conditions that determine long-term habitat viability. Under these circumstances a combination of selective monitoring and modeling are required for predicting future geospatial and temporal conditions. This study describes a model that is broadly applicable to different watersheds while using readily available regional air temperature data. Daily water temperature data from thirty-eight gauges with drainage areas from 2 km2 to 2000 km2 in the Sonoma Valley, Napa Valley, and Russian River Valley in California were used to develop, calibrate, and test a stream temperature model. Air temperature data from seven NOAA gauges provided the daily maximum and minimum air temperatures. The model was developed and calibrated using five years of data from the Sonoma Valley at ten water temperature gauges and a NOAA air temperature gauge. The daily average stream temperatures within this watershed were bounded by the preceding maximum and minimum air temperatures with smaller upstream watersheds being more dependent on the minimum air temperature than maximum air temperature. The model assumed a linear dependence on maximum and minimum air temperature with a weighting factor dependent on upstream area determined by error minimization using observed data. Fitted minimum air temperature weighting factors were consistent over all five years of data for each gauge, and they ranged from 0.75 for upstream drainage areas less than 2 km2 to 0.45 for upstream drainage areas greater than 100 km2. For the calibration data sets within the Sonoma Valley, the average error between the model estimated daily water temperature and the observed water temperature data ranged from 0.7

  17. A stage structured mosquito model incorporating effects of precipitation and daily temperature fluctuations.

    PubMed

    Wang, Xia; Tang, Sanyi; Cheke, Robert A

    2016-12-21

    An outbreak of dengue fever in Guangdong province in 2014 was the most serious outbreak ever recorded in China. Given the known positive correlation between the abundance of mosquitoes and the number of dengue fever cases, a stage structured mosquito model was developed to investigate the cause of the large abundance of mosquitoes in 2014 and its implications for outbreaks of the disease. Data on the Breteau index (number of containers positive for larvae per 100 premises investigated), temperature and precipitation were used for model fitting. The egg laying rate, the development rate and the mortality rates of immatures and adults were obtained from the estimated parameters. Moreover, effects of daily fluctuations of temperature on these parameters were obtained and the effects of temperature and precipitation were analyzed by simulations. Our results indicated that the abundance of mosquitoes depended not only on the total annual precipitation but also on the distribution of the precipitation. The daily mean temperature had a nonlinear relationship with the abundance of mosquitoes, and large diurnal temperature differences can reduce the abundance of mosquitoes. In addition, effects of increasing precipitation and temperature were interdependent. Our findings suggest that the large abundance of mosquitoes in 2014 was mainly caused by the distribution of the precipitation. In the perspective of mosquito control, our results reveal that it is better to clear water early and spray insecticide between April and August in case of limited resources.

  18. The Earth's Shape and Movements: Teachers' Perception of the Relations Between Daily Observation and Scientific Models

    NASA Astrophysics Data System (ADS)

    Ferreira, Flávia Polati; Leite, Cristina

    2015-07-01

    The Earth’s shape and movements are some of the most common issues in official documents and research studies of astronomy education. Many didactic proposals suggest these issues within observational astronomy. Therefore, we present in this paper some of the main results of a research study of the teachers’ perception of the relations between the knowledge from daily observation and scientific models currently accepted about the “earth’s shape and movements”. Data were obtained in application of the didactic proposal during a teacher training course for teachers from São Paulo, have been constructed with the dynamics “Three Pedagogical Moments” and guided by some of the central ideas of the educator Paulo Freire. The results indicate that a small proportion of teachers seem to understand some of the relations of “apparent contradictions” and “limitations” with the concepts of spatiality, and many of them argued based only on vague phrases or "buzzwords", unconnected to the problem explored. The difficulties of teachers to relate elements of daily observation with scientific models seem to indicate a necessity to approach some these aspects with the astronomical knowledge in the teacher training courses.

  19. Modeling daily soil salinity dynamics in response to agricultural and environmental changes in coastal Bangladesh

    NASA Astrophysics Data System (ADS)

    Payo, Andrés.; Lázár, Attila N.; Clarke, Derek; Nicholls, Robert J.; Bricheno, Lucy; Mashfiqus, Salehin; Haque, Anisul

    2017-05-01

    Understanding the dynamics of salt movement in the soil is a prerequisite for devising appropriate management strategies for land productivity of coastal regions, especially low-lying delta regions, which support many millions of farmers around the world. At present, there are no numerical models able to resolve soil salinity at regional scale and at daily time steps. In this research, we develop a novel holistic approach to simulate soil salinization comprising an emulator-based soil salt and water balance calculated at daily time steps. The method is demonstrated for the agriculture areas of coastal Bangladesh (˜20,000 km2). This shows that we can reproduce the dynamics of soil salinity under multiple land uses, including rice crops, combined shrimp and rice farming, as well as non-rice crops. The model also reproduced well the observed spatial soil salinity for the year 2009. Using this approach, we have projected the soil salinity for three different climate ensembles, including relative sea-level rise for the year 2050. Projected soil salinity changes are significantly smaller than other reported projections. The results suggest that inter-season weather variability is a key driver of salinization of agriculture soils at coastal Bangladesh.

  20. Reconstruction of daily erythemal UV radiation values for the last century - The benefit of modelled ozone

    NASA Astrophysics Data System (ADS)

    Junk, J.; Feister, U.; Rozanov, E.; Krzyścin, J. W.

    2013-05-01

    Solar erythemal UV radiation (UVER) is highly relevant for numerous biological processes that affect plants, animals, and human health. Nevertheless, long-term UVER records are scarce. As significant declines in the column ozone concentration were observed in the past and a recovery of the stratospheric ozone layer is anticipated by the middle of the 21st century, there is a strong interest in the temporal variation of UVER time series. Therefore, we combined groundbased measurements of different meteorological variables with modeled ozone data sets to reconstruct time series of daily totals of UVER at the Meteorological Observatory Potsdam, Germany. Artificial neural networks were trained with measured UVER, sunshine duration, the day of year, measured and modeled total column ozone, as well as the minimum solar zenith angle. This allows for the reconstruction of daily totals of UVER for the period from 1901 to 1999. Additionally, analyses of the long-term variations from 1901 until 1999 of the reconstructed, new UVER data set are presented. The time series of monthly and annual totals of UVER provide a long-term meteorological basis for epidemiological investigations in human health and occupational medicine for the region of Potsdam and Berlin. A strong benefit of our ANN-approach is the fact that it can be easily adapted to different geographical locations, as successfully tested in the framework of the COSTAction 726.

  1. Temporal scaling analysis of irradiance estimated from daily satellite data and numerical modelling

    NASA Astrophysics Data System (ADS)

    Vindel, Jose M.; Navarro, Ana A.; Valenzuela, Rita X.; Ramírez, Lourdes

    2016-11-01

    The temporal variability of global irradiance estimated from daily satellite data and numerical models has been compared for different spans of time. According to the time scale considered, a different behaviour can be expected for each climate. Indeed, for all climates and at small scale, the persistence decreases as this scale increases, but the mediterranean climate, and its continental variety, shows higher persistence than oceanic climate. The probabilities of maintaining the values of irradiance after a certain period of time have been used as a first approximation to analyse the quality of each source, according to the climate. In addition, probability distributions corresponding to variations of clearness indices measured at several stations located in different climate zones have been compared with those obtained from satellite and modelling estimations. For this work, daily radiation data from the reanalysis carried out by the European Centre for Medium-Range Weather Forecasts and from the Satellite Application Facilities on climate monitoring have been used for mainland Spain. According to the results, the temporal series estimation of irradiance is more accurate when using satellite data, independent of the climate considered. In fact, the coefficients of determination corresponding to the locations studied are always above 0.92 in the case of satellite data, while this coefficient decreases to 0.69 for some cases of the numerical model. This conclusion is more evident in oceanic climates, where the most important errors can be observed. Indeed, in this case, the RRMSE derived from the CM-SAF estimations is 20.93%, while in the numerical model, it is 48.33%. Analysis of the probabilities corresponding to variations in the clearness indices also shows a better behaviour of the satellite-derived estimates for oceanic climate. For the standard mediterranean climate, the satellite also provides better results, though the numerical model improves

  2. Modeling trait and state variation using multilevel factor analysis with PANAS daily diary data

    PubMed Central

    Merz, Erin L.; Roesch, Scott C.

    2011-01-01

    This study used daily diary data to model trait and state Positive Affect (PA) and Negative Affect (NA) using the Positive and Negative Affect Schedule (PANAS; Watson, Clark, & Tellegen, 1988). Data were collected from 364 college students over five days. Intraclass correlation coefficients suggested approximately equal amounts of variability at the trait and state levels. Multilevel factor analysis revealed that the model specifying two correlated factors (PA, NA) and correlated uniqueness terms among redundant items provided the best fit. Trait and state PA and NA were generally associated with stress, anxiety, depression, and three types of self-esteem (performance, academic, social). The coefficients describing these relationships differed somewhat, suggesting that trait and state measurement may have different predictive utility. PMID:21516166

  3. Genetic parameters for functional traits in dairy cattle from daily random regression models.

    PubMed

    Karacaören, B; Jaffrézic, F; Kadarmideen, H N

    2006-02-01

    The objective of the research was to estimate genetic parameters, such as heritabilities and genetic correlations, using daily test day data for milk yield (MY), milking speed (MS), dry matter intake (DMI), and body weight (BW) using random regression methodology. Data were from first lactation dairy cows (n = 320) from the Chamau research farm of the Swiss Federal Institute of Technology, Switzerland over the period from April 1994 to 2004. All traits were recorded daily using automated machines. Estimated heritabilities (h(2)) varied from 0.18 to 0.30 (mean h(2) = 0.24) for MY, 0.003 to 0.098 (mean h(2) = 0.03) for MS, 0.22 to 0.53 (mean h(2) = 0.43) for BW, and 0.12 to 0.34 (mean h(2) = 0.23) for DMI. A permanent environmental effect was included in both the univariate and bivariate models, but was assumed constant in estimating some genetic correlations because of convergence problems. Estimated genetic correlations varied from 0.31 to 0.41 between MY and MS, from -0.47 to 0.29 between MY and DMI, from -0.60 to 0.54 between MY and BW, from 0.17 to 0.26 between MS and DMI, from -0.18 to 0.25 between MS and BW, and from -0.89 to 0.29 between DMI and BW. Genetic correlations for MY, MS, DMI, and BW from calving to midlactation decreased similarly to 0.40, 0.36, 0.14, and 0.36 and, at the end of the lactation, decreased to -0.06, 0.23, -0.07, and 0.09, respectively. Daily genetic variance-covariance of many functional traits are reported for the first time and will be useful when constructing selection indexes for more than one trait based on longitudinal genetic parameters.

  4. Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modelling heteroscedastic residual errors

    NASA Astrophysics Data System (ADS)

    David, McInerney; Mark, Thyer; Dmitri, Kavetski; George, Kuczera

    2017-04-01

    This study provides guidance to hydrological researchers which enables them to provide probabilistic predictions of daily streamflow with the best reliability and precision for different catchment types (e.g. high/low degree of ephemerality). Reliable and precise probabilistic prediction of daily catchment-scale streamflow requires statistical characterization of residual errors of hydrological models. It is commonly known that hydrological model residual errors are heteroscedastic, i.e. there is a pattern of larger errors in higher streamflow predictions. Although multiple approaches exist for representing this heteroscedasticity, few studies have undertaken a comprehensive evaluation and comparison of these approaches. This study fills this research gap by evaluating 8 common residual error schemes, including standard and weighted least squares, the Box-Cox transformation (with fixed and calibrated power parameter, lambda) and the log-sinh transformation. Case studies include 17 perennial and 6 ephemeral catchments in Australia and USA, and two lumped hydrological models. We find the choice of heteroscedastic error modelling approach significantly impacts on predictive performance, though no single scheme simultaneously optimizes all performance metrics. The set of Pareto optimal schemes, reflecting performance trade-offs, comprises Box-Cox schemes with lambda of 0.2 and 0.5, and the log scheme (lambda=0, perennial catchments only). These schemes significantly outperform even the average-performing remaining schemes (e.g., across ephemeral catchments, median precision tightens from 105% to 40% of observed streamflow, and median biases decrease from 25% to 4%). Theoretical interpretations of empirical results highlight the importance of capturing the skew/kurtosis of raw residuals and reproducing zero flows. Recommendations for researchers and practitioners seeking robust residual error schemes for practical work are provided.

  5. HIGH-RESOLUTION SPATIAL MODELING OF DAILY WEATHER ELEMENTS FOR A CATCHMENT IN THE OREGON CASCADE MOUNTAINS, UNITED STATES

    EPA Science Inventory

    High-quality, daily meteorological data at high spatial resolution are essential for a variety of hydrologic and ecological modeling applications that support environmental risk assessments and decision making. This paper describes the development, application, and assessment of ...

  6. HIGH-RESOLUTION SPATIAL MODELING OF DAILY WEATHER ELEMENTS FOR A CATCHMENT IN THE OREGON CASCADE MOUNTAINS, UNITED STATES

    EPA Science Inventory

    High-quality, daily meteorological data at high spatial resolution are essential for a variety of hydrologic and ecological modeling applications that support environmental risk assessments and decision making. This paper describes the development, application, and assessment of ...

  7. Bayesian multinomial probit modeling of daily windows of susceptibility for maternal PM2.5 exposure and congenital heart defects

    EPA Science Inventory

    Past epidemiologic studies suggest maternal ambient air pollution exposure during critical periods of the pregnancy is associated with fetal development. We introduce a multinomial probit model that allows for the joint identification of susceptible daily periods during the pregn...

  8. Bayesian multinomial probit modeling of daily windows of susceptibility for maternal PM2.5 exposure and congenital heart defects

    EPA Science Inventory

    Past epidemiologic studies suggest maternal ambient air pollution exposure during critical periods of the pregnancy is associated with fetal development. We introduce a multinomial probit model that allows for the joint identification of susceptible daily periods during the pregn...

  9. A current precipitation index-based model for continuous daily runoff simulation in seasonally snow covered sub-arctic catchments

    NASA Astrophysics Data System (ADS)

    Akanegbu, Justice O.; Marttila, Hannu; Ronkanen, Anna-Kaisa; Kløve, Bjørn

    2017-02-01

    A new precipitation index-based model, which includes a snow accumulation and melt component, has been developed to simulate hydrology in high latitude catchments. The model couples a point snowmelt model with a current precipitation index (CPI) formulation to simulate continuous daily runoff from catchments with seasonal snow cover. A new runoff conversion factor: CT and Lf, threshold flow factor ThQ and runoff transformation function Maxbas were introduced into the CPI equation, which converts and transforms the routed daily CPI into daily runoff and maintains the daily base flow in the catchment. The model was developed using twelve sub-arctic boreal catchments located above and below the Arctic Circle in northern Finland, representing a region with considerable seasonal snow cover. The results showed that the model can adequately simulate and produce the dynamics of daily runoff from catchments where the underlying physical conditions are not known. An open-access Excel-based model is provided with this paper for daily runoff simulations. The model can be used to estimate runoff in sub-arctic regions where little data is typically available but significant changes in climate are expected, with considerable shifts in the amount and timing of snowmelt and runoff.

  10. Evaluating regional climate models for simulating sub-daily rainfall extremes

    NASA Astrophysics Data System (ADS)

    Cortés-Hernández, Virginia Edith; Zheng, Feifei; Evans, Jason; Lambert, Martin; Sharma, Ashish; Westra, Seth

    2016-09-01

    Sub-daily rainfall extremes are of significant societal interest, with implications for flash flooding and the design of urban stormwater systems. It is increasingly recognised that extreme subdaily rainfall will intensify as a result of global temperature increases, with regional climate models (RCMs) representing one of the principal lines of evidence on the likely magnitude and spatiotemporal characteristics of these changes. To evaluate the ability of RCMs to simulate subdaily extremes, it is common to compare the simulated statistical characteristics of the extreme rainfall events with those from observational records. While such analyses are important, they provide insufficient insight into whether the RCM reproduces the correct underlying physical processes; in other words, whether the model "gets the right answers for the right reasons". This paper develops a range of metrics to assess the performance of RCMs in capturing the physical mechanisms that produce extreme rainfall. These metrics include the diurnal and seasonal cycles, relationship between rainfall intensity and temperature, temporal scaling, and the spatial structure of extreme rainfall events. We evaluate a high resolution RCM—the Weather Research Forecasting model—over the Greater Sydney region, using three alternative parametrization schemes. The model shows consistency with the observations for most of the proposed metrics. Where differences exist, these are dependent on both the rainfall duration and model parameterization strategy. The use of physically meaningful performance metrics not only enhances the confidence in model simulations, but also provides better diagnostic power to assist with future model improvement.

  11. Enhancing fecal coliform total maximum daily load models through bacterial source tracking

    USGS Publications Warehouse

    Hyer, K.E.; Moyer, D.L.

    2004-01-01

    Surface water impairment by fecal coliform bacteria is a water quality issue of national scope and importance. In Virginia, more than 400 stream and river segments are on the Commonwealth's 2002 303(d) list because of fecal coliform impairment. Total maximum daily loads (TMDLs) will be developed for most of these listed streams and rivers. Information regarding the major fecal coliform sources that impair surface water quality would enhance the development of effective watershed models and improve TMDLs. Bacterial source tracking (BST) is a recently developed technology for identifying the sources of fecal coliform bacteria and it may be helpful in generating improved TMDLs. Bacterial source tracking was performed, watershed models were developed, and TMDLs were prepared for three streams (Accotink Creek, Christians Creek, and Blacks Run) on Virginia's 303(d) list of impaired waters. Quality assurance of the BST work suggests that these data adequately describe the bacteria sources that are impairing these streams. Initial comparison of simulated bacterial sources with the observed BST data indicated that the fecal coliform sources were represented inaccurately in the initial model simulation. Revised model simulations (based on BST data) appeared to provide a better representation of the sources of fecal coliform bacteria in these three streams. The coupled approach of incorporating BST data into the fecal coliform transport model appears to reduce model uncertainty and should result in an improved TMDL.

  12. Daily reservoir sedimentation model: Case study from the Fena Valley Reservoir, Guam

    USGS Publications Warehouse

    Marineau, Mathieu D.; Wright, Scott A.

    2017-01-01

    A model to compute reservoir sedimentation rates at daily timescales is presented. The model uses streamflow and sediment load data from nearby stream gauges to obtain an initial estimate of sediment yield for the reservoir’s watershed; it is then calibrated to the total deposition calculated from repeat bathymetric surveys. Long-term changes to reservoir trapping efficiency are also taken into account. The model was applied to the Fena Valley Reservoir, a water supply reservoir on the island of Guam. This reservoir became operational in 1951 and was recently surveyed in 2014. The model results show that the highest rate of deposition occurred during two typhoons (Typhoon Alice in 1953 and Typhoon Tingting in 2004); each storm decreased reservoir capacity by approximately 2–3% in only a few days. The presented model can be used to evaluate the impact of an extreme event, or it can be coupled with a watershed runoff model to evaluate potential impacts to storage capacity as a result of climate change or other hydrologic modifications.

  13. A Pareto-optimal moving average multigene genetic programming model for daily streamflow prediction

    NASA Astrophysics Data System (ADS)

    Danandeh Mehr, Ali; Kahya, Ercan

    2017-06-01

    Genetic programming (GP) is able to systematically explore alternative model structures of different accuracy and complexity from observed input and output data. The effectiveness of GP in hydrological system identification has been recognized in recent studies. However, selecting a parsimonious (accurate and simple) model from such alternatives still remains a question. This paper proposes a Pareto-optimal moving average multigene genetic programming (MA-MGGP) approach to develop a parsimonious model for single-station streamflow prediction. The three main components of the approach that take us from observed data to a validated model are: (1) data pre-processing, (2) system identification and (3) system simplification. The data pre-processing ingredient uses a simple moving average filter to diminish the lagged prediction effect of stand-alone data-driven models. The multigene ingredient of the model tends to identify the underlying nonlinear system with expressions simpler than classical monolithic GP and, eventually simplification component exploits Pareto front plot to select a parsimonious model through an interactive complexity-efficiency trade-off. The approach was tested using the daily streamflow records from a station on Senoz Stream, Turkey. Comparing to the efficiency results of stand-alone GP, MGGP, and conventional multi linear regression prediction models as benchmarks, the proposed Pareto-optimal MA-MGGP model put forward a parsimonious solution, which has a noteworthy importance of being applied in practice. In addition, the approach allows the user to enter human insight into the problem to examine evolved models and pick the best performing programs out for further analysis.

  14. Sleep and affect in older adults: using multilevel modeling to examine daily associations

    PubMed Central

    McCRAE, CHRISTINA S.; McNAMARA, JOSEPH P. H.; ROWE, MEREDETH A.; DZIERZEWSKI, JOSEPH M.; DIRK, JUDITH; MARSISKE, MICHAEL; CRAGGS, JASON G.

    2010-01-01

    SUMMARY The main objective of the present study was to examine daily associations (intraindividual variability or IIV) between sleep and affect in older adults. Greater understanding of these associations is important, because both sleep and affect represent modifiable behaviors that can have a major influence on older adults’ health and well-being. We collected sleep diaries, actigraphy, and affect data concurrently for 14 days in 103 community-dwelling older adults. Multilevel modeling was used to assess the sleep–affect relationship at both the group (between-persons) and individual (within-person or IIV) levels. We hypothesized that nights characterized by better sleep would be associated with days characterized by higher positive affect and lower negative affect, and that the inverse would be true for poor sleep. Daily associations were found between affect and subjective sleep, only and were in the hypothesized direction. Specifically, nights with greater reported awake time or lower sleep quality ratings were associated with days characterized by less positive affect and more negative affect. Gender was not a significant main effect in the present study, despite previous research suggesting gender differences in the sleep–affect relationship. The fact that self-ratings of sleep emerged as the best predictors of affect may suggest that perceived sleep is a particularly important predictor. Finally, our results suggest exploration of affect as a potential intervention target in late-life insomnia is warranted. PMID:18275554

  15. Effect of daily parathyroid hormone (1-34) on lumbar fusion in a rat model.

    PubMed

    Lawrence, James P; Ennis, Frank; White, Andrew P; Magit, David; Polzhofer, Gert; Drespe, Inneke; Troiano, Nancy W; Grauer, Jonathan N

    2006-01-01

    Despite numerous studies evaluating the anabolic effects of intermittent administration of parathyroid hormone (PTH) on bone, there are no published studies examining its effect on spinal fusion outcomes. To determine the effect of daily injection of human recombinant PTH(1-34) on posterolateral lumbar fusions in a rat model. Prospective, case-controlled, preclinical animal study. Manual palpation and serum osteocalcin. Single-level, intertransverse process spinal fusions were performed with iliac crest autograft in 56 Sprague-Dawley rats. Animals received daily injections of placebo or PTH(1-34). At 6 weeks, fusion masses were assessed by manual palpation. Serum osteocalcin levels were assessed in a subset of the animals. Manual palpation revealed the control group to have a fusion rate of 37% (10/27) and the PTH(1-34)-treated group to have a fusion rate of 52% (15/29). Mean serum osteocalcin levels were 59.8 and 88.6 ng/L for the control and PTH(1-34) groups, respectively. There was a trend towards greater fusion rate in the PTH(1-34) group as compared with the placebo group. Further, PTH(1-34) administration was associated with a significant increase in osteocalcin levels. Certainly, further investigations are warranted, as an injectable agent capable of increasing fusion rates would be of great clinical value.

  16. Daily life activity routine discovery in hemiparetic rehabilitation patients using topic models.

    PubMed

    Seiter, J; Derungs, A; Schuster-Amft, C; Amft, O; Tröster, G

    2015-01-01

    Monitoring natural behavior and activity routines of hemiparetic rehabilitation patients across the day can provide valuable progress information for therapists and patients and contribute to an optimized rehabilitation process. In particular, continuous patient monitoring could add type, frequency and duration of daily life activity routines and hence complement standard clinical scores that are assessed for particular tasks only. Machine learning methods have been applied to infer activity routines from sensor data. However, supervised methods require activity annotations to build recognition models and thus require extensive patient supervision. Discovery methods, including topic models could provide patient routine information and deal with variability in activity and movement performance across patients. Topic models have been used to discover characteristic activity routine patterns of healthy individuals using activity primitives recognized from supervised sensor data. Yet, the applicability of topic models for hemiparetic rehabilitation patients and techniques to derive activity primitives without supervision needs to be addressed. We investigate, 1) whether a topic model-based activity routine discovery framework can infer activity routines of rehabilitation patients from wearable motion sensor data. 2) We compare the performance of our topic model-based activity routine discovery using rule-based and clustering-based activity vocabulary. We analyze the activity routine discovery in a dataset recorded with 11 hemiparetic rehabilitation patients during up to ten full recording days per individual in an ambulatory daycare rehabilitation center using wearable motion sensors attached to both wrists and the non-affected thigh. We introduce and compare rule-based and clustering-based activity vocabulary to process statistical and frequency acceleration features to activity words. Activity words were used for activity routine pattern discovery using topic models

  17. Connecting the Dots: Linking Environmental Justice Indicators to Daily Dose Model Estimates

    PubMed Central

    Huang, Hongtai; Barzyk, Timothy M.

    2016-01-01

    Many different quantitative techniques have been developed to either assess Environmental Justice (EJ) issues or estimate exposure and dose for risk assessment. However, very few approaches have been applied to link EJ factors to exposure dose estimate and identify potential impacts of EJ factors on dose-related variables. The purpose of this study is to identify quantitative approaches that incorporate conventional risk assessment (RA) dose modeling and cumulative risk assessment (CRA) considerations of disproportionate environmental exposure. We apply the Average Daily Dose (ADD) model, which has been commonly used in RA, to better understand impacts of EJ indicators upon exposure dose estimates and dose-related variables, termed the Environmental-Justice-Average-Daily-Dose (EJ-ADD) approach. On the U.S. nationwide census tract-level, we defined and quantified two EJ indicators (poverty and race/ethnicity) using an EJ scoring method to examine their relation to census tract-level multi-chemical exposure dose estimates. Pollutant doses for each tract were calculated using the ADD model, and EJ scores were assigned to each tract based on poverty- or race-related population percentages. Single- and multiple-chemical ADD values were matched to the tract-level EJ scores to analyze disproportionate dose relationships and contributing EJ factors. We found that when both EJ indicators were examined simultaneously, ADD for all pollutants generally increased with larger EJ scores. To demonstrate the utility of using EJ-ADD on the local scale, we approximated ADD levels of lead via soil/dust ingestion for simulated communities with different EJ-related scenarios. The local-level simulation indicates a substantial difference in exposure-dose levels between wealthy and EJ communities. The application of the EJ-ADD approach can link EJ factors to exposure dose estimate and identify potential EJ impacts on dose-related variables. PMID:28036053

  18. Connecting the Dots: Linking Environmental Justice Indicators to Daily Dose Model Estimates.

    PubMed

    Huang, Hongtai; Barzyk, Timothy M

    2016-12-28

    Many different quantitative techniques have been developed to either assess Environmental Justice (EJ) issues or estimate exposure and dose for risk assessment. However, very few approaches have been applied to link EJ factors to exposure dose estimate and identify potential impacts of EJ factors on dose-related variables. The purpose of this study is to identify quantitative approaches that incorporate conventional risk assessment (RA) dose modeling and cumulative risk assessment (CRA) considerations of disproportionate environmental exposure. We apply the Average Daily Dose (ADD) model, which has been commonly used in RA, to better understand impacts of EJ indicators upon exposure dose estimates and dose-related variables, termed the Environmental-Justice-Average-Daily-Dose (EJ-ADD) approach. On the U.S. nationwide census tract-level, we defined and quantified two EJ indicators (poverty and race/ethnicity) using an EJ scoring method to examine their relation to census tract-level multi-chemical exposure dose estimates. Pollutant doses for each tract were calculated using the ADD model, and EJ scores were assigned to each tract based on poverty- or race-related population percentages. Single- and multiple-chemical ADD values were matched to the tract-level EJ scores to analyze disproportionate dose relationships and contributing EJ factors. We found that when both EJ indicators were examined simultaneously, ADD for all pollutants generally increased with larger EJ scores. To demonstrate the utility of using EJ-ADD on the local scale, we approximated ADD levels of lead via soil/dust ingestion for simulated communities with different EJ-related scenarios. The local-level simulation indicates a substantial difference in exposure-dose levels between wealthy and EJ communities. The application of the EJ-ADD approach can link EJ factors to exposure dose estimate and identify potential EJ impacts on dose-related variables.

  19. Daily Suction Provided by External Volume Expansion Inducing Regeneration of Grafted Fat in a Murine Model.

    PubMed

    Ye, Yuan; Liao, Yunjun; Lu, Feng; Gao, Jianhua

    2017-02-01

    Fat grafting has variable and sometimes poor outcomes, and therefore new methods are needed. Multiple studies have demonstrated the excellent performance of external volume expansion and focused only on preexpansion with emphasis on the recipient. Two mouse models (a suction model and a fat-exchange transplantation model) were established to investigate changes in the origins and biological behaviors of regeneration-related cells in grafted fat under daily suction provided by external volume expansion. Blood supply increased from new host-derived capillaries or macrophage infiltration under suction. CD34-positive cells showed increased migration from the host into the grafts under suction. At week 12, nearly half of the mature adipocytes regenerated in the grafts in the suction group were derived from the host. Peroxisome proliferator-activated receptor γ expression of the suction group was significantly higher than that of controls at weeks 2 and 4 during adipogenesis. The normalized sample weight of the grafted fat was significantly greater than that of controls at 1 (0.081 ± 0.001 versus 0.072 ± 0.005; p < 0.001), 4 (0.060 ± 0.002 versus 0.048 ± 0.001; p = 0.002), 8 (0.060 ± 0.001 versus 0.046 ± 0.001; p < 0.001), and 12 weeks (0.060 ± 0.001 versus 0.046 ± 0.001; p = 0.002). The mechanical effect of daily suction provided by external volume expansion favors the regeneration of grafted fat and improves retention by promoting the migration of regeneration-related cells and the differentiation of adipocytes. Thus, more mature fat tissue with a well-organized structure was formed under suction.

  20. Ability in daily activities after early supported discharge models of stroke rehabilitation

    PubMed Central

    Taule, Tina; Strand, Liv Inger; Assmus, Jörg; Skouen, Jan Sture

    2015-01-01

    Abstract More knowledge is needed about how different rehabilitation models in the municipality influence stroke survivors’ ability in activities of daily living (ADL). Objectives: To compare three models of outpatient rehabilitation; early supported discharge (ESD) in a day unit, ESD at home and traditional treatment in the municipality (control group), regarding change in ADL ability during the first three months after stroke. Methods: A group comparison study was designed within a randomized controlled trial. Included participants were tested with the Assessment of Motor and Process Skills (AMPS) at baseline and discharged directly home. Primary and secondary outcomes were the AMPS and the modified Rankin Scale (mRS). Results and conclusions: Included were 154 participants (57% men, median age 73 years), and 103 participants completed the study. There were no significant group differences in pre–post changed ADL ability measured by the AMPS. To find the best rehabilitation model to improve the quality of stroke survivors’ motor and process skills needs further research. Patients participating in the ESD rehabilitation models were, compared with traditional treatment, significantly associated with improved ADL ability measured by the mRS when controlling for confounding factors, indicating that patients with social needs and physical impairment after stroke may benefit from ESD rehabilitation models. PMID:26005768

  1. Ability in daily activities after early supported discharge models of stroke rehabilitation.

    PubMed

    Taule, Tina; Strand, Liv Inger; Assmus, Jörg; Skouen, Jan Sture

    2015-01-01

    More knowledge is needed about how different rehabilitation models in the municipality influence stroke survivors' ability in activities of daily living (ADL). To compare three models of outpatient rehabilitation; early supported discharge (ESD) in a day unit, ESD at home and traditional treatment in the municipality (control group), regarding change in ADL ability during the first three months after stroke. A group comparison study was designed within a randomized controlled trial. Included participants were tested with the Assessment of Motor and Process Skills (AMPS) at baseline and discharged directly home. Primary and secondary outcomes were the AMPS and the modified Rankin Scale (mRS). Included were 154 participants (57% men, median age 73 years), and 103 participants completed the study. There were no significant group differences in pre-post changed ADL ability measured by the AMPS. To find the best rehabilitation model to improve the quality of stroke survivors' motor and process skills needs further research. Patients participating in the ESD rehabilitation models were, compared with traditional treatment, significantly associated with improved ADL ability measured by the mRS when controlling for confounding factors, indicating that patients with social needs and physical impairment after stroke may benefit from ESD rehabilitation models.

  2. Modelling of spatio-temporal correlation structure of daily precipitation - an Austrian example

    NASA Astrophysics Data System (ADS)

    Salinas, Jose Luis; Nester, Thomas; Komma, Jürgen; Blöschl, Günter

    2017-04-01

    Understanding the spatial and temporal correlation of rainfall is of pivotal importance for assessing regional hydroclimatic hazard, and for addressing problems like confluences or joint probability of flood waves. Furthermore, if one aims to simulate precipitation as the input for long term rainfall-runoff simulations, the correct reproduction of the observed rainfall spatial and temporal correlations is necessary to adequately model important hydrological features, like antecedent soil moisture conditions before extreme rainfall events. In this work, we present a modification of the model presented by Bardossy and Platte (1992), where precipitation is modeled on a station basis as a mutivariate autoregressive model (mAr) in a Normal space, and then transformed to a Gamma-distributed space. The spatial and temporal correlation structures are imposed in the normal space, allowing for a different temporal autocorrelation parameter for each station, and simultaneously ensuring the positive-definiteness of the correlation matrices for both the mAr errors, and the Normal-space rainfall. The calibration of the spatial and temporal correlation parameters is performed with a focus on extremes, trying to reproduce the variograms of a series of relevant rainfall events over the last 50 years in the region of interest (Tirolean Alps in Austia), as well as intensity-duration-frequency curves aggregated at different spatial and temporal scales. Bardossy, A., and E. J. Plate (1992), Space-time model for daily rainfall using atmospheric circulation patterns, Water Resour. Res., 28(5), 1247-1259, doi:10.1029/91WR02589.

  3. A long range dependent model with nonlinear innovations for simulating daily river flows

    NASA Astrophysics Data System (ADS)

    Elek, P.; Márkus, L.

    2004-04-01

    We present the analysis aimed at the estimation of flood risks of Tisza River in Hungary on the basis of daily river discharge data registered in the last 100 years. The deseasonalised series has skewed and leptokurtic distribution and various methods suggest that it possesses substantial long memory. This motivates the attempt to fit a fractional ARIMA model with non-Gaussian innovations as a first step. Synthetic streamflow series can then be generated from the bootstrapped innovations. However, there remains a significant difference between the empirical and the synthetic density functions as well as the quantiles. This brings attention to the fact that the innovations are not independent, both their squares and absolute values are autocorrelated. Furthermore, the innovations display non-seasonal periods of high and low variances. This behaviour is characteristic to generalised autoregressive conditional heteroscedastic (GARCH) models. However, when innovations are simulated as GARCH processes, the quantiles and extremes of the discharge series are heavily overestimated. Therefore we suggest to fit a smooth transition GARCH-process to the innovations. In a standard GARCH model the dependence of the variance on the lagged innovation is quadratic whereas in our proposed model it is a bounded function. While preserving long memory and eliminating the correlation from both the generating noise and from its square, the new model is superior to the previously mentioned ones in approximating the probability density, the high quantiles and the extremal behaviour of the empirical river flows.

  4. On the use of gridded daily temperature data to calculate the extended spring indices phenological models

    NASA Astrophysics Data System (ADS)

    Zurita-Milla, Raul; Mehdipoor, Hamed; Batarseh, Sana; Ault, Toby; Schwartz, Mark D.

    2014-05-01

    Models that predict the timing of recurrent biological events play an important role in supporting the systematic study of phenological changes at a variety of spatial and temporal scales. One set of such models are the extended Spring indices (SI-x). These models predicts a suite of phenological metrics ("first leaf" and "first bloom," "last freeze" and the "damage index") from temperature data and geographic location (to model the duration of the day). The SI-x models were calibrated using historical phenological and weather observations from the continental US. In particular, the models relied on first leaf and first bloom observations for lilac and honeysuckle and on daily minimum and maximum temperature values from a number of weather stations located near to the sites where phenological observations were made. In this work, we study the use of DAYMET (http://daymet.ornl.gov/) to calculate the SI-x models over the continental USA. DAYMET offers daily gridded maximum and minimum temperature values for the period 1980 to 2012. Using an automatic downloader, we downloaded complete DAYMET temperature time series for the over 1100 geographic locations where historical lilac observations were made. The temperature values were parsed and, using the recently available MATLAB code, the SI-x indices were calculated. Subsequently, the predicted first leaf and first bloom dates were compared with historical lilac observations. The RMSE between predicted and observed lilac leaf/bloom dates was calculated after identifying data from the same geographic location and year. Results were satisfactory for the lilac observations in the Eastern US (e.g. the RMSE for the blooming date was of about 5 days). However, the correspondence between the observed and predicted lilac values in the West was rather week (e.g. RMSE for the blooming date of about 22 days). This might indicate that DAYMET temperature data in this region of the US might contain larger uncertainties due to a more

  5. An Evidence-Based Adoption of Technology Model for Remote Monitoring of Elders’ Daily Activities

    PubMed Central

    2011-01-01

    What benefit will new technologies offer if they are inadequately or not used? This work presents a meta-synthesis of adoption of technology related findings from four innovative monitoring intervention research studies with older adults and their informal and/or formal caregivers. Each study employed mixed methods analyses that lead to an understanding of the key variables that influenced adoption of telephone and Internet based wireless remote monitoring technologies by elders and their caregivers. The studies were all conducted in “real world” homes ranging from solo residences to multi-story independent living residential buildings. Insights gained came from issues not found in controlled laboratory environments but in the complex interplay of family-elder-staff dynamics around balancing safety and independence. Findings resulted in an adoption of technology model for remote monitoring of elders’ daily activities derived from evidence based research to advance both practical and theoretical development in the field of gerontechnology. PMID:21423843

  6. A comparison of daily evaporation downscaled using WRFDA model and GLEAM dataset over the Iberian Peninsula.

    NASA Astrophysics Data System (ADS)

    José González-Rojí, Santos; Sáenz, Jon; Ibarra-Berastegi, Gabriel

    2017-04-01

    GLEAM dataset was presented a few years ago and since that moment, it has just been used for validation of evaporation in a few places of the world (Australia and Africa). The Iberian Peninsula is composed of different soil types and it is affected by different weather regimes, with different climate regions. It is this feature which makes it a very interesting zone for the study of the meteorological cycle, including evaporation. For that purpose, a numerical downscaling exercise over the Iberian Peninsula was run nesting the WRF model inside ERA Interim. Two model configurations were tested in two experiments spanning the period 2010-2014 after a one-year spin-up (2009). In the first experiment (N), boundary conditions drive the model. The second experiment (D) is configured the same way as the N case, but 3DVAR data assimilation is run every six hours (00Z, 06Z, 12Z and 18Z) using observations obtained from the PREPBUFR dataset. For both N and D runs and ERA Interim, the evaporation of the model runs was compared to GLEAM v3.0b and v3.0c datasets over the Iberian Peninsula, both at the daily and monthly time scales. GLEAM v3.0a was not used for validation as it uses for forcing radiation and air temperature data from ERA Interim. Results show that the experiment with data assimilation (D) improve the results obtained for N experiment. Moreover, correlations values are comparable to the ones obtained with ERA Interim. However, some negative correlation values are observed at Portuguese and Mediterranean coasts for both WRF runs. All of these problematic points are considered as urban sites by the NOAH land surface model. Because of that, the model is not able to simulate a correct evaporation value. Even with these discrepancies, better results than for ERA Interim are observed for seasonal Biases and daily RMSEs over Iberian Peninsula, obtaining the best values inland. Minimal differences are observed for the two GLEAM datasets selected.

  7. Estimation of completeness magnitude with a Bayesian modeling of daily and weekly variations in earthquake detectability

    NASA Astrophysics Data System (ADS)

    Iwata, T.

    2014-12-01

    In the analysis of seismic activity, assessment of earthquake detectability of a seismic network is a fundamental issue. For this assessment, the completeness magnitude Mc, the minimum magnitude above which all earthquakes are recorded, is frequently estimated. In most cases, Mc is estimated for an earthquake catalog of duration longer than several weeks. However, owing to human activity, noise level in seismic data is higher on weekdays than on weekends, so that earthquake detectability has a weekly variation [e.g., Atef et al., 2009, BSSA]; the consideration of such a variation makes a significant contribution to the precise assessment of earthquake detectability and Mc. For a quantitative evaluation of the weekly variation, we introduced the statistical model of a magnitude-frequency distribution of earthquakes covering an entire magnitude range [Ogata & Katsura, 1993, GJI]. The frequency distribution is represented as the product of the Gutenberg-Richter law and a detection rate function. Then, the weekly variation in one of the model parameters, which corresponds to the magnitude where the detection rate of earthquakes is 50%, was estimated. Because earthquake detectability also have a daily variation [e.g., Iwata, 2013, GJI], and the weekly and daily variations were estimated simultaneously by adopting a modification of a Bayesian smoothing spline method for temporal change in earthquake detectability developed in Iwata [2014, Aust. N. Z. J. Stat.]. Based on the estimated variations in the parameter, the value of Mc was estimated. In this study, the Japan Meteorological Agency catalog from 2006 to 2010 was analyzed; this dataset is the same as analyzed in Iwata [2013] where only the daily variation in earthquake detectability was considered in the estimation of Mc. A rectangular grid with 0.1° intervals covering in and around Japan was deployed, and the value of Mc was estimated for each gridpoint. Consequently, a clear weekly variation was revealed; the

  8. Evaluation of sunscreen products using a reconstructed skin model exposed to simulated daily ultraviolet radiation: relevance of filtration profile and SPF value for daily photoprotection.

    PubMed

    Lejeune, François; Christiaens, François; Bernerd, Françoise

    2008-10-01

    The recent definition of a standard daily ultraviolet radiation (DUVR) has allowed us to reproduce non-zenithal sun exposure conditions. Exposure to simulated DUVR induces biological damage in human skin, suggesting the need for an appropriate photoprotection. Sunscreen products were evaluated using human reconstructed skin in vitro. Two commercial sunscreens (A and B) having similar sun (burn) protection factor (SPF) values (approximately 15) but different profiles of transmission over the UVA range were tested on skin models exposed to increasing doses of DUVR. Another pair of sunscreens was also tested. One (product C) had an SPF approximately 18 with a well-balanced UVB-UVA profile and the other (product D) an SPF of approximately 27 with low UVA absorption. Biological parameters were assessed by (i) histology, (ii) vimentin immunostaining for dermal fibroblasts, and (iii) analysis of matrix metalloprotease (MMP)-1 secretion. Products A and C gave better protection from DUVR with regard to fibroblast alterations and MMP-1 release compared with products B and D, respectively. To ensure an efficient daily photoprotection from DUVR, the filtration profile of the product should be well balanced with a sufficient level of UVA absorption. With regard to end points evaluated in this study, our data suggest that a higher SPF value does not compensate for low UVA filtration.

  9. BIOMAP A Daily Time Step, Mechanistic Model for the Study of Ecosystem Dynamics

    NASA Astrophysics Data System (ADS)

    Wells, J. R.; Neilson, R. P.; Drapek, R. J.; Pitts, B. S.

    2010-12-01

    BIOMAP simulates competition between two Plant Functional Types (PFT) at any given point in the conterminous U.S. using a time series of daily temperature (mean, minimum, maximum), precipitation, humidity, light and nutrients, with PFT-specific rooting within a multi-layer soil. The model employs a 2-layer canopy biophysics, Farquhar photosynthesis, the Beer-Lambert Law for light attenuation and a mechanistic soil hydrology. In essence, BIOMAP is a re-built version of the biogeochemistry model, BIOME-BGC, into the form of the MAPSS biogeography model. Specific enhancements are: 1) the 2-layer canopy biophysics of Dolman (1993); 2) the unique MAPSS-based hydrology, which incorporates canopy evaporation, snow dynamics, infiltration and saturated and unsaturated percolation with ‘fast’ flow and base flow and a ‘tunable aquifer’ capacity, a metaphor of D’Arcy’s Law; and, 3) a unique MAPSS-based stomatal conductance algorithm, which simultaneously incorporates vapor pressure and soil water potential constraints, based on physiological information and many other improvements. Over small domains the PFTs can be parameterized as individual species to investigate fundamental vs. potential niche theory; while, at more coarse scales the PFTs can be rendered as more general functional groups. Since all of the model processes are intrinsically leaf to plot scale (physiology to PFT competition), it essentially has no ‘intrinsic’ scale and can be implemented on a grid of any size, taking on the characteristics defined by the homogeneous climate of each grid cell. Currently, the model is implemented on the VEMAP 1/2 degree, daily grid over the conterminous U.S. Although both the thermal and water-limited ecotones are dynamic, following climate variability, the PFT distributions remain fixed. Thus, the model is currently being fitted with a ‘reproduction niche’ to allow full dynamic operation as a Dynamic General Vegetation Model (DGVM). While global simulations

  10. Development and Validation of a Predictive Model for Nonadherence with Once-Daily Glaucoma Medications

    PubMed Central

    Chang, Dolly S.; Friedman, David S.; Frazier, Travis; Plyler, Ryan; Boland, Michael V.

    2014-01-01

    Objective To develop and validate a predictive model to estimate the probability of being nonadherent to topical glaucoma medications. Design Prospective cohort study. Participants Patients being treated with once-daily prostaglandin eye drops. Methods A predictive model for nonadherence was developed from the Travatan Dosing Aid (TDA) study (n = 196) using stepwise logistic regression. The performance of the TDA-derived model was assessed using a separate cohort of subjects from the Automated Dosing Reminder Study (ADRS; n = 407). The assessment was based on regression coefficients, discrimination, and calibration. We also developed a scoring system from the TDA-derived model to simplify the estimation of risk for clinical use. Main Outcome Measures Usage of drops was monitored electronically for 3 months in both studies. Adherence was calculated as the percentage of days on which a dose was taken within 4 hours of the average dosing time for that patient. Nonadherence was defined as taking ≤75% prescribed doses within a window starting 2 weeks after the baseline visit until 2 weeks before the follow-up visit. Results Six factors, including younger age, black race, worse general health status, shorter duration of glaucoma medication therapy, lower self-reported adherence, and admitting to not following doctors’ orders, were associated with being nonadherent and were included in the predictive model. The coefficients for the TDA-derived and the ADRS-derived predictive models were similar. The risk scoring system developed from the TDA study had good discrimination (area under the receiver operating characteristic curve of 0.80) and calibration (Hosmer-Lemeshow goodness-of-fit test, P = 0.102) when applied to the ADRS population. Conclusions The TDA-derived predictive model for nonadherence performed well in an independent population. A risk scoring system was developed using demographic data and patient responses to 4 questions to provide an estimate of the

  11. Development and validation of a predictive model for nonadherence with once-daily glaucoma medications.

    PubMed

    Chang, Dolly S; Friedman, David S; Frazier, Travis; Plyler, Ryan; Boland, Michael V

    2013-07-01

    To develop and validate a predictive model to estimate the probability of being nonadherent to topical glaucoma medications. Prospective cohort study. Patients being treated with once-daily prostaglandin eye drops. A predictive model for nonadherence was developed from the Travatan Dosing Aid (TDA) study (n = 196) using stepwise logistic regression. The performance of the TDA-derived model was assessed using a separate cohort of subjects from the Automated Dosing Reminder Study (ADRS; n = 407). The assessment was based on regression coefficients, discrimination, and calibration. We also developed a scoring system from the TDA-derived model to simplify the estimation of risk for clinical use. Usage of drops was monitored electronically for 3 months in both studies. Adherence was calculated as the percentage of days on which a dose was taken within 4 hours of the average dosing time for that patient. Nonadherence was defined as taking ≤ 75% prescribed doses within a window starting 2 weeks after the baseline visit until 2 weeks before the follow-up visit. Six factors, including younger age, black race, worse general health status, shorter duration of glaucoma medication therapy, lower self-reported adherence, and admitting to not following doctors' orders, were associated with being nonadherent and were included in the predictive model. The coefficients for the TDA-derived and the ADRS-derived predictive models were similar. The risk scoring system developed from the TDA study had good discrimination (area under the receiver operating characteristic curve of 0.80) and calibration (Hosmer-Lemeshow goodness-of-fit test, P = 0.102) when applied to the ADRS population. The TDA-derived predictive model for nonadherence performed well in an independent population. A risk scoring system was developed using demographic data and patient responses to 4 questions to provide an estimate of the probability of being nonadherent. Copyright © 2013 American Academy of

  12. A physics-based correction model for homogenizing sub-daily temperature series

    NASA Astrophysics Data System (ADS)

    Auchmann, R.; BröNnimann, S.

    2012-09-01

    A new physics-based technique for correcting inhomogeneities present in sub-daily temperature records is proposed. The approach accounts for changes in the sensor-shield characteristics that affect the energy balance dependent on ambient weather conditions (radiation, wind). An empirical model is formulated that reflects the main atmospheric processes and can be used in the correction step of a homogenization procedure. The model accounts for short- and long-wave radiation fluxes (including a snow cover component for albedo calculation) of a measurement system, such as a radiation shield. One part of the flux is further modulated by ventilation. The model requires only cloud cover and wind speed for each day, but detailed site-specific information is necessary. The final model has three free parameters, one of which is a constant offset. The three parameters can be determined, e.g., using the mean offsets for three observation times. The model is developed using the example of the change from the Wild screen to the Stevenson screen in the temperature record of Basel, Switzerland, in 1966. It is evaluated based on parallel measurements of both systems during a sub-period at this location, which were discovered during the writing of this paper. The model can be used in the correction step of homogenization to distribute a known mean step-size to every single measurement, thus providing a reasonable alternative correction procedure for high-resolution historical climate series. It also constitutes an error model, which may be applied, e.g., in data assimilation approaches.

  13. Modeling daily discharge responses of a large karstic aquifer using soft computing methods: Artificial neural network and neuro-fuzzy

    NASA Astrophysics Data System (ADS)

    Kurtulus, Bedri; Razack, Moumtaz

    2010-02-01

    SummaryThis paper compares two methods for modeling karst aquifers, which are heterogeneous, highly non-linear, and hierarchical systems. There is a clear need to model these systems given the crucial role they play in water supply in many countries. In recent years, the main components of soft computing (fuzzy logic (FL), and Artificial Neural Networks, (ANNs)) have come to prevail in the modeling of complex non-linear systems in different scientific and technologic disciplines. In this study, Artificial Neural Networks and Adaptive Neuro-Fuzzy Interface System (ANFIS) methods were used for the prediction of daily discharge of karstic aquifers and their capability was compared. The approach was applied to 7 years of daily data of La Rochefoucauld karst system in south-western France. In order to predict the karst daily discharges, single-input (rainfall, piezometric level) vs. multiple-input (rainfall and piezometric level) series were used. In addition to these inputs, all models used measured or simulated discharges from the previous days with a specified delay. The models were designed in a Matlab™ environment. An automatic procedure was used to select the best calibrated models. Daily discharge predictions were then performed using the calibrated models. Comparing predicted and observed hydrographs indicates that both models (ANN and ANFIS) provide close predictions of the karst daily discharges. The summary statistics of both series (observed and predicted daily discharges) are comparable. The performance of both models is improved when the number of inputs is increased from one to two. The root mean square error between the observed and predicted series reaches a minimum for two-input models. However, the ANFIS model demonstrates a better performance than the ANN model to predict peak flow. The ANFIS approach demonstrates a better generalization capability and slightly higher performance than the ANN, especially for peak discharges.

  14. A biology-driven receptor model for daily pollen allergy risk in Korea based on Weibull probability density function.

    PubMed

    Kim, Kyu Rang; Kim, Mijin; Choe, Ho-Seong; Han, Mae Ja; Lee, Hye-Rim; Oh, Jae-Won; Kim, Baek-Jo

    2017-02-01

    Pollen is an important cause of respiratory allergic reactions. As individual sanitation has improved, allergy risk has increased, and this trend is expected to continue due to climate change. Atmospheric pollen concentration is highly influenced by weather conditions. Regression analysis and modeling of the relationships between airborne pollen concentrations and weather conditions were performed to analyze and forecast pollen conditions. Traditionally, daily pollen concentration has been estimated using regression models that describe the relationships between observed pollen concentrations and weather conditions. These models were able to forecast daily concentrations at the sites of observation, but lacked broader spatial applicability beyond those sites. To overcome this limitation, an integrated modeling scheme was developed that is designed to represent the underlying processes of pollen production and distribution. A maximum potential for airborne pollen is first determined using the Weibull probability density function. Then, daily pollen concentration is estimated using multiple regression models. Daily risk grade levels are determined based on the risk criteria used in Korea. The mean percentages of agreement between the observed and estimated levels were 81.4-88.2 % and 92.5-98.5 % for oak and Japanese hop pollens, respectively. The new models estimated daily pollen risk more accurately than the original statistical models because of the newly integrated biological response curves. Although they overestimated seasonal mean concentration, they did not simulate all of the peak concentrations. This issue would be resolved by adding more variables that affect the prevalence and internal maturity of pollens.

  15. A biology-driven receptor model for daily pollen allergy risk in Korea based on Weibull probability density function

    NASA Astrophysics Data System (ADS)

    Kim, Kyu Rang; Kim, Mijin; Choe, Ho-Seong; Han, Mae Ja; Lee, Hye-Rim; Oh, Jae-Won; Kim, Baek-Jo

    2017-02-01

    Pollen is an important cause of respiratory allergic reactions. As individual sanitation has improved, allergy risk has increased, and this trend is expected to continue due to climate change. Atmospheric pollen concentration is highly influenced by weather conditions. Regression analysis and modeling of the relationships between airborne pollen concentrations and weather conditions were performed to analyze and forecast pollen conditions. Traditionally, daily pollen concentration has been estimated using regression models that describe the relationships between observed pollen concentrations and weather conditions. These models were able to forecast daily concentrations at the sites of observation, but lacked broader spatial applicability beyond those sites. To overcome this limitation, an integrated modeling scheme was developed that is designed to represent the underlying processes of pollen production and distribution. A maximum potential for airborne pollen is first determined using the Weibull probability density function. Then, daily pollen concentration is estimated using multiple regression models. Daily risk grade levels are determined based on the risk criteria used in Korea. The mean percentages of agreement between the observed and estimated levels were 81.4-88.2 % and 92.5-98.5 % for oak and Japanese hop pollens, respectively. The new models estimated daily pollen risk more accurately than the original statistical models because of the newly integrated biological response curves. Although they overestimated seasonal mean concentration, they did not simulate all of the peak concentrations. This issue would be resolved by adding more variables that affect the prevalence and internal maturity of pollens.

  16. A biology-driven receptor model for daily pollen allergy risk in Korea based on Weibull probability density function

    NASA Astrophysics Data System (ADS)

    Kim, Kyu Rang; Kim, Mijin; Choe, Ho-Seong; Han, Mae Ja; Lee, Hye-Rim; Oh, Jae-Won; Kim, Baek-Jo

    2016-07-01

    Pollen is an important cause of respiratory allergic reactions. As individual sanitation has improved, allergy risk has increased, and this trend is expected to continue due to climate change. Atmospheric pollen concentration is highly influenced by weather conditions. Regression analysis and modeling of the relationships between airborne pollen concentrations and weather conditions were performed to analyze and forecast pollen conditions. Traditionally, daily pollen concentration has been estimated using regression models that describe the relationships between observed pollen concentrations and weather conditions. These models were able to forecast daily concentrations at the sites of observation, but lacked broader spatial applicability beyond those sites. To overcome this limitation, an integrated modeling scheme was developed that is designed to represent the underlying processes of pollen production and distribution. A maximum potential for airborne pollen is first determined using the Weibull probability density function. Then, daily pollen concentration is estimated using multiple regression models. Daily risk grade levels are determined based on the risk criteria used in Korea. The mean percentages of agreement between the observed and estimated levels were 81.4-88.2 % and 92.5-98.5 % for oak and Japanese hop pollens, respectively. The new models estimated daily pollen risk more accurately than the original statistical models because of the newly integrated biological response curves. Although they overestimated seasonal mean concentration, they did not simulate all of the peak concentrations. This issue would be resolved by adding more variables that affect the prevalence and internal maturity of pollens.

  17. Testing the daily PRISM air temperature model on semiarid mountain slopes

    NASA Astrophysics Data System (ADS)

    Strachan, Scotty; Daly, Christopher

    2017-06-01

    Studies in mountainous terrain related to ecology and hydrology often use interpolated climate products because of a lack of local observations. One data set frequently used to develop plot-to-watershed-scale climatologies is PRISM (Parameter-elevation Regression on Independent Slopes Model) temperature. Benefits of this approach include geographically weighted station observations and topographic positioning modifiers, which become important factors for predicting temperature in complex topography. Because of the paucity of long-term climate records in mountain environments, validation of PRISM algorithms across diverse regions remains challenging, with end users instead relying on atmospheric relationships derived in sometimes distant geographic settings. Presented here are results from testing observations of daily temperature maximum (TMAX) and minimum (TMIN) on 16 sites in the Walker Basin, California-Nevada, located on open woodland slopes ranging from 1967 to 3111 m in elevation. Individual site mean absolute error varied from 1.1 to 3.7°C with better performance observed during summertime as opposed to winter. We observed a consistent cool bias in TMIN for all seasons across all sites, with cool bias in TMAX varying with season. Model error for TMIN was associated with elevation, whereas model error for TMAX was associated with topographic radiative indices (solar exposure and heat loading). These results demonstrate that temperature conditions across mountain woodland slopes are more heterogeneous than interpolated models (such as PRISM) predict, that drivers of these differences are complex and localized in nature, and that scientific application of atmospheric/climate models in mountains requires additional attention to model assumptions and source data.

  18. Statistical modeling of daily and subdaily stream temperatures: Application to the Methow River Basin, Washington

    NASA Astrophysics Data System (ADS)

    Caldwell, R. J.; Gangopadhyay, S.; Bountry, J.; Lai, Y.; Elsner, M. M.

    2013-07-01

    Management of water temperatures in the Columbia River Basin (Washington) is critical because water projects have substantially altered the habitat of Endangered Species Act listed species, such as salmon, throughout the basin. This is most important in tributaries to the Columbia, such as the Methow River, where the spawning and rearing life stages of these cold water fishes occurs. Climate change projections generally predict increasing air temperatures across the western United States, with less confidence regarding shifts in precipitation. As air temperatures rise, we anticipate a corresponding increase in water temperatures, which may alter the timing and availability of habitat for fish reproduction and growth. To assess the impact of future climate change in the Methow River, we couple historical climate and future climate projections with a statistical modeling framework to predict daily mean stream temperatures. A K-nearest neighbor algorithm is also employed to: (i) adjust the climate projections for biases compared to the observed record and (ii) provide a reference for performing spatiotemporal disaggregation in future hydraulic modeling of stream habitat. The statistical models indicate the primary drivers of stream temperature are maximum and minimum air temperature and stream flow and show reasonable skill in predictability. When compared to the historical reference time period of 1916-2006, we conclude that increases in stream temperature are expected to occur at each subsequent time horizon representative of the year 2020, 2040, and 2080, with an increase of 0.8 ± 1.9°C by the year 2080.

  19. Hostile Mood and Social Strain during Daily Life: A Test of the Transactional Model

    PubMed Central

    Vella, Elizabeth J.; Kamarck, Thomas W.; Flory, Janine D.; Manuck, Stephen

    2012-01-01

    Background Hostility is a multidimensional construct related to cardiovascular (CV) disease risk. Daily hostile mood and social interactions may precipitate stress-related CV responses in hostile individuals. Purpose Determine whether trait cognitive hostility best predicts daily hostile mood and social interactions relative to other trait hostility factors and explore the temporal links between these daily measures. Methods 171 participants completed assessments of 4 trait hostility scales. Participants completed an electronic diary across 3 days, assessing current hostile mood and social interaction quality. Results Multiple regression analyses revealed both affective and cognitive hostility to be significant predictors of daily hostile mood, and cognitive hostility alone to predict daily social strain. Additional analyses revealed previous social strain to predict elevated subsequent hostile mood. Conclusions Episodes of social strain may give rise to elevated hostile mood. Trait cognitive hostility may be an important factor in predicting daily social strain. PMID:22899302

  20. Multisite multivariate modeling of daily precipitation and temperature in the Canadian Prairie Provinces using generalized linear models

    NASA Astrophysics Data System (ADS)

    Asong, Zilefac E.; Khaliq, M. N.; Wheater, H. S.

    2016-11-01

    Based on the Generalized Linear Model (GLM) framework, a multisite stochastic modelling approach is developed using daily observations of precipitation and minimum and maximum temperatures from 120 sites located across the Canadian Prairie Provinces: Alberta, Saskatchewan and Manitoba. Temperature is modeled using a two-stage normal-heteroscedastic model by fitting mean and variance components separately. Likewise, precipitation occurrence and conditional precipitation intensity processes are modeled separately. The relationship between precipitation and temperature is accounted for by using transformations of precipitation as covariates to predict temperature fields. Large scale atmospheric covariates from the National Center for Environmental Prediction Reanalysis-I, teleconnection indices, geographical site attributes, and observed precipitation and temperature records are used to calibrate these models for the 1971-2000 period. Validation of the developed models is performed on both pre- and post-calibration period data. Results of the study indicate that the developed models are able to capture spatiotemporal characteristics of observed precipitation and temperature fields, such as inter-site and inter-variable correlation structure, and systematic regional variations present in observed sequences. A number of simulated weather statistics ranging from seasonal means to characteristics of temperature and precipitation extremes and some of the commonly used climate indices are also found to be in close agreement with those derived from observed data. This GLM-based modelling approach will be developed further for multisite statistical downscaling of Global Climate Model outputs to explore climate variability and change in this region of Canada.

  1. A novel hybrid forecasting model for PM₁₀ and SO₂ daily concentrations.

    PubMed

    Wang, Ping; Liu, Yong; Qin, Zuodong; Zhang, Guisheng

    2015-02-01

    Air-quality forecasting in urban areas is difficult because of the uncertainties in describing both the emission and meteorological fields. The use of incomplete information in the training phase restricts practical air-quality forecasting. In this paper, we propose a hybrid artificial neural network and a hybrid support vector machine, which effectively enhance the forecasting accuracy of an artificial neural network (ANN) and support vector machine (SVM) by revising the error term of the traditional methods. The hybrid methodology can be described in two stages. First, we applied the ANN or SVM forecasting system with historical data and exogenous parameters, such as meteorological variables. Then, the forecasting target was revised by the Taylor expansion forecasting model using the residual information of the error term in the previous stage. The innovation involved in this approach is that it sufficiently and validly utilizes the useful residual information on an incomplete input variable condition. The proposed method was evaluated by experiments using a 2-year dataset of daily PM₁₀ (particles with a diameter of 10 μm or less) concentrations and SO₂ (sulfur dioxide) concentrations from four air pollution monitoring stations located in Taiyuan, China. The theoretical analysis and experimental results demonstrated that the forecasting accuracy of the proposed model is very promising.

  2. Coping with Daily Stressors: Modeling Intraethnic Variation in Mexican American Adolescents

    ERIC Educational Resources Information Center

    Aldridge, Arianna A.; Roesch, Scott C.

    2008-01-01

    Using daily diary methodology, 67 Mexican American adolescents completed measures assessing daily stressors experienced, specific coping strategies employed with reference to these stressors, and indices of psychological health over 5 consecutive days. With respect to coping usage, adolescents reported they most often used planning and least often…

  3. Coping with Daily Stressors: Modeling Intraethnic Variation in Mexican American Adolescents

    ERIC Educational Resources Information Center

    Aldridge, Arianna A.; Roesch, Scott C.

    2008-01-01

    Using daily diary methodology, 67 Mexican American adolescents completed measures assessing daily stressors experienced, specific coping strategies employed with reference to these stressors, and indices of psychological health over 5 consecutive days. With respect to coping usage, adolescents reported they most often used planning and least often…

  4. Differential Associations and Daily Smoking of Adolescents: The Importance of Same-Sex Models

    ERIC Educational Resources Information Center

    Nofziger, Stacey; Lee, Hye-Ryeon

    2006-01-01

    This article examines whether the importance of parents, siblings, best friends, and romantic interests are sex-specific in predicting daily juvenile smoking. Juveniles who smoke daily are strongly influenced by prosmoking attitudes and behaviors of same-sex family members. However, peers remain the most important associations in predicting daily…

  5. Modeling aspen and red pine shoot growth to daily weather variations.

    Treesearch

    Donald A. Perala

    1983-01-01

    Quantifies daily shoot growth of quaking aspen and red pine in response to daily variation in air temperature, soil moisture, solar radiation, evapotranspiration, and inherent seasonal plant growth rhythm. Discusses potential application of shoot growth equations to silvicultural problems related to microclimatic variation. Identifies limitations and areas for...

  6. Online Recognition of Daily Activities by Color-Depth Sensing and Knowledge Models

    PubMed Central

    Gómez Uría, Alvaro; Strumia, Carola; Koperski, Michal; König, Alexandra; Negin, Farhood; Cosar, Serhan; Nghiem, Anh Tuan; Chau, Duc Phu; Charpiat, Guillaume; Bremond, Francois

    2017-01-01

    Visual activity recognition plays a fundamental role in several research fields as a way to extract semantic meaning of images and videos. Prior work has mostly focused on classification tasks, where a label is given for a video clip. However, real life scenarios require a method to browse a continuous video flow, automatically identify relevant temporal segments and classify them accordingly to target activities. This paper proposes a knowledge-driven event recognition framework to address this problem. The novelty of the method lies in the combination of a constraint-based ontology language for event modeling with robust algorithms to detect, track and re-identify people using color-depth sensing (Kinect® sensor). This combination enables to model and recognize longer and more complex events and to incorporate domain knowledge and 3D information into the same models. Moreover, the ontology-driven approach enables human understanding of system decisions and facilitates knowledge transfer across different scenes. The proposed framework is evaluated with real-world recordings of seniors carrying out unscripted, daily activities at hospital observation rooms and nursing homes. Results demonstrated that the proposed framework outperforms state-of-the-art methods in a variety of activities and datasets, and it is robust to variable and low-frame rate recordings. Further work will investigate how to extend the proposed framework with uncertainty management techniques to handle strong occlusion and ambiguous semantics, and how to exploit it to further support medicine on the timely diagnosis of cognitive disorders, such as Alzheimer’s disease. PMID:28661440

  7. Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modeling heteroscedastic residual errors

    NASA Astrophysics Data System (ADS)

    McInerney, David; Thyer, Mark; Kavetski, Dmitri; Lerat, Julien; Kuczera, George

    2017-03-01

    Reliable and precise probabilistic prediction of daily catchment-scale streamflow requires statistical characterization of residual errors of hydrological models. This study focuses on approaches for representing error heteroscedasticity with respect to simulated streamflow, i.e., the pattern of larger errors in higher streamflow predictions. We evaluate eight common residual error schemes, including standard and weighted least squares, the Box-Cox transformation (with fixed and calibrated power parameter λ) and the log-sinh transformation. Case studies include 17 perennial and 6 ephemeral catchments in Australia and the United States, and two lumped hydrological models. Performance is quantified using predictive reliability, precision, and volumetric bias metrics. We find the choice of heteroscedastic error modeling approach significantly impacts on predictive performance, though no single scheme simultaneously optimizes all performance metrics. The set of Pareto optimal schemes, reflecting performance trade-offs, comprises Box-Cox schemes with λ of 0.2 and 0.5, and the log scheme (λ = 0, perennial catchments only). These schemes significantly outperform even the average-performing remaining schemes (e.g., across ephemeral catchments, median precision tightens from 105% to 40% of observed streamflow, and median biases decrease from 25% to 4%). Theoretical interpretations of empirical results highlight the importance of capturing the skew/kurtosis of raw residuals and reproducing zero flows. Paradoxically, calibration of λ is often counterproductive: in perennial catchments, it tends to overfit low flows at the expense of abysmal precision in high flows. The log-sinh transformation is dominated by the simpler Pareto optimal schemes listed above. Recommendations for researchers and practitioners seeking robust residual error schemes for practical work are provided.

  8. Daily sesame oil supplement attenuates joint pain by inhibiting muscular oxidative stress in osteoarthritis rat model.

    PubMed

    Hsu, Dur-Zong; Chu, Pei-Yi; Jou, I-Ming

    2016-03-01

    Osteoarthritis (OA) is the most common form of arthritis, affecting approximately 15% of the population. The aim of this study was to evaluate the efficacy of sesame oil in controlling OA pain in rats. Rat joint pain was induced by medial meniscal transection in Sprague-Dawley rats and assessed by using hindlimb weight distribution method. Muscular oxidative stress was assessed by determining lipid peroxidation, reactive oxygen species and circulating antioxidants. Sesame oil significantly decreased joint pain compared with positive control group in a dose-dependent manner. Sesame oil decreased lipid peroxidation in muscle but not in serum. Further, sesame oil significantly decreased muscular superoxide anion and peroxynitrite generations but increased muscular glutathione and glutathione peroxidase levels. Further, sesame oil significantly increased nuclear factor erythroid-2-related factor (Nrf2) expression compared with positive control group. We concluded that daily sesame oil supplement may attenuate early joint pain by inhibiting Nrf2-associated muscular oxidative stress in OA rat model. Copyright © 2015 Elsevier Inc. All rights reserved.

  9. Comparative efficacy of two daily use mouthrinses: randomized clinical trial using an experimental gingivitis model.

    PubMed

    Charles, Christine Ann; McGuire, James Anthony; Sharma, Naresh Chandra; Qaqish, James

    2011-01-01

    Two antimicrobial agents, a fixed combination of essential oils (EOs) and 0.07% cetylpyridinium chloride (CPC) are found in commercially available mouthrinses, Listerine® Antiseptic and Crest® Pro HealthTM, respectively. Both mouthrinses have been shown to control dental plaque and gingivitis in short and longer term studies. The aim of this study was to determine the comparative effectiveness of these two mouthrinses using a 2-week experimental gingivitis model. Qualified subjects were randomly assigned to one of three mouthrinse groups: a fixed combination of EOs, 0.07% CPC, or negative control (C) rinse. Following baseline clinical assessments and a dental prophylaxis, subjects began a two-week period in which they rinsed twice daily with their assigned rinse and abstained from any mechanical oral hygiene procedures or other oral care products. Subjects were reassessed at the end of the two-week period. One hundred and forty-seven subjects were randomized and 142 completed this study. After two weeks use, the EOs rinse was superior (p < 0.011) to the CPC rinse in inhibiting the development of gingivitis, plaque, and bleeding, with 9.4% and 6.6% reductions compared to CPC for gingivitis and plaque, respectively. Both rinses were superior to the negative control rinse (p < 0.001). This study demonstrates that the essential oil-containing mouthrinse has superior antiplaque/antigingivitis effectiveness compared to the 0.07% CPC-containing mouthrinse without mechanical oral hygiene influence.

  10. Functional and psychological variables both affect daily physical activity in COPD: a structural equations model.

    PubMed

    Altenburg, Wytske A; Bossenbroek, Linda; de Greef, Mathieu H G; Kerstjens, Huib A M; ten Hacken, Nick H T; Wempe, Johan B

    2013-11-01

    Daily physical activity (DPA) level is reduced in patients with COPD. The aim of this study was to investigate the association of DPA with functional and psychological variables in these patients. 155 COPD patients (102 males, median (IQR) age 62 years (54-69 years), predicted FEV1 60% (40-75%) were included. We assessed DPA (DigiWalker SW-200), functional capacity and psychological factors. DPA level was significantly associated with all functional capacity variables and two psychological variables (Perceived Physical Ability Subscale, depression subscale of the Hospital Anxiety and Depression Scale). The six-minute walking distance and St. George Respiratory Questionnaire activity score explained 37% of the variance of DPA in a regression analysis. A structural equations model revealed that psychological variables indirectly explained DPA through functional capacity variables. DPA was stronger associated with functional capacity variables and weaker with psychological variables in patients with lower functional status than in patients with higher functional status. Higher levels of DPA are associated with better functional capacity, but interestingly, DPA is also affected by psychological factors, though only indirectly, via functional capacity. The effect of specific treatment addressing psychological factors on DPA level and exercise tolerance needs further investigation. ClinicalTrials.gov, NCT00614796. Copyright © 2013 Elsevier Ltd. All rights reserved.

  11. Modeling and forecasting daily movement of ambient air mean PM₂.₅ concentration based on the elliptic orbit model with weekly quasi-periodic extension: a case study.

    PubMed

    Yang, Zong-chang

    2014-01-01

    Nowadays, the issue of air pollution has continuously been a global public health concern. Modeling and forecasting daily movement of ambient air mean PM2.5 concentration is an increasingly important task as it is intimately associated with human health that the air pollution has unignorable negative effects in reducing air quality, damaging environment, even causing serious harm to health. It is demonstrated that daily movement of mean PM₂.₅ concentration approximately exhibits weekly cyclical variations as daily particle pollution in the air is largely influenced by human daily activities. Then, based on weekly quasi-periodic extension for daily movement of mean PM₂.₅ concentration, the called elliptic orbit model is proposed to describe its movement. By mapping daily movement of mean PM₂.₅ concentration as one time series into the polar coordinates, each 7-day movement is depicted as one elliptic orbit. Experimental result and analysis indicate workability and effectiveness of the proposed method. Here we show that with the weekly quasi-periodic extension, daily movements of mean PM₂.₅ concentration at the given monitoring stations in Xiangtan of China are well described by the elliptic orbit model, which provides a vivid description for modeling and prediction daily movement of mean PM₂.₅ concentration in a concise and intuitive way.

  12. Modeling Approach to Estimate Daily Streamflow Characteristics at Monitoring Sites in New Jersey

    NASA Astrophysics Data System (ADS)

    Kauffman, L. J.; Ayers, M. A.; Wolock, D. M.

    2001-05-01

    Results of a recent U.S. Geological Survey (USGS) study of streams in northern New Jersey confirm the concern of many ecologists that the maintenance of forest cover, stable base flows, cobble habitats, and functioning wetland systems are critical to stream biological communities. In the study, biological communities were found to be affected adversely by increases in peak discharge, streamflow variability, impervious surfaces, and phosphorus concentrations--all of which are related to increases in human population density. The effect of changes in streamflow on aquatic community health led the USGS, in cooperation with the New Jersey Department of Environmental Protection, to develop a watershed modeling approach to define the flow characteristics associated with each of the 820 biologic monitoring sites in New Jersey. The flow characteristics generated with the model will provide key information for an assessment of the major factors that contribute to the current status of benthic invertebrate community health. The model also will be incorporated in a State planning "toolbox" for use in local and regional land-use planning. The watershed model uses daily precipitation and temperature values from 1948 to present that are estimated for each watershed by using a spatial regression approach with available National Weather Service station data. A topographic wetness index, derived from digital topography, is used in conjunction with soil depth and permeability as the basis for pervious-area runoff estimates. Pervious-area runoff parameters are calibrated to USGS streamflow data for watersheds with less than 5 percent total impervious surface cover (ISC). The parameters then are regionalized. With pervious-area parameters set, the impervious-area runoff is calibrated for gaged urban watersheds (ISC greater than 5 percent). Differences between observed and predicted runoff are assumed to be the result of changes in runoff caused by urban development. The effective

  13. Observation and modeling of thermoelastic strain in Southern California Integrated GPS Network daily position time series

    NASA Astrophysics Data System (ADS)

    Prawirodirdjo, Linette; Ben-Zion, Yehuda; Bock, Yehuda

    2006-02-01

    We suggest that strain in the elastic part of the Earth's crust induced by surface temperature variations is a significant contributor to the seasonal variations observed in the spatially filtered daily position time series of Southern California Integrated GPS Network (SCIGN) stations. We compute the predicted thermoelastic strain from the observed local atmospheric temperature record assuming an elastically decoupled layer over a uniform elastic half-space and compare the seasonal variations in thermoelastic strain to the horizontal GPS position time series. We consider three regions (Palmdale, 29 Palms, and Idyllwild), each with one temperature station and three to six GPS stations. The temperature time series is used to compute thermoelastic strain at each station on the basis of its relative location in the temperature field. For each region we assume a wavelength for the temperature field that is related to the local topography. The depth of the decoupled layer is inferred from the phase delay between the temperature record and the GPS time series. The relative amplitude of strain variation at each GPS station, calculated to be on the order of 0.1 μstrain, is related to the relative location of that station in the temperature field. The goodness of fit between model and data is evaluated from the relative amplitudes of the seasonal signals, as well as the appropriateness of the chosen temperature field wavelength and decoupled layer depth. The analysis shows a good fit between the predicted strains and the GPS time series. This suggests that the model captures the key first-order ingredients that determine the thermoelastic strain in a given area. The results can be used to improve the signal/noise ratio in GPS data.

  14. Development of models to inform a national Daily Landslide Hazard Assessment for Great Britain

    NASA Astrophysics Data System (ADS)

    Dijkstra, Tom A.; Reeves, Helen J.; Dashwood, Claire; Pennington, Catherine; Freeborough, Katy; Mackay, Jonathan D.; Uhlemann, Sebastian S.; Chambers, Jonathan E.; Wilkinson, Paul B.

    2015-04-01

    were combined with records of observed landslide events to establish which antecedent effective precipitation (AEP) signatures of different duration could be used as a pragmatic proxy for the occurrence of landslides. It was established that 1, 7, and 90 days AEP provided the most significant correlations and these were used to calculate the probability of at least one landslide occurring. The method was then extended over the period 2006 to 2014 and the results evaluated against observed occurrences. It is recognised that AEP is a relatively poor proxy for simulating effective stress conditions along potential slip surfaces. However, the temporal pattern of landslide probability compares well to the observed occurrences and provides a potential benefit to assist with the DLHA. Further work is continuing to fine-tune the model for landslide type, better spatial resolution of effective precipitation input and cross-reference to models that capture changes in water balance and conditions along slip surfaces. The latter is facilitated by intensive research at several field laboratories, such as the Hollin Hill site in Yorkshire, England. At this site, a decade of activity has generated a broad range of research and a wealth of data. This paper reports on one example of recent work; the characterisation of near surface hydrology using infiltration experiments where hydrological pathways are captured, among others, by electrical resistivity tomography. This research, which has further developed our understanding of soil moisture movement in a heterogeneous landslide complex, has highlighted the importance of establishing detailed ground models to enable determination of landslide potential at high resolution. In turn, the knowledge gained through this research is used to enhance the expertise for the daily landslide hazard assessments at a national scale.

  15. A general model for estimation of daily global solar radiation using air temperatures and site geographic parameters in Southwest China

    NASA Astrophysics Data System (ADS)

    Li, Mao-Fen; Fan, Li; Liu, Hong-Bin; Guo, Peng-Tao; Wu, Wei

    2013-01-01

    Estimation of daily global solar radiation (Rs) from routinely measured temperature data has been widely developed and used in many different areas of the world. However, many of them are site specific. It is assumed that a general model for estimating daily Rs using temperature variables and geographical parameters could be achieved within a climatic region. This paper made an attempt to develop a general model to estimate daily Rs using routinely measured temperature data (maximum (Tmax, °C) and minimum (Tmin, °C) temperatures) and site geographical parameters (latitude (La, °N), longitude (Ld, °E) and altitude (Alt, m)) for Guizhou and Sichuan basin of southwest China, which was classified into the hot summer and cold winter climate zone. Comparison analysis was carried out through statistics indicators such as root mean squared error of percentage (RMSE%), modeling efficiency (ME), coefficient of residual mass (CRM) and mean bias error (MBE). Site-dependent daily Rs estimating models were calibrated and validated using long-term observed weather data. A general formula was then obtained from site geographical parameters and the better fit site-dependent models with mean RMSE% of 38.68%, mean MBE of 0.381 MJ m-2 d-1, mean CRM of 0.04 and mean ME value of 0.713.

  16. [Multilevel model analysis on the relevant factors influencing the total amount of drinking water consumed daily by Beijing residents].

    PubMed

    Zhao, Jinhui; Wei, Jianrong; Chen, Huajie; Liu, Yumin; Li, Tiantian; Sun, Qinghua; Liu, Qiaolan

    2012-09-01

    To investigate the influencing factors for daily water intake of Beijing residents. A multi-stage sampling method was constructed to interview 270 Beijing residents in the winter of 2009 and in the summer of 2010 by using a questionnaire to collect data on daily drinking water consumption. Multilevel models were used to analyze the variation and influencing factors for the amount of water intake. Multilevel model results showed that the average daily water intake of residents living in different villages or neighborhood committees was statistically significant (sigma2 mu0 = = 0.030 (0.009), P < 0.05). The individual variation in the same village or neighborhood committee was also significant (sigma2 e0 = 0.157 (0.010), P < 0.05). Season, gender, and body weight affected the daily water intake (P < 0.05). There were interaction between season and source of water supply. The average daily water intake of residents was affected by several factors. In the health risk assessment of drinking water, it needs considering not only the individual characteristics but also the differences of villages/neighborhood committees and the seasonal variation.

  17. A gentamicin pharmacokinetic population model and once-daily dosing algorithm for neonates.

    PubMed

    DiCenzo, Robert; Forrest, Alan; Slish, Judianne C; Cole, Carol; Guillet, Ronnie

    2003-05-01

    To develop a gentamicin pharmacokinetic population model and once-daily dosing algorithm for neonates younger than 10 days. Prospective, open-label study. Neonatal intensive care unit. One hundred thirty-nine neonates prescribed gentamicin. Gentamicin peak and trough serum concentrations were collected from 139 neonates divided into three groups who were receiving one of the following intravenous 24-hour gentamicin regimens during the first 10 days of life, based on gestational age and birth weight (group 1, < 28 wks, 2.5 mg/kg; group 2, 28-34 wks, 3 mg/kg; and group 3, > 34 wks, 4 mg/kg). A structural model was developed in ADAPT II software using a MAP Bayesian approach. Final population parameter estimates were calculated using iterative two-stage analysis. The median (range) gestational age and birth weight, respectively, were 32 weeks (23-42 wks) and 1.92 kg (0.47-5.00 kg). The final one-compartmental linear model had a median (range) gentamicin total clearance, half-life, and volume of distribution of 0.0709 L/hour (0.0151-0.246 L/hr), 8.59 hours (4.88-16.9 hrs), and 0.262 L (0.0903-0.929 L), respectively. Total clearance increased as gestational age increased (p<0.001). Group 1 (10.2 hrs) had a significantly longer half-life than either group 2 (8.89 hrs, p<0.01) or group 3 (6.98 hrs, p<0.01). Total clearance was associated with gestational age and birth weight: clearance (L/hr) = (0.00504 + [0.00108 x gestational age]) x birth weight (coefficient of determination [r2] = 0.897), and volume of distribution was associated with birth weight (r2 = 0.700). The following dosing algorithm was designed to reach a therapeutic 24-hour area under the curve (87.5 mg/L x hr) in neonates during the first 10 days after birth: 24-hour gentamicin dose (mg) = (0.441 + [0.0945 x gestational age]) x birth weight. This dosing algorithm provides a new approach for determining initial gentamicin dosing regimens in neonates; however, clinical validation is required.

  18. [Does daily hemodialysis influence urea kinetic modeling (UKM) coefficients?--Preliminary report].

    PubMed

    Korohoda, Przemysław; Pietrzyk, Jacek A; Miklaszewska, Monika; Komorowska, Małgorzata; Rumian, Roman; Drozdz, Dorota; Krawentek, Lidia; Zachwieja, Katarzyna

    2006-01-01

    Large number of data shows beneficial effects of implementing daily hemodialysis (DH) upon the outcome in patients dialysed previously in 3 times a week hemodialysis (3H) schedule. The mechanisms responsible for this phenomenon are still unclear, despite the time of low-flux DH sessions is shortened almost by half. Evaluation of the effect of doubling the number of hemodialyses per week upon so called cellular clearance (intercompartmental diffusion coefficient, Kc) computed in 2 pool-model was main aim of this study. 6 chronically dialysed patients (previously 3x per week) were subjected to DH. Based upon output data from UKM and weekly KT/V, the time for each DH session was computed, with no change in Kd (dialyser clearance). Kc was estimated from double-pool volume variable model equations and rebound. By the use of almost similar dialyser clearances in 16 conventional and 29 DH modeling sessions, estimated Kc values had been found non significantly higher in DH: (323.16; S.D. 187.86 vs. 268.80; S.D. 104.09 ml/min; p=0,68). Mean ultrafiltration/pre-dialysis body weight ratio (UFR/BW1) was 4,97 (S.D. 2.27)% in conventional hemodialysis and 3.66 (S.D. 1.46)% in DH. Mean dialysis index Kt/V values had decreased in DH (0.79; S.D. 0.17, vs. 1,34 (S.D. 0.26). Mean UFR/W1 ratio correlated negatively with Kc either in conventional or in DH (r=-0.653; p = 0.006 and r=-0.552; p=0.0036, respectively). Statistically significant negative correlation between Kt/V and Kc was found only in DH subjects (r=-0.466, p =0.010). The authors concluded, that increased Kc observed in patients subjected to DH may be responsible for better dialysis efficacy in patients switched into this treatment modality.

  19. A spatial-temporal regression model to predict daily outdoor residential PAH concentrations in an epidemiologic study in Fresno, CA

    NASA Astrophysics Data System (ADS)

    Noth, Elizabeth M.; Hammond, S. Katharine; Biging, Gregory S.; Tager, Ira B.

    2011-05-01

    BackgroundPolycyclic aromatic hydrocarbons (PAHs) are generated as a byproduct of combustion, and are associated with respiratory symptoms and increased risk of asthma attacks. ObjectivesTo assign daily, outdoor exposures to participants in the Fresno Asthmatic Children's Environment Study (FACES) using land use regression models for the sum of 4-, 5- and 6-ring PAHs (PAH456). MethodsPAH data were collected daily at the EPA Supersite in Fresno, CA from 10/2000 through 2/2007. From 2/2002 to 2/2003, intensive air pollution sampling was conducted at 83 homes of participants in the FACES study. These measurement data were combined with meteorological data, source data, and other spatial variables to form a land use regression model to assign daily exposure at all FACES homes for all years of the study (2001-2008). ResultsThe model for daily, outdoor residential PAH456 concentrations accounted for 80% of the between-home variability and 18% of the within-home variability. Both temporal and spatial variables were significant in the model. Traffic characteristics and home heating fuel were the main spatial explanatory variables. ConclusionsBecause spatial and temporal distributions of PAHs vary on an intra-urban scale, the location of the child's home within the urban setting plays an important role in the level of exposure that each child has to PAHs.

  20. Technical evaluation of a total maximum daily load model for Upper Klamath and Agency Lakes, Oregon

    USGS Publications Warehouse

    Wood, Tamara M.; Wherry, Susan A.; Carter, James L.; Kuwabara, James S.; Simon, Nancy S.; Rounds, Stewart A.

    2013-01-01

    We reviewed a mass balance model developed in 2001 that guided establishment of the phosphorus total maximum daily load (TMDL) for Upper Klamath and Agency Lakes, Oregon. The purpose of the review was to evaluate the strengths and weaknesses of the model and to determine whether improvements could be made using information derived from studies since the model was first developed. The new data have contributed to the understanding of processes in the lakes, particularly internal loading of phosphorus from sediment, and include measurements of diffusive fluxes of phosphorus from the bottom sediments, groundwater advection, desorption from iron oxides at high pH in a laboratory setting, and estimates of fluxes of phosphorus bound to iron and aluminum oxides. None of these processes in isolation, however, is large enough to account for the episodically high values of whole-lake internal loading calculated from a mass balance, which can range from 10 to 20 milligrams per square meter per day for short periods. The possible role of benthic invertebrates in lake sediments in the internal loading of phosphorus in the lake has become apparent since the development of the TMDL model. Benthic invertebrates can increase diffusive fluxes several-fold through bioturbation and biodiffusion, and, if the invertebrates are bottom feeders, they can recycle phosphorus to the water column through metabolic excretion. These organisms have high densities (1,822–62,178 individuals per square meter) in Upper Klamath Lake. Conversion of the mean density of tubificid worms (Oligochaeta) and chironomid midges (Diptera), two of the dominant taxa, to an areal flux rate based on laboratory measurements of metabolic excretion of two abundant species suggested that excretion by benthic invertebrates is at least as important as any of the other identified processes for internal loading to the water column. Data from sediment cores collected around Upper Klamath Lake since the development of the

  1. Development and evaluation of a daily temporal interpolation model for fine particulate matter species concentrations and source apportionment

    NASA Astrophysics Data System (ADS)

    Redman, Jeremiah D.; Holmes, Heather A.; Balachandran, Sivaraman; Maier, Marissa L.; Zhai, Xinxin; Ivey, Cesunica; Digby, Kyle; Mulholland, James A.; Russell, Armistead G.

    2016-09-01

    The impacts of emissions sources on air quality in St. Louis, Missouri are assessed for use in acute health effects studies. However, like many locations in the United States, the speciated particulate matter (PM) measurements from regulatory monitoring networks in St. Louis are only available every third day. The power of studies investigating acute health effects of air pollution is reduced when using one-in-three day source impacts compared to daily source impacts. This paper presents a temporal interpolation model to estimate daily speciated PM2.5 mass concentrations and source impact estimates using one-in-three day measurements. The model is used to interpolate 1-in-3 day source impact estimates and to interpolate the 1-in-3 day PM species concentrations prior to source apportionment (SA). Both approaches are compared and evaluated using two years (June 2001-May 2003) of daily data from the St. Louis Midwest Supersite (STL-SS). Data withholding is used to simulate a 1-in-3 day data set from the daily data to evaluate interpolated estimates. After evaluation using the STL-SS data, the model is used to estimate daily source impacts at another site approximately seven kilometers (7 km) northwest of the STL-SS (Blair); results between the sites are compared. For interpolated species concentrations, the model performs better for secondary species (sulfate, nitrate, ammonium, and organic carbon) than for primary species (metals and elemental carbon), likely due to the greater spatial autocorrelation of secondary species. Pearson correlation (R) values for sulfate, nitrate, ammonium, elemental carbon, and organic carbon ranged from 0.61 (elemental carbon, EC2) to 0.97 (sulfate). For trace metals, the R values ranged from 0.31 (Ba) to 0.81 (K). The interpolated source impact estimates also indicated a stronger correlation for secondary sources. Correlations of the secondary source impact estimates based on measurement data and interpolation data ranged from 0.68 to 0

  2. Comparison of Daily Total Precipitable Water From Satellite and Model Reanalysis Fields

    NASA Technical Reports Server (NTRS)

    Jedlovec, Gary J.; Suggs, R. J.; Haines, S. L.

    2000-01-01

    Previous studies have shown that there is fairly good agreement between the monthly values of total precipitable water (TPW) from the NVAP data set, a multi satellite and radiosonde merged data product, and the NCEP reanalysis TPW data. However, there are regions and time periods where significant differences between the data sets are found in the monthly mean TPW time series and period of record trends. To better understand these differences seen in the monthly data values, the daily mean TPW data are examined. Possible contributions to the TPW differences such as data quality of the individual NVAP data set components associated with missing or irregular daily values are addressed. Also, effects associated with the weighting of the individual NVAP data set components to form a merged product are considered. The daily variations in TPW exhibited by each data set are also quantified and compared between the data sets.

  3. Comparison of Daily Total Precipitable Water From Satellite and Model Reanalysis Fields

    NASA Technical Reports Server (NTRS)

    Jedlovec, Gary J.; Suggs, R. J.; Haines, S. L.

    2000-01-01

    Previous studies have shown that there is fairly good agreement between the monthly values of total precipitable water (TPW) from the NVAP data set, a multi satellite and radiosonde merged data product, and the NCEP reanalysis TPW data. However, there are regions and time periods where significant differences between the data sets are found in the monthly mean TPW time series and period of record trends. To better understand these differences seen in the monthly data values, the daily mean TPW data are examined. Possible contributions to the TPW differences such as data quality of the individual NVAP data set components associated with missing or irregular daily values are addressed. Also, effects associated with the weighting of the individual NVAP data set components to form a merged product are considered. The daily variations in TPW exhibited by each data set are also quantified and compared between the data sets.

  4. One-Day Offset between Simulated and Observed Daily Hydrographs: An Exploration of the Issue in Automatic Model Calibration

    NASA Astrophysics Data System (ADS)

    Asadzadeh, M.; Leon, L.; Yang, W.

    2014-12-01

    The literature of hydrologic modelling shows that in daily simulation of the rainfall-runoff relationship, the simulated hydrograph response to some rainfall events happens one day earlier than the observed one. This one-day offset issue results in significant residuals between the simulated and observed hydrographs and adversely impacts the model performance metrics that are based on the aggregation of daily residuals. Based on the analysis of sub-daily rainfall and runoff data sets in this study, the one-day offset issue appears to be inevitable when the same time interval, e.g. the calendar day, is used to measure daily rainfall and runoff data sets. This is an error introduced through data aggregation and needs to be properly addressed before calculating the model performance metrics. Otherwise, the metrics would not represent the modelling quality and could mislead the automatic calibration of the model. In this study, an algorithm is developed to scan the simulated hydrograph against the observed one, automatically detect all one-day offset incidents and shift the simulated hydrograph of those incidents one day forward before calculating the performance metrics. This algorithm is employed in the automatic calibration of the Soil and Water Assessment Tool that is set up for the Rouge River watershed in Southern Ontario, Canada. Results show that with the proposed algorithm, the automatic calibration to maximize the daily Nash-Sutcliffe (NS) identifies a solution that accurately estimates the magnitude of peak flow rates and the shape of rising and falling limbs of the observed hydrographs. But, without the proposed algorithm, the same automatic calibration finds a solution that systematically underestimates the peak flow rates in order to perfectly match the timing of simulated and observed peak flows.

  5. Daily fluctuations in teachers' well-being: a diary study using the Job Demands-Resources model.

    PubMed

    Simbula, Silvia

    2010-10-01

    The study tests the dynamic nature of the Job Demands-Resources model with regard to both motivational and health impairment processes. It does so by examining whether daily fluctuations in co-workers' support (i.e., a typical job resource) and daily fluctuations in work/family conflict (i.e., a typical job demand) predict day-levels of job satisfaction and mental health through work engagement and exhaustion, respectively. A total of 61 schoolteachers completed a general questionnaire and a daily survey over a period of five consecutive work days. Multilevel analyses provided evidence for both the above processes. Consistently with the hypotheses, our results showed that day-level work engagement mediated the impact of day-level co-workers' support on day-level job satisfaction and day-level mental health, after general levels of work engagement and outcome variables had been controlled for. Moreover, day-level exhaustion mediated the relationship between day-level work/family conflict and day-level job satisfaction and day-level mental health after general levels of exhaustion and outcome variables had been controlled for. These findings provide new insights into the dynamic psychological processes that determine daily fluctuations in employee well-being. Such insights may be transformed into job redesign strategies and other interventions designed to enhance work-related psychological well-being on a daily level.

  6. Stochastic modelling of spatially and temporally consistent daily precipitation time-series over complex topography

    NASA Astrophysics Data System (ADS)

    Keller, Denise E.; Fischer, Andreas M.; Frei, Christoph; Liniger, Mark A.; Appenzeller, Christof; Knutti, Reto

    2014-05-01

    There is a growing demand for high-resolution precipitation time-series at the local scale that are both consistent in time and in space. This is of high relevance for climate impact models that are sensitive to averaged rainfall amount over a specific region and over a multi-day period (e.g. for modeling river runoff regimes). In this regard, weather generators (WGs) calibrated at multiple sites, are an appealing technique that allow the simulation of synthetic series of unlimited length taking into account the spatio-temporal correlation structure. To date, only a few multi-site WGs have been documented in the literature and those that exist have rarely been tested for a topography as complex as the Alps. It is the aim of this study to fill this gap. Here, we present results from multi-site precipitation simulations with a generator that has been inspired by Wilks (1998). In essence, it is a Richardson-type WG that additionally takes into account the spatial correlation structure between all the station pairs. A first-order two-state Markov process is chosen to simulate daily precipitation occurrences, while precipitation amounts are re-sampled from a mixture model of two exponential distributions fitted at individual stations. Our multi-site WG is tested and evaluated here at the example of the hydrological catchment "Thur" in the Swiss Alps for the time-period 1961-2011 and on a monthly basis. In the catchment eight meteorological stations (from MeteoSwiss) are considered at which artificial time-series with the respective spatio-temporal dependence structure are simulated. The eight measurement sites are evenly distributed over the catchment, representing the complex topographical and associated precipitation characteristics. The study reveals first that our stochastic model is able to generate time-series that well represent the annual cycle of the precipitation statistics, such as mean wet day frequency and intensity as well as accumulated precipitation

  7. Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes

    USGS Publications Warehouse

    Yuan, W.; Liu, S.; Zhou, G.; Tieszen, L.L.; Baldocchi, D.; Bernhofer, C.; Gholz, H.; Goldstein, Allen H.; Goulden, M.L.; Hollinger, D.Y.; Hu, Y.; Law, B.E.; Stoy, Paul C.; Vesala, T.; Wofsy, S.C.

    2007-01-01

    The quantitative simulation of gross primary production (GPP) at various spatial and temporal scales has been a major challenge in quantifying the global carbon cycle. We developed a light use efficiency (LUE) daily GPP model from eddy covariance (EC) measurements. The model, called EC-LUE, is driven by only four variables: normalized difference vegetation index (NDVI), photosynthetically active radiation (PAR), air temperature, and the Bowen ratio of sensible to latent heat flux (used to calculate moisture stress). The EC-LUE model relies on two assumptions: First, that the fraction of absorbed PAR (fPAR) is a linear function of NDVI; Second, that the realized light use efficiency, calculated from a biome-independent invariant potential LUE, is controlled by air temperature or soil moisture, whichever is most limiting. The EC-LUE model was calibrated and validated using 24,349 daily GPP estimates derived from 28 eddy covariance flux towers from the AmeriFlux and EuroFlux networks, covering a variety of forests, grasslands and savannas. The model explained 85% and 77% of the observed variations of daily GPP for all the calibration and validation sites, respectively. A comparison with GPP calculated from the Moderate Resolution Imaging Spectroradiometer (MODIS) indicated that the EC-LUE model predicted GPP that better matched tower data across these sites. The realized LUE was predominantly controlled by moisture conditions throughout the growing season, and controlled by temperature only at the beginning and end of the growing season. The EC-LUE model is an alternative approach that makes it possible to map daily GPP over large areas because (1) the potential LUE is invariant across various land cover types and (2) all driving forces of the model can be derived from remote sensing data or existing climate observation networks.

  8. A NEW MODEL TO ESTIMATE DAILY ENERGY EXPENDITURE FOR WINTERING WATERFOWL

    EPA Science Inventory

    Activity budgets of wintering waterfowl have been widely used to assess habitat quality. However, when factors such as prey abundance or protection from exposure to cold or wind determine quality, measures of daily energy expenditure (DEE) may be more appropriate for this purpos...

  9. Suitability of global circulation model downscaled BCCA daily precipitation for local hydrologic applications

    USDA-ARS?s Scientific Manuscript database

    Monthly precipitation projections for various climate change scenarios have been available for over a decade. More recently, Bias Corrected Constructed Analogue (BCCA) daily precipitation projections have been available for climate change investigations. In this study, the direct use of BCCA precipi...

  10. Harmful effects behind the daily supplementation of a fixed vegetarian blend in the rat model.

    PubMed

    Bonamassa, Barbara; Canistro, Donatella; Sapone, Andrea; Vivarelli, Fabio; Vornoli, Andrea; Longo, Vincenzo; Paolini, Moreno

    2016-11-01

    Fruit and vegetables (FV) have long been considered a panacea against major chronic diseases, including cancer. However, there is no convincing epidemiological, clinical or experimental evidence supporting FV chemopreventive ability. A daily mono-supplementation of lyophilized onion, tomato, peach, black grape or lettuce was compared with the daily combined administration of the same FV (5 a day-like diet). Ten days post-treatment, the phase-I/II xenobiotic metabolizing and antioxidant enzyme activities, protein and mRNA levels were investigated. As a marker of oxidative stress, the level of hydroperoxides was measured in rat serum samples. Here we show that a blend of FV orally administered to rats not only potentially manipulates metabolism but also disrupts systemic oxidative homeostasis. A daily combination of the five servings remarkably down-regulates the catalytic activity, protein and mRNA levels of a cohort of hepatic metabolizing enzymes, suggesting a possible depressed clearance upon exposure to ubiquitous carcinogens. Strikingly, we observed an impairment of antioxidant enzymes with a boost in systemic hydroperoxide levels. Our study identifies new potential factors of cancer risk connected with the persistent consumption of fixed servings of FV, suggesting that dietary guidance should rely on a "daily diversification" of FV. Copyright © 2016 Elsevier Ltd. All rights reserved.

  11. Prioritization of pesticides based on daily dietary exposure potential as determined from the SHEDS model

    EPA Science Inventory

    A major pathway for exposure to many pesticides is through diet. The objectives were to rank pesticides by comparing their calculated daily dietary exposure as determined by EPA's Stochastic Human Exposure and Dose Simulation (SHEDS) to single pesticides for different age groups ...

  12. A NEW MODEL TO ESTIMATE DAILY ENERGY EXPENDITURE FOR WINTERING WATERFOWL

    EPA Science Inventory

    Activity budgets of wintering waterfowl have been widely used to assess habitat quality. However, when factors such as prey abundance or protection from exposure to cold or wind determine quality, measures of daily energy expenditure (DEE) may be more appropriate for this purpos...

  13. Prioritization of pesticides based on daily dietary exposure potential as determined from the SHEDS model

    EPA Science Inventory

    A major pathway for exposure to many pesticides is through diet. The objectives were to rank pesticides by comparing their calculated daily dietary exposure as determined by EPA's Stochastic Human Exposure and Dose Simulation (SHEDS) to single pesticides for different age groups ...

  14. Daily Life Therapy: A Japanese Model for Educating Children with Autism.

    ERIC Educational Resources Information Center

    Quill, Kathleen; And Others

    1989-01-01

    Five principles of Daily Life Therapy are examined: group-oriented instruction; highly structured routine activities; learning through imitation; reduction of unproductive activity levels through rigorous exercise; and a curriculum based on music, movement, and art. These tenets are discussed in terms of current theory, research, and educational…

  15. Estimating daily surface NO2 concentrations from satellite data - a case study over Hong Kong using land use regression models

    NASA Astrophysics Data System (ADS)

    Anand, Jasdeep S.; Monks, Paul S.

    2017-07-01

    Land use regression (LUR) models have been used in epidemiology to determine the fine-scale spatial variation in air pollutants such as nitrogen dioxide (NO2) in cities and larger regions. However, they are often limited in their temporal resolution, which may potentially be rectified by employing the synoptic coverage provided by satellite measurements. In this work a mixed-effects LUR model is developed to model daily surface NO2 concentrations over the Hong Kong SAR during the period 2005-2015. In situ measurements from the Hong Kong Air Quality Monitoring Network, along with tropospheric vertical column density (VCD) data from the OMI, GOME-2A, and SCIAMACHY satellite instruments were combined with fine-scale land use parameters to provide the spatiotemporal information necessary to predict daily surface concentrations. Cross-validation with the in situ data shows that the mixed-effects LUR model using OMI data has a high predictive power (adj. R2 = 0. 84), especially when compared with surface concentrations derived using the MACC-II reanalysis model dataset (adj. R2 = 0. 11). Time series analysis shows no statistically significant trend in NO2 concentrations during 2005-2015, despite a reported decline in NOx emissions. This study demonstrates the utility in combining satellite data with LUR models to derive daily maps of ambient surface NO2 for use in exposure studies.

  16. Evaluation of SUNY satellite-to-irradiance model performance using ECMWF GEMS daily aerosol optical depth reanalysis data

    NASA Astrophysics Data System (ADS)

    Itterly, Kyle F.

    The current version of the State University of New York (SUNY) radiative transfer model (RTM) uses climatological monthly averages derived from a National Renewable Energy Labs (NREL) gridded dataset to parameterize aerosol optical depth (AOD), water vapor and ozone. This is mostly due to the limited availability of high spatially and temporally resolved observations. Several global chemical transport models are analyzed and compared in depth to determine which daily AOD dataset should be implemented into the SUNY Model. After thorough comparison, the chemical transport model chosen was the Global and regional Earth-system Monitoring using satellite and in-situ data (GEMS) model developed by the European Center for Medium Range Weather Forecasts (ECMWF). Using daily AOD values instead of monthly climatological values, the SUNY Model better captures events of extreme aerosol loadings, which greatly improves the accuracy in calculations of direct normal irradiance (DNI) and to a lesser extent, global horizontal irradiance (GHI). In clear-sky conditions with the sun directly overhead, a change in AOD from 0.1 to 0.5 is found to cause a 55% (20%) decrease in DNI (GHI) for Desert Rock, Nevada in January. A calibration scheme is applied to the daily GEMS AOD reanalysis data. For each site, the monthly means of the GEMS daily AOD are corrected by a factor to match the currently used monthly climatological AOD in order to avoid large errors caused by changing the magnitude of the monthly average AOD. The performance of the SUNY model improved significantly for many of the stations analyzed in this work after applying the daily-calibrated GEMS AOD. The Root Mean Squared Error (RMSE) was the most notable statistical improvement, which measures the model’s precision compared to the observed measurements from a ground station, and many other statistical improvements are also evident. All 7 SURFRAD locations showed improvements in DNI RMSE after using the calibrated GEMS

  17. A model-based approach for the evaluation of once daily dosing of lamivudine in HIV-infected children

    PubMed Central

    Piana, Chiara; Zhao, Wei; Adkison, Kimberly; Burger, David; Jacqz-Aigrain, Evelyne; Danhof, Meindert; Della Pasqua, Oscar

    2014-01-01

    Aim Little attention has been paid to the effects of compliance and prescription practice on treatment outcome in HIV-infected children. In this context, an evaluation of the role of covariates on pharmacokinetics is required to establish the impact of differences in dosing regimens. Here we investigate whether a once daily dosing regimen of lamivudine provides comparable exposure to the currently approved paediatric regimen. Methods A hypothetical group of 180 patients between 3 months and 12 years old was used to evaluate the impact of body weight on systemic exposure to lamivudine. Simulation scenarios were evaluated using AUC and Cmax as parameters of interest. The analysis was performed using a population pharmacokinetic model previously implemented in nonmem v.6.2. Results The simulations show that once daily dosing of lamivudine yields comparable exposure to historical values observed in children and adults, both for liquid and solid dosage forms. Simulated steady-state AUC(0–24 h) and Cmax values after once daily doses ranged respectively from 9.95 mg l−1 h and 1.9 mg l−1 for children lighter than 14 kg to 13.75 mg l−1 h and 3.0 mg l−1 for children heavier than 30 kg. These values are comparable or higher than historical values observed after once daily dosing in children and adults. Conclusions Our findings illustrate how dosing regimens can be evaluated taking into account the effects of developmental growth on drug disposition. Most importantly, they suggest that the reduction in dosing frequency to once daily leads to comparable lamivudine exposure, as observed after administration of a twice daily dosing regimen. PMID:24118047

  18. The impact of the "calendar effect" and pseudo-daily interpolation algorithms on paleoclimatic data-model comparisons

    NASA Astrophysics Data System (ADS)

    Bartlein, P. J.; Shafer, S. L.

    2016-12-01

    Climate-model output, such as that produced by CMIP and PMIP, is generally archived as post-processed monthly time-step data. Meanwhile, paleoclimatic reconstructions, such as those based on terrestrial pollen or plant macrofossil data, are preferably expressed in terms of variables that have some mechanistic influence on vegetation, such as growing degree-days (above a 5°C base, GDD5). The calculation of such variables, with either modern or paleo data, requires daily data, usually "pseudo-daily" values produced by interpolation between monthly means. The "calendar effect" is a common expression for the impact of the known changes in the length of months or seasons over time, related to changes in the eccentricity of Earth's orbit and precession. There are a number of approaches for adjusting monthly data that were averaged using present-day calendar definitions to a "paleo calendar". A simple one involves a) determining the appropriate fixed-angular month lengths for a paleo experiment (e.g., Kutzbach and Gallimore, 1988, JGR 98:803-821), b) interpolating the data to a daily time step, and then c) averaging or accumulating the interpolated daily data using the appropriate paleo month lengths. It turns out that the most common way of producing pseudo-daily values, linear interpolation between monthly means, is not mean preserving; the monthly means of the interpolated daily values will generally not match the original values. Here we use the mean-preserving "harmonic" interpolation method of Epstein (1991, J. Climate 4:365-368) to gauge this mismatch, and compare it with the calendar-effect adjustment (which itself is sensitive to the daily interpolation method). Using the CRU CL 2.0 monthly temperature data set as test case, the differences between linear and harmonic pseudo-daily interpolation range between -1.94 and 0.51°C for January and -0.59 and 1.49 °C for July, while the calendar-effect adjustment appropriate for 6 ka (assuming no change in climate

  19. Evaluation of Downscaled CMIP5 Model Skill in Simulating Daily Maximum Temperature Over the Southeastern United States

    NASA Astrophysics Data System (ADS)

    Keellings, D.

    2015-12-01

    Downscaled CMIP5 climate projections of maximum daily temperature from the Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections archive are examined regionally over the southeastern U.S. Three measures of model skill (means-based, distribution-based, extreme-based) are utilized to assess the ability of 15 downscaled models to simulate daily maximum temperature observations. A new test is proposed to determine statistical significance of the probability density function based skill measures. Skill scores are found to be generally high for all three measures throughout the study region, but lower scores are present in coastal and mountainous areas. Application of the significance test shows that while the skill scores may be high they are not significantly higher than could be expected at random in some areas. The distribution-based skill scores are not significant in much of Florida and the Appalachians. The extreme-based skill scores are not significant in more than 90% of the region for all models investigated. The findings suggest that although the downscaled models have simulated observed means well and are a good match to the entire distribution of observations, they are not simulating the occurrence of extreme (above 90th percentile) maximum daily temperatures.

  20. Daily and 3-hourly Variability in Global Fire Emissions and Consequences for Atmospheric Model Predictions of Carbon Monoxide

    NASA Technical Reports Server (NTRS)

    Mu, M.; Randerson, J. T.; vanderWerf, G. R.; Giglio, L.; Kasibhatla, P.; Morton, D.; Collatz, G. J.; DeFries, R. S.; Hyer, E. J.; Prins, E. M.; Griffith, D. W. T.; Wunch, D.; Toon, G. C.; Sherlock, V.; Wennberg, P. O.

    2011-01-01

    Attribution of the causes of atmospheric trace gas and aerosol variability often requires the use of high resolution time series of anthropogenic and natural emissions inventories. Here we developed an approach for representing synoptic- and diurnal-scale temporal variability in fire emissions for the Global Fire Emissions Database version 3 (GFED3). We disaggregated monthly GFED3 emissions during 2003.2009 to a daily time step using Moderate Resolution Imaging Spectroradiometer (MODIS) ]derived measurements of active fires from Terra and Aqua satellites. In parallel, mean diurnal cycles were constructed from Geostationary Operational Environmental Satellite (GOES) Wildfire Automated Biomass Burning Algorithm (WF_ABBA) active fire observations. Daily variability in fires varied considerably across different biomes, with short but intense periods of daily emissions in boreal ecosystems and lower intensity (but more continuous) periods of burning in savannas. These patterns were consistent with earlier field and modeling work characterizing fire behavior dynamics in different ecosystems. On diurnal timescales, our analysis of the GOES WF_ABBA active fires indicated that fires in savannas, grasslands, and croplands occurred earlier in the day as compared to fires in nearby forests. Comparison with Total Carbon Column Observing Network (TCCON) and Measurements of Pollution in the Troposphere (MOPITT) column CO observations provided evidence that including daily variability in emissions moderately improved atmospheric model simulations, particularly during the fire season and near regions with high levels of biomass burning. The high temporal resolution estimates of fire emissions developed here may ultimately reduce uncertainties related to fire contributions to atmospheric trace gases and aerosols. Important future directions include reconciling top ]down and bottom up estimates of fire radiative power and integrating burned area and active fire time series from

  1. Daily and 3-hourly Variability in Global Fire Emissions and Consequences for Atmospheric Model Predictions of Carbon Monoxide

    NASA Technical Reports Server (NTRS)

    Mu, M.; Randerson, J. T.; vanderWerf, G. R.; Giglio, L.; Kasibhatla, P.; Morton, D.; Collatz, G. J.; DeFries, R. S.; Hyer, E. J.; Prins, E. M.; hide

    2011-01-01

    Attribution of the causes of atmospheric trace gas and aerosol variability often requires the use of high resolution time series of anthropogenic and natural emissions inventories. Here we developed an approach for representing synoptic- and diurnal-scale temporal variability in fire emissions for the Global Fire Emissions Database version 3 (GFED3). We disaggregated monthly GFED3 emissions during 2003.2009 to a daily time step using Moderate Resolution Imaging Spectroradiometer (MODIS) ]derived measurements of active fires from Terra and Aqua satellites. In parallel, mean diurnal cycles were constructed from Geostationary Operational Environmental Satellite (GOES) Wildfire Automated Biomass Burning Algorithm (WF_ABBA) active fire observations. Daily variability in fires varied considerably across different biomes, with short but intense periods of daily emissions in boreal ecosystems and lower intensity (but more continuous) periods of burning in savannas. These patterns were consistent with earlier field and modeling work characterizing fire behavior dynamics in different ecosystems. On diurnal timescales, our analysis of the GOES WF_ABBA active fires indicated that fires in savannas, grasslands, and croplands occurred earlier in the day as compared to fires in nearby forests. Comparison with Total Carbon Column Observing Network (TCCON) and Measurements of Pollution in the Troposphere (MOPITT) column CO observations provided evidence that including daily variability in emissions moderately improved atmospheric model simulations, particularly during the fire season and near regions with high levels of biomass burning. The high temporal resolution estimates of fire emissions developed here may ultimately reduce uncertainties related to fire contributions to atmospheric trace gases and aerosols. Important future directions include reconciling top ]down and bottom up estimates of fire radiative power and integrating burned area and active fire time series from

  2. Modeling of daily rainfall sequence and extremes based on a semiparametric Pareto tail approach at multiple locations

    NASA Astrophysics Data System (ADS)

    So, Byung-Jin; Kwon, Hyun-Han; Kim, Dongkyun; Lee, Seung Oh

    2015-10-01

    A stochastic generation framework for simulation of daily rainfall at multiple sites is presented in this study. The limitations of a Gamma distribution-based Markov chain model for reproducing high-order moments are well-known, and the problems have increased the uncertainties when the models are used in establishing water resource plans. In this regard, this study attempted to develop a semiparametric model based on a piecewise Kernel-Pareto distribution for simulation of daily rainfall in order to further improve the existing model in terms of reproducing extremes, and in addition, the algorithm to reproduce the spatial correlation was combined. The proposed model can essentially be seen as a piecewise distribution approach constructed by parametrically modeling the tails of the distribution using a generalized Pareto and the interior by kernel density estimation methods. As a result, a Kernel-Pareto distribution-based Markov chain model has been shown to perform well at reproducing most statistics, such as mean, standard deviation, skewness and kurtosis. The proposed model provided a significantly improved estimate of design rainfalls for all the stations.

  3. Achieving Consistent Multiple Daily Low-Dose Bacillus anthracis Spore Inhalation Exposures in the Rabbit Model

    PubMed Central

    Barnewall, Roy E.; Comer, Jason E.; Miller, Brian D.; Gutting, Bradford W.; Wolfe, Daniel N.; Director-Myska, Alison E.; Nichols, Tonya L.; Taft, Sarah C.

    2012-01-01

    Repeated low-level exposures to biological agents could occur before or after the remediation of an environmental release. This is especially true for persistent agents such as B. anthracis spores, the causative agent of anthrax. Studies were conducted to examine aerosol methods needed for consistent daily low aerosol concentrations to deliver a low-dose (less than 106 colony forming units (CFU) of B. anthracis spores) and included a pilot feasibility characterization study, acute exposure study, and a multiple 15 day exposure study. This manuscript focuses on the state-of-the-science aerosol methodologies used to generate and aerosolize consistent daily low aerosol concentrations and resultant low inhalation doses to rabbits. The pilot feasibility characterization study determined that the aerosol system was consistent and capable of producing very low aerosol concentrations. In the acute, single day exposure experiment, targeted inhaled doses of 1 × 102, 1 × 103, 1 × 104, and 1 × 105 CFU were used. In the multiple daily exposure experiment, rabbits were exposed multiple days to targeted inhaled doses of 1 × 102, 1 × 103, and 1 × 104 CFU. In all studies, targeted inhaled doses remained consistent from rabbit-to-rabbit and day-to-day. The aerosol system produced aerosolized spores within the optimal mass median aerodynamic diameter particle size range to reach deep lung alveoli. Consistency of the inhaled dose was aided by monitoring and recording respiratory parameters during the exposure with real-time plethysmography. Overall, the presented results show that the animal aerosol system was stable and highly reproducible between different studies and over multiple exposure days. PMID:22919662

  4. UNITED STATES METEOROLOGICAL DATA - DAILY AND HOURLY FILES TO SUPPORT PREDICTIVE EXPOSURE MODELING

    EPA Science Inventory

    ORD numerical models for pesticide exposure include a model of spray drift (AgDisp), a cropland pesticide persistence model (PRZM), a surface water exposure model (EXAMS), and a model of fish bioaccumulation (BASS). A unified climatological database for these models has been asse...

  5. UNITED STATES METEOROLOGICAL DATA - DAILY AND HOURLY FILES TO SUPPORT PREDICTIVE EXPOSURE MODELING

    EPA Science Inventory

    ORD numerical models for pesticide exposure include a model of spray drift (AgDisp), a cropland pesticide persistence model (PRZM), a surface water exposure model (EXAMS), and a model of fish bioaccumulation (BASS). A unified climatological database for these models has been asse...

  6. Markov Chains for Random Urinalysis III: Daily Model and Drug Kinetics

    DTIC Science & Technology

    1994-01-01

    III: Daily M and Drug Kinetcs E L ’-- TF " 94-04546 9 4 2 0 9 0 6 2 Ap ,o.vod f r p c, tease cdsibutn Is un a ted NPRDC-TN-94-12 January 1994 Markov...and maintairi- 9 the d~ata needed. a~d corro’eting a-!d rev~ewing the collection of infrmt~ationi Seid conriments regarding this burden estimate or any...PERFORMiNG ORGANIZATION Navy Personnel Research -and Development Center REPORT NUMBER San Diego, CA 92152-7250 NPRDC-TN-94-12 9 . SPONSO R!NGIMO NTO

  7. A Comparison of Satellite Based, Modeled Derived Daily Solar Radiation Data with Observed Data for the Continental US

    NASA Technical Reports Server (NTRS)

    White, Jeffrey W.; Hoogenboom, Gerrit; Wilkens, Paul W.; Stackhouse, Paul W., Jr.; Hoell, James M.

    2010-01-01

    Many applications of simulation models and related decision support tools for agriculture and natural resource management require daily meteorological data as inputs. Availability and quality of such data, however, often constrain research and decision support activities that require use of these tools. Daily solar radiation (SRAD) data are especially problematic because the instruments require electronic integrators, accurate sensors are expensive, and calibration standards are seldom available. The Prediction Of Worldwide Energy Resources (NASA/POWER; power.larc.nasa.gov) project at the NASA Langley Research Center estimates daily solar radiation based on data that are derived from satellite observations of outgoing visible radiances and atmospheric parameters based upon satellite observations and assimilation models. The solar data are available for a global 1 degree x 1 degree coordinate grid. SRAD can also be estimated based on attenuation of extraterrestrial radiation (Q0) using daily temperature and rainfall data to estimate the optical thickness of the atmosphere. This study compares daily solar radiation data from NASA/POWER (SRADNP) with instrument readings from 295 stations (SRADOB), as well as with values that were estimated with the WGENR solar generator. WGENR was used both with daily temperature and precipitation records from the stations reporting solar data and records from the NOAA Cooperative Observer Program (COOP), thus providing two additional sources of solar data, SRADWG and SRADCO. Values of SRADNP for different grid cells consistently showed higher correlations (typically 0.85 to 0.95) with SRADOB data than did SRADWG or SRADCO for sites within the corresponding cells. Mean values of SRADOB, SRADWG and SRADNP for sites within a grid cell usually were within 1 MJm-2d-1 of each other, but NASA/POWER values averaged 1.1 MJm-2d-1 lower than SRADOB. The magnitude of this bias was greater at lower latitudes and during summer months and may be at

  8. Daily and Hourly Variability in Global Fire Emissions and Consequences for Atmospheric Model Predictions of Carbon Monoxide

    NASA Technical Reports Server (NTRS)

    Mu, M.; Randerson, J. T.; van der Werf, G. R.; Giglio, L.; Kasibhatla, P.; Morton, D.; Collatz, G. J.; DeFries, R. S.; Hyer, E. J.; Prins, E. M.; Griffith, D. W. T.; Wunch, D.; Toon, G. C.; Sherlock, V.; Wennberg, P. O.

    2011-01-01

    Attribution of the causes of atmospheric trace gas and aerosol variability often requires the use of high resolution time series of anthropogenic and natural emissions inventories. Here we developed an approach for representing synoptic- and diurnal-scale temporal variability in fire emissions for the Global Fire Emissions Database version 3 (GFED3). We distributed monthly GFED3 emissions during 2003-2009 on a daily time step using Moderate Resolution Imaging Spectroradiometer (MODIS)-derived measurements of active fires from Terra and Aqua satellites. In parallel, mean diurnal cycles were constructed from Geostationary Operational Environmental Satellite (GOES) active fire observations. We found that patterns of daily variability in fires varied considerably across different biomes, with short but intense periods of daily emissions in boreal ecosystems and lower intensity (but more continuous) periods of bunting in savannas. On diurnal timescales, our analysis of the GOES active fires indicated that fires in savannas, grasslands, and croplands occurred earlier in the day as compared to fires in nearby forests. Comparison with Total Carbon Column Observing Network (TCCON) and Measurements of Pollution in the Troposphere (MOPITT) column CO observations provided evidence that including daily variability in emissions moderately improved atmospheric model simulations, particularly during the fire season and near regions with high levels of biomass burning. The high temporal resolution estimates of fire emissions developed here may ultimately reduce uncertainties related to fire contributions to atmospheric trace gases and aerosols. Important future directions include reconciling top-down and bottom up estimates of fire radiative power and integrating burned area and active fire time series from multiple satellite sensors to improve daily emissions estimates.

  9. Coping with Daily Thermal Variability: Behavioural Performance of an Ectotherm Model in a Warming World

    PubMed Central

    Rojas, José M.; Castillo, Simón B.; Folguera, Guillermo; Abades, Sebastián; Bozinovic, Francisco

    2014-01-01

    Global climate change poses one of the greatest threats to species persistence. Most analyses of the potential biological impacts have focused on changes in mean temperature, but changes in thermal variance will also impact organisms and populations. We assessed the effects of acclimation to daily variance of temperature on dispersal and exploratory behavior in the terrestrial isopod Porcellio laevis in an open field. Acclimation treatments were 24±0, 24±4 and 24±8°C. Because the performance of ectotherms relates nonlinearly to temperature, we predicted that animals acclimated to a higher daily thermal variation should minimize the time exposed in the centre of open field, – i.e. increase the linearity of displacements. Consistent with our prediction, isopods acclimated to a thermally variable environment reduce their exploratory behaviour, hypothetically to minimize their exposure to adverse environmental conditions. This scenario as well as the long latency of animals after releases acclimated to variable environments is consistent with this idea. We suggested that to develop more realistic predictions about the biological impacts of climate change, one must consider the interactions between the mean and variance of environmental temperature on animals' performance. PMID:25207653

  10. Coping with daily thermal variability: behavioural performance of an ectotherm model in a warming world.

    PubMed

    Rojas, José M; Castillo, Simón B; Folguera, Guillermo; Abades, Sebastián; Bozinovic, Francisco

    2014-01-01

    Global climate change poses one of the greatest threats to species persistence. Most analyses of the potential biological impacts have focused on changes in mean temperature, but changes in thermal variance will also impact organisms and populations. We assessed the effects of acclimation to daily variance of temperature on dispersal and exploratory behavior in the terrestrial isopod Porcellio laevis in an open field. Acclimation treatments were 24 ± 0, 24 ± 4 and 24 ± 8 °C. Because the performance of ectotherms relates nonlinearly to temperature, we predicted that animals acclimated to a higher daily thermal variation should minimize the time exposed in the centre of open field, --i.e. increase the linearity of displacements. Consistent with our prediction, isopods acclimated to a thermally variable environment reduce their exploratory behaviour, hypothetically to minimize their exposure to adverse environmental conditions. This scenario as well as the long latency of animals after releases acclimated to variable environments is consistent with this idea. We suggested that to develop more realistic predictions about the biological impacts of climate change, one must consider the interactions between the mean and variance of environmental temperature on animals' performance.

  11. Prioritization of pesticides based on daily dietary exposure potential as determined from the SHEDS model.

    PubMed

    Melnyk, Lisa Jo; Wang, Zhaohui; Li, Zhilin; Xue, Jianping

    2016-10-01

    A major pathway for exposure to many pesticides is through diet. The objectives were to rank pesticides by comparing their calculated daily dietary exposure as determined by EPA's Stochastic Human Exposure and Dose Simulation (SHEDS) to single pesticides for different age groups to acceptable daily intakes (ADI), characterize pesticide trends in exposures over different time periods, and determine commodities contributing to pesticide exposures. SHEDS was applied, using Pesticide Data Program (PDP) (1991-2011) and pesticide usage data on crops from USDA combined with NHANES dietary consumption data, to generate exposure estimates by age group. ADI data collected from EPA, WHO, and other sources were used to rank pesticides based on relativeness of the dietary exposure potential to ADI by age groups. Sensitivity analysis provided trends in pesticide exposures. Within SHEDS, commodities contributing the majority of pesticides with greatest exposure potential were determined. The results indicated that the highest ranking pesticides were methamidophos and diazinon which exceeded 100% of the ADI. Sensitivity analysis indicated that exposure to methamidophos, diazinon, malathion, ethion and formetanate hydrochloride had a marked decrease from 1991-1999 to 2000-2011. Contributions analysis indicated that apples, mushroom, carrots, and lettuce contributed to diazinon exposure. Beans and pepper contributed to methamidophos exposure. Published by Elsevier Ltd.

  12. A Count Model to Study the Correlates of 60 Min of Daily Physical Activity in Portuguese Children

    PubMed Central

    Borges, Alessandra; Gomes, Thayse Natacha; Santos, Daniel; Pereira, Sara; dos Santos, Fernanda K.; Chaves, Raquel; Katzmarzyk, Peter T.; Maia, José

    2015-01-01

    This study aimed to present data on Portuguese children (aged 9–11 years) complying with moderate-to-vigorous physical activity (MVPA) guidelines, and to identify the importance of correlates from multiple domains associated with meeting the guidelines. Physical activity (PA) was objectively assessed by accelerometry throughout seven days on 777 children. A count model using Poisson regression was used to identify the best set of correlates that predicts the variability in meeting the guidelines. Only 3.1% of children met the recommended daily 60 min of MVPA for all seven days of the week. Further, the Cochrane–Armitage chi-square test indicated a linear and negative trend (p < 0.001) from none to all seven days of children complying with the guidelines. The count model explained 22% of the variance in meeting MVPA guidelines daily. Being a girl, having a higher BMI, belonging to families with higher income, sleeping more and taking greater time walking from home to a sporting venue significantly reduced the probability of meeting daily recommended MVPA across the seven days. Furthermore, compared to girls, increasing sleep time in boys increased their chances of compliance with the MVPA recommendations. These results reinforce the relevance of considering different covariates’ roles on PA compliance when designing efficient intervention strategies to promote healthy and active lifestyles in children. PMID:25730296

  13. The application of a Poisson model to the annual distribution of daily mortality at six Montreal hospitals.

    PubMed Central

    Zweig, J P; Csank, J Z

    1978-01-01

    The daily distributions of annual mortality for varying numbers of years between 1965 and 1975 were investigated in three geriatric hospitals and three general hospitals in the Montreal area. Nearly all the observed mortality distributions were found to mimic the classical Poisson distribution, with little departure. In two of the larger hospitals, the matching of the daily mortality distributions with their Poisson models met stringent statistical criteria. In one of them it was even possible to predict the expected mortality frequencies merely from a knowledge of the annual totals. The remaining four hospitals, which included the three geriatric institutions, also exhibited mortalities regarded as highly suggestive of Poisson distributions, although in one of the geriatric hospitals the mortality distribution tended to be somewhat erratic in this respect. PMID:711981

  14. A dataset of future daily weather data for crop modelling over Europe derived from climate change scenarios

    NASA Astrophysics Data System (ADS)

    Duveiller, G.; Donatelli, M.; Fumagalli, D.; Zucchini, A.; Nelson, R.; Baruth, B.

    2017-02-01

    Coupled atmosphere-ocean general circulation models (GCMs) simulate different realizations of possible future climates at global scale under contrasting scenarios of land-use and greenhouse gas emissions. Such data require several additional processing steps before it can be used to drive impact models. Spatial downscaling, typically by regional climate models (RCM), and bias-correction are two such steps that have already been addressed for Europe. Yet, the errors in resulting daily meteorological variables may be too large for specific model applications. Crop simulation models are particularly sensitive to these inconsistencies and thus require further processing of GCM-RCM outputs. Moreover, crop models are often run in a stochastic manner by using various plausible weather time series (often generated using stochastic weather generators) to represent climate time scale for a period of interest (e.g. 2000 ± 15 years), while GCM simulations typically provide a single time series for a given emission scenario. To inform agricultural policy-making, data on near- and medium-term decadal time scale is mostly requested, e.g. 2020 or 2030. Taking a sample of multiple years from these unique time series to represent time horizons in the near future is particularly problematic because selecting overlapping years may lead to spurious trends, creating artefacts in the results of the impact model simulations. This paper presents a database of consolidated and coherent future daily weather data for Europe that addresses these problems. Input data consist of daily temperature and precipitation from three dynamically downscaled and bias-corrected regional climate simulations of the IPCC A1B emission scenario created within the ENSEMBLES project. Solar radiation is estimated from temperature based on an auto-calibration procedure. Wind speed and relative air humidity are collected from historical series. From these variables, reference evapotranspiration and vapour pressure

  15. Comparing an annual and daily time-step model for predicting field-scale P loss

    USDA-ARS?s Scientific Manuscript database

    Several models with varying degrees of complexity are available for describing P movement through the landscape. The complexity of these models is dependent on the amount of data required by the model, the number of model parameters needed to be estimated, the theoretical rigor of the governing equa...

  16. Development of temporally refined land-use regression models predicting daily household-level air pollution in a panel study of lung function among asthmatic children.

    PubMed

    Johnson, Markey; Macneill, Morgan; Grgicak-Mannion, Alice; Nethery, Elizabeth; Xu, Xiaohong; Dales, Robert; Rasmussen, Pat; Wheeler, Amanda

    2013-01-01

    Regulatory monitoring data and land-use regression (LUR) models have been widely used for estimating individual exposure to ambient air pollution in epidemiologic studies. However, LUR models lack fine-scale temporal resolution for predicting acute exposure and regulatory monitoring provides daily concentrations, but fails to capture spatial variability within urban areas. This study coupled LUR models with continuous regulatory monitoring to predict daily ambient nitrogen dioxide (NO(2)) and particulate matter (PM(2.5)) at 50 homes in Windsor, Ontario. We compared predicted versus measured daily outdoor concentrations for 5 days in winter and 5 days in summer at each home. We also examined the implications of using modeled versus measured daily pollutant concentrations to predict daily lung function among asthmatic children living in those homes. Mixed effect analysis suggested that temporally refined LUR models explained a greater proportion of the spatial and temporal variance in daily household-level outdoor NO(2) measurements compared with daily concentrations based on regulatory monitoring. Temporally refined LUR models captured 40% (summer) and 10% (winter) more of the spatial variance compared with regulatory monitoring data. Ambient PM(2.5) showed little spatial variation; therefore, daily PM(2.5) models were similar to regulatory monitoring data in the proportion of variance explained. Furthermore, effect estimates for forced expiratory volume in 1 s (FEV(1)) and peak expiratory flow (PEF) based on modeled pollutant concentrations were consistent with effects based on household-level measurements for NO(2) and PM(2.5). These results suggest that LUR modeling can be combined with continuous regulatory monitoring data to predict daily household-level exposure to ambient air pollution. Temporally refined LUR models provided a modest improvement in estimating daily household-level NO(2) compared with regulatory monitoring data alone, suggesting that this

  17. Improving daily streamflow forecasts in mountainous Upper Euphrates basin by multi-layer perceptron model with satellite snow products

    NASA Astrophysics Data System (ADS)

    Uysal, Gökçen; Şensoy, Aynur; Şorman, A. Arda

    2016-12-01

    This paper investigates the contribution of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite Snow Cover Area (SCA) product and in-situ snow depth measurements to Artificial Neural Network model (ANN) based daily streamflow forecasting in a mountainous river basin. In order to represent non-linear structure of the snowmelt process, Multi-Layer Perceptron (MLP) Feed-Forward Backpropagation (FFBP) architecture is developed and applied in Upper Euphrates River Basin (10,275 km2) of Turkey where snowmelt constitutes approximately 2/3 of total annual volume of runoff during spring and early summer months. Snowmelt season is evaluated between March and July; 7 years (2002-2008) seasonal daily data are used during training while 3 years (2009-2011) seasonal daily data are split for forecasting. One of the fastest ANN training algorithms, the Levenberg-Marquardt, is used for optimization of the network weights and biases. The consistency of the network is checked with four performance criteria: coefficient of determination (R2), Nash-Sutcliffe model efficiency (ME), root mean square error (RMSE) and mean absolute error (MAE). According to the results, SCA observations provide useful information for developing of a neural network model to predict snowmelt runoff, whereas snow depth data alone are not sufficient. The highest performance is experienced when total daily precipitation, average air temperature data are combined with satellite snow cover data. The data preprocessing technique of Discrete Wavelet Analysis (DWA) is coupled with MLP modeling to further improve the runoff peak estimates. As a result, Nash-Sutcliffe model efficiency is increased from 0.52 to 0.81 for training and from 0.51 to 0.75 for forecasting. Moreover, the results are compared with that of a conceptual model, Snowmelt Runoff Model (SRM), application using SCA as an input. The importance and the main contribution of this study is to use of satellite snow products and data

  18. Estimating daily air temperature across the Southeastern United States using high-resolution satellite data: A statistical modeling study.

    PubMed

    Shi, Liuhua; Liu, Pengfei; Kloog, Itai; Lee, Mihye; Kosheleva, Anna; Schwartz, Joel

    2016-04-01

    Accurate estimates of spatio-temporal resolved near-surface air temperature (Ta) are crucial for environmental epidemiological studies. However, values of Ta are conventionally obtained from weather stations, which have limited spatial coverage. Satellite surface temperature (Ts) measurements offer the possibility of local exposure estimates across large domains. The Southeastern United States has different climatic conditions, more small water bodies and wetlands, and greater humidity in contrast to other regions, which add to the challenge of modeling air temperature. In this study, we incorporated satellite Ts to estimate high resolution (1km×1km) daily Ta across the southeastern USA for 2000-2014. We calibrated Ts-Ta measurements using mixed linear models, land use, and separate slopes for each day. A high out-of-sample cross-validated R(2) of 0.952 indicated excellent model performance. When satellite Ts were unavailable, linear regression on nearby monitors and spatio-temporal smoothing was used to estimate Ta. The daily Ta estimations were compared to the NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) model. A good agreement with an R(2) of 0.969 and a mean squared prediction error (RMSPE) of 1.376°C was achieved. Our results demonstrate that Ta can be reliably predicted using this Ts-based prediction model, even in a large geographical area with topography and weather patterns varying considerably. Copyright © 2015 Elsevier Inc. All rights reserved.

  19. Estimating daily air temperature across the Southeastern United States using high-resolution satellite data: a statistical modeling study

    PubMed Central

    Shi, Liuhua; Liu, Pengfei; Kloog, Itai; Lee, Mihye; Kosheleva, Anna; Schwartz, Joel

    2015-01-01

    Accurate estimates of spatio-temporal resolved near-surface air temperature (Ta) are crucial for environmental epidemiological studies. However, values of Ta are conventionally obtained from weather stations, which have limited spatial coverage. Satellite surface temperature (Ts) measurements offer the possibility of local exposure estimates across large domains. The Southeastern United States has different climatic conditions, more small water bodies and wetlands, and greater humidity in contrast to other regions, which add to the challenge of modeling air temperature. In this study, we incorporated satellite Ts to estimate high resolution (1 km × 1 km) daily Ta across the southeastern USA for 2000-2014. We calibrated Ts to Ta measurements using mixed linear models, land use, and separate slopes for each day. A high out-of-sample cross-validated R2 of 0.952 indicated excellent model performance. When satellite Ts were unavailable, linear regression on nearby monitors and spatio-temporal smoothing was used to estimate Ta. The daily Ta estimations were compared to the NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) model. A good agreement with an R2 of 0.969 and a mean squared prediction error (RMSPE) of 1.376 °C was achieved. Our results demonstrate that Ta can be reliably predicted using this Ts-based prediction model, even in a large geographical area with topography and weather patterns varying considerably. PMID:26717080

  20. A Procedure for Inter-Comparing the Skill of Regional-Scale Air Quality Model Simulations of Daily Maximum 8-Hour Ozone Concentrations

    EPA Science Inventory

    An operational model evaluation procedure is described to quantitatively assess the relative skill among several regionalscale air quality models simulating various percentiles of the cumulative frequency distribution of observed daily maximum 8-h ozone concentrations. Bootstrap ...

  1. Validating a model that predicts daily growth and feed quality of New Zealand dairy pastures.

    PubMed

    Woodward, S J

    2001-09-01

    The Pasture Quality (PQ) model is a simple, mechanistic, dynamical system model that was designed to capture the essential biological processes in grazed grass-clover pasture, and to be optimised to derive improved grazing strategies for New Zealand dairy farms. While the individual processes represented in the model (photosynthesis, tissue growth, flowering, leaf death, decomposition, worms) were based on experimental data, this did not guarantee that the assembled model would accurately predict the behaviour of the system as a whole (i.e., pasture growth and quality). Validation of the whole model was thus a priority, since any strategy derived from the model could impact a farm business in the order of thousands of dollars per annum if adopted. This paper describes the process of defining performance criteria for the model, obtaining suitable data to test the model, and carrying out the validation analysis. The validation process highlighted a number of weaknesses in the model, which will lead to the model being improved. As a result, the model's utility will be enhanced. Furthermore, validation was found to have an unexpected additional benefit, in that despite the model's poor initial performance, support was generated for the model among field scientists involved in the wider project.

  2. Adjustment of carbon fluxes to light conditions regulates the daily turnover of starch in plants: a computational model.

    PubMed

    Pokhilko, Alexandra; Flis, Anna; Sulpice, Ronan; Stitt, Mark; Ebenhöh, Oliver

    2014-03-04

    In the light, photosynthesis provides carbon for metabolism and growth. In the dark, plant growth depends on carbon reserves that were accumulated during previous light periods. Many plants accumulate part of their newly-fixed carbon as starch in their leaves in the day and remobilise it to support metabolism and growth at night. The daily rhythms of starch accumulation and degradation are dynamically adjusted to the changing light conditions such that starch is almost but not totally exhausted at dawn. This requires the allocation of a larger proportion of the newly fixed carbon to starch under low carbon conditions, and the use of information about the carbon status at the end of the light period and the length of the night to pace the rate of starch degradation. This regulation occurs in a circadian clock-dependent manner, through unknown mechanisms. We use mathematical modelling to explore possible diurnal mechanisms regulating the starch level. Our model combines the main reactions of carbon fixation, starch and sucrose synthesis, starch degradation and consumption of carbon by sink tissues. To describe the dynamic adjustment of starch to daily conditions, we introduce diurnal regulators of carbon fluxes, which modulate the activities of the key steps of starch metabolism. The sensing of the diurnal conditions is mediated in our model by the timer α and the "dark sensor"β, which integrate daily information about the light conditions and time of the day through the circadian clock. Our data identify the β subunit of SnRK1 kinase as a good candidate for the role of the dark-accumulated component β of our model. The developed novel approach for understanding starch kinetics through diurnal metabolic and circadian sensors allowed us to explain starch time-courses in plants and predict the kinetics of the proposed diurnal regulators under various genetic and environmental perturbations.

  3. Process modeling of ICU patient flow: effect of daily load leveling of elective surgeries on ICU diversion.

    PubMed

    Kolker, Alexander

    2009-02-01

    Despite the considerable number of publications on ICU patient flow and analysis of its variability, a basic and practically important question remained unanswered: what maximum number of elective surgeries per day should be scheduled (along with the competing demand from emergency surgeries) in order to reduce diversion in an ICU with fixed bed capacity to an acceptable low level, or prevent it at all? The goal of this work was to develop a methodology to answer this question. An ICU patient flow simulation model was developed to establish a quantitative link between the daily load leveling of elective surgeries (elective schedule smoothing) and ICU diversion. It was demonstrated that by scheduling not more than four elective surgeries per day ICU diversion due to 'no ICU beds' would be practically eliminated. However this would require bumping 'extra' daily surgeries to the block time day of another week which could be up to 2 months apart. Because not all patients could wait that long for their elective surgery, another more practical scenario was tested that would also result in a very low ICU diversion: bumping 'extra' daily elective surgeries within less than 2 weeks apart, scheduling not more than five elective surgeries per day, and strict adherence to the ICU admission/ discharge criteria.

  4. Estimating the daily course of Konza Prairie latent heat fluxes using a modified Tergra model

    NASA Technical Reports Server (NTRS)

    Hope, Allen S.

    1992-01-01

    Experimental tests of the Tergra-2 model are based on data collected under moderately wet to wet and very dry soil moisture conditions. Further testing of the model under intermediate soil moisture conditions is required and additional testing under very dry conditions may lead to modifications that make the model more suitable to water-stressed conditions. Combining the Tergra model with a soil evaporation routine should enhance the accuracy of the model and allow it to be employed in situations where vapor fluxes are not almost solely attributable to transpiration.

  5. Documentation of a daily mean stream temperature module—An enhancement to the Precipitation-Runoff Modeling System

    USGS Publications Warehouse

    Sanders, Michael J.; Markstrom, Steven L.; Regan, R. Steven; Atkinson, R. Dwight

    2017-09-15

    A module for simulation of daily mean water temperature in a network of stream segments has been developed as an enhancement to the U.S. Geological Survey Precipitation Runoff Modeling System (PRMS). This new module is based on the U.S. Fish and Wildlife Service Stream Network Temperature model, a mechanistic, one-dimensional heat transport model. The new module is integrated in PRMS. Stream-water temperature simulation is activated by selection of the appropriate input flags in the PRMS Control File and by providing the necessary additional inputs in standard PRMS input files.This report includes a comprehensive discussion of the methods relevant to the stream temperature calculations and detailed instructions for model input preparation.

  6. Seasonal & Daily Amazon Column CO2 & CO Observations from Ground & Space Used to Evaluate Tropical Ecosystem Models

    NASA Astrophysics Data System (ADS)

    Dubey, M. K.; Parker, H. A.; Wennberg, P. O.; Wunch, D.; Jacobson, A. R.; Kawa, S. R.; Keppel-Aleks, G.; Basu, S.; O'Dell, C.; Frankenberg, C.; Michalak, A. M.; Baker, D. F.; Christofferson, B.; Restrepo-Coupe, N.; Saleska, S. R.; De Araujo, A. C.; Miller, J. B.

    2016-12-01

    The Amazon basin stores 150-200 PgC, exchanges 18 PgC with the atmosphere every year and has taken up 0.42-0.65 PgC/y over the past two decades. Despite its global significance, the response of the tropical carbon cycle to climate variability and change is ill constrained as evidenced by the large negative and positive feedbacks in future climate simulations. The complex interplay of radiation, water and ecosystem phenology remains unresolved in current tropical ecosystem models. We use high frequency regional scale TCCON observations of column CO2, CO and CH4 near Manaus, Brazil that began in October 2014 to understand the aforementioned interplay of processes in regulating biosphere-atmosphere exchange. We observe a robust daily column CO2 uptake of about 2 ppm (4 ppm to 0.5 ppm) over 8 hours and evaluate how it changes as we transition to the dry season. Back-trajectory calculations show that the daily CO2 uptake footprint is terrestrial and influenced by the heterogeneity of the Amazon rain forests. The column CO falls from above 120 ppb to below 80 ppb as we transition from the biomass burning to wet seasons. The daily mean column CO2 rises by 3 ppm from October through June. Removal of biomass burning, secular CO2 increase and variations from transport (by Carbon tracker simulations) implies an increase of 2.3 ppm results from tropical biospheric processes (respiration and photosynthesis). This is consistent with ground-based remote sensing and eddy flux observations that indicate that leaf development and demography drives the tropical carbon cycle in regions that are not water limited and is not considered in current models. We compare our observations with output from 7 CO2 inversion transport models with assimilated meteorology and find that while 5 models reproduce the CO2 seasonal cycle all of them under predict the daily drawdown of CO2 by a factor of 3. This indicates that the CO2 flux partitioning between photosynthesis and respiration is incorrect

  7. Modelling sub-daily latent heat fluxes from a small reservoir

    NASA Astrophysics Data System (ADS)

    McGloin, Ryan; McGowan, Hamish; McJannet, David; Burn, Stewart

    2014-11-01

    Accurate methods of latent heat flux quantification are essential for water management and for use in hydrological and meteorological models. Currently the effect of small lakes in most numerical weather prediction modelling systems is either entirely ignored or crudely parameterized. In order to test methods for modelling hourly latent heat flux from small water bodies, this study compares results from several modelling approaches to values measured by the eddy covariance method at an agricultural reservoir in southeast Queensland, Australia. Mass transfer estimates of LE calculated using the theoretical mass transfer model and using the Tanny et al. (2008) and Sacks et al. (1994) bulk transfer coefficients showed the best relationship with measured values under a range of meteorological conditions. The theoretical model showed the strongest correlation with measured values, while the Tanny et al. (2008) and Sacks et al. (1994) models had regression equation slopes with the closest proximity to 1. Latent heat fluxes estimated using the Granger and Hedstrom (2011) evaporation model, that was specifically developed for use at small reservoirs, showed a poor relationship with measured values, particularly in stable atmospheric conditions. The 1-dimensional hydrodynamics model, DYRESM, was used to obtain predictions of hourly latent heat flux without the use of water surface temperature measurements. DYRESM estimates of latent heat flux showed a slightly worse relationship with measured values than those predicted using the traditional mass transfer models (which used measurements of water surface temperature). However, DYRESM performed considerably better than the Granger and Hedstrom (2011) model.

  8. Effects of Model Resolution and Subgrid-Scale Physics on the Simulation of Daily Precipitation in the Continental United States

    SciTech Connect

    Duffy, P B; Iorio, J P; Govindasamy, B; Thompson, S L; Khairoutdinov, M; Randall, D

    2004-07-28

    We analyze simulations of the global climate performed at a range of spatial resolutions to assess the effects of horizontal spatial resolution on the ability to simulate precipitation in the continental United States. The model investigated is the CCM3 general circulation model. We also preliminarily assess the effect of replacing cloud and convective parameterizations in a coarse-resolution (T42) model with an embedded cloud-system resolving model (CSRM). We examine both spatial patterns of seasonal-mean precipitation and daily-timescale temporal variability of precipitation in the continental United States. For DJF and SON, high-resolution simulations produce spatial patterns of seasonal-mean precipitation that agree more closely with observed precipitation patterns than do results from the same model (CCM3) at coarse resolution. However, in JJA and MAM, there is little improvement in spatial patterns of seasonal-mean precipitation with increasing resolution, particularly in the Southeast. This is owed to the dominance of convective (i.e., parameterized) precipitation in these two seasons. We further find that higher-resolution simulations have more realistic daily precipitation statistics. In particular, the well-known tendency at coarse resolution to have too many days with weak precipitation and not enough intense precipitation is partially eliminated in higher-resolution simulations. However, even at the highest resolution examined here (T239), the simulated intensity of the mean and of high-percentile daily precipitation amounts is too low. This is especially true in the Southeast, where the most extreme events occur. A new GCM, in which a cloud-resolving model (CSRM) is embedded in each grid cell and replaces convective and stratiform cloud parameterizations, solves this problem, and actually produces too much precipitation in the form of extreme events. However, in contrast to high-resolution versions of CCM3, this model produces little improvement in

  9. Adaptive step goals and rewards: a longitudinal growth model of daily steps for a smartphone-based walking intervention.

    PubMed

    Korinek, Elizabeth V; Phatak, Sayali S; Martin, Cesar A; Freigoun, Mohammad T; Rivera, Daniel E; Adams, Marc A; Klasnja, Pedja; Buman, Matthew P; Hekler, Eric B

    2017-09-16

    Adaptive interventions are an emerging class of behavioral interventions that allow for individualized tailoring of intervention components over time to a person's evolving needs. The purpose of this study was to evaluate an adaptive step goal + reward intervention, grounded in Social Cognitive Theory delivered via a smartphone application (Just Walk), using a mixed modeling approach. Participants (N = 20) were overweight (mean BMI = 33.8 ± 6.82 kg/m(2)), sedentary adults (90% female) interested in participating in a 14-week walking intervention. All participants received a Fitbit Zip that automatically synced with Just Walk to track daily steps. Step goals and expected points were delivered through the app every morning and were designed using a pseudo-random multisine algorithm that was a function of each participant's median baseline steps. Self-report measures were also collected each morning and evening via daily surveys administered through the app. The linear mixed effects model showed that, on average, participants significantly increased their daily steps by 2650 (t = 8.25, p < 0.01) from baseline to intervention completion. A non-linear model with a quadratic time variable indicated an inflection point for increasing steps near the midpoint of the intervention and this effect was significant (t(2) = -247, t = -5.01, p < 0.001). An adaptive step goal + rewards intervention using a smartphone app appears to be a feasible approach for increasing walking behavior in overweight adults. App satisfaction was high and participants enjoyed receiving variable goals each day. Future mHealth studies should consider the use of adaptive step goals + rewards in conjunction with other intervention components for increasing physical activity.

  10. VT-1161 Dosed Once Daily or Once Weekly Exhibits Potent Efficacy in Treatment of Dermatophytosis in a Guinea Pig Model

    PubMed Central

    Hoekstra, W. J.; Moore, W. R.; Schotzinger, R. J.; Long, L.

    2015-01-01

    Current therapies used to treat dermatophytoses such as onychomycosis are effective but display room for improvement in efficacy, safety, and convenience of dosing. We report here that the investigational agent VT-1161 displays potent in vitro antifungal activity against dermatophytes, with MIC values in the range of ≤0.016 to 0.5 μg/ml. In pharmacokinetic studies supporting testing in a guinea pig model of dermatophytosis, VT-1161 plasma concentrations following single oral doses were dose proportional and persisted at or above the MIC values for at least 48 h, indicating potential in vivo efficacy with once-daily and possibly once-weekly dosing. Subsequently, in a guinea pig dermatophytosis model utilizing Trichophyton mentagrophytes and at oral doses of 5, 10, or 25 mg/kg of body weight once daily or 70 mg/kg once weekly, VT-1161 was statistically superior to untreated controls in fungal burden reduction (P < 0.001) and improvement in clinical scores (P < 0.001). The efficacy profile of VT-1161 was equivalent to those for doses and regimens of itraconazole and terbinafine except that VT-1161 was superior to itraconazole when each drug was dosed once weekly (P < 0.05). VT-1161 was distributed into skin and hair, with plasma and tissue concentrations in all treatment and regimen groups ranging from 0.8 to 40 μg/ml (or μg/g), at or above the MIC against the isolate used in the model (0.5 μg/ml). These data strongly support the clinical development of VT-1161 for the oral treatment of onychomycosis using either once-daily or once-weekly dosing regimens. PMID:25605358

  11. VT-1161 dosed once daily or once weekly exhibits potent efficacy in treatment of dermatophytosis in a guinea pig model.

    PubMed

    Garvey, E P; Hoekstra, W J; Moore, W R; Schotzinger, R J; Long, L; Ghannoum, M A

    2015-04-01

    Current therapies used to treat dermatophytoses such as onychomycosis are effective but display room for improvement in efficacy, safety, and convenience of dosing. We report here that the investigational agent VT-1161 displays potent in vitro antifungal activity against dermatophytes, with MIC values in the range of ≤0.016 to 0.5 μg/ml. In pharmacokinetic studies supporting testing in a guinea pig model of dermatophytosis, VT-1161 plasma concentrations following single oral doses were dose proportional and persisted at or above the MIC values for at least 48 h, indicating potential in vivo efficacy with once-daily and possibly once-weekly dosing. Subsequently, in a guinea pig dermatophytosis model utilizing Trichophyton mentagrophytes and at oral doses of 5, 10, or 25 mg/kg of body weight once daily or 70 mg/kg once weekly, VT-1161 was statistically superior to untreated controls in fungal burden reduction (P < 0.001) and improvement in clinical scores (P < 0.001). The efficacy profile of VT-1161 was equivalent to those for doses and regimens of itraconazole and terbinafine except that VT-1161 was superior to itraconazole when each drug was dosed once weekly (P < 0.05). VT-1161 was distributed into skin and hair, with plasma and tissue concentrations in all treatment and regimen groups ranging from 0.8 to 40 μg/ml (or μg/g), at or above the MIC against the isolate used in the model (0.5 μg/ml). These data strongly support the clinical development of VT-1161 for the oral treatment of onychomycosis using either once-daily or once-weekly dosing regimens. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

  12. Comparison of spatiotemporal prediction models of daily exposure of individuals to ambient nitrogen dioxide and ozone in Montreal, Canada.

    PubMed

    Buteau, Stephane; Hatzopoulou, Marianne; Crouse, Dan L; Smargiassi, Audrey; Burnett, Richard T; Logan, Travis; Cavellin, Laure Deville; Goldberg, Mark S

    2017-07-01

    In previous studies investigating the short-term health effects of ambient air pollution the exposure metric that is often used is the daily average across monitors, thus assuming that all individuals have the same daily exposure. Studies that incorporate space-time exposures of individuals are essential to further our understanding of the short-term health effects of ambient air pollution. As part of a longitudinal cohort study of the acute effects of air pollution that incorporated subject-specific information and medical histories of subjects throughout the follow-up, the purpose of this study was to develop and compare different prediction models using data from fixed-site monitors and other monitoring campaigns to estimate daily, spatially-resolved concentrations of ozone (O3) and nitrogen dioxide (NO2) of participants' residences in Montreal, 1991-2002. We used the following methods to predict spatially-resolved daily concentrations of O3 and NO2 for each geographic region in Montreal (defined by three-character postal code areas): (1) assigning concentrations from the nearest monitor; (2) spatial interpolation using inverse-distance weighting; (3) back-extrapolation from a land-use regression model from a dense monitoring survey, and; (4) a combination of a land-use and Bayesian maximum entropy model. We used a variety of indices of agreement to compare estimates of exposure assigned from the different methods, notably scatterplots of pairwise predictions, distribution of differences and computation of the absolute agreement intraclass correlation (ICC). For each pairwise prediction, we also produced maps of the ICCs by these regions indicating the spatial variability in the degree of agreement. We found some substantial differences in agreement across pairs of methods in daily mean predicted concentrations of O3 and NO2. On a given day and postal code area the difference in the concentration assigned could be as high as 131ppb for O3 and 108ppb for NO2. For

  13. Extreme Daily Temperature and Precipitation in a Weather@home Superensemble for the Western United States: Model Performance and Projections

    NASA Astrophysics Data System (ADS)

    Li, S.; Rupp, D. E.; Mote, P.; Massey, N.; Allen, M. R.

    2015-12-01

    Making credible projections of future changes in extreme events has been challenging because it requires not only running climate models at high resolution to faithfully reproduce impact-relevant extreme events, but also ensemble sizes on the order of 10³ and greater to obtain reliable statistics on the intensity and frequency of extreme events. Due to sparsity of high-resolution data, most studies have used fitted analytical probability distributions to produce statistics for extreme events, which in itself has limitations and uncertainties. Here we present results of a superensemble of simulations generated by weather@home, a citizen science computing platform, where Western United States climate was simulated for the recent past (1985-2014) and future (2030-2059) using a coupled regional/global model (HadRM3P/HadAM3P) at 25-km resolution. The very large number of simulations permits the detection of robust spatial patterns of anthropogenically forced change, amidst the "noise" of natural variability, in extremes in daily temperature and precipitation. We investigate to what extent extreme events change in frequency and intensity, relative to changes in the means. Also, the physical mechanisms underlying such changes are explored. We also compare projected daily extreme temperature and precipitation from weather@home with those from regional/global coupled model parings from the North American Regional Climate Change Assessment Program (NARCCAP), whereby statistics (e.g. 20-year, 50-year, etc., return values) are estimated from fitted extreme value distribution.

  14. Performance of five surface energy balance models for estimatng daily evapotranspiration in high biomass sorghum

    USDA-ARS?s Scientific Manuscript database

    Robust evapotranspiration (ET) models are required to predict water usage in a variety of terrestrial ecosystems under different geographical and agrometeorological conditions. As a result, numerous remote sensing-based ET models have been developed to estimate large-scale ET based on the surface en...

  15. Preserving spatial linear correlations between neighboring stations in simulating daily precipitation using extended Markov models

    NASA Astrophysics Data System (ADS)

    Ababaei, Behnam; Sohrabi, Teymour; Mirzaei, Farhad

    2014-10-01

    Most stochastic weather generators have their focus on precipitation because it is the most important variable affecting environmental processes. One of the methods to reproduce the precipitation occurrence time series is to use a Markov process. But, in addition to the simulation of short-term autocorrelations in one station, it is sometimes important to preserve the spatial linear correlations (SLC) between neighboring stations as well. In this research, an extension of one-site Markov models was proposed to preserve the SLC between neighboring stations. Qazvin station was utilized as the reference station and Takestan (TK), Magsal, Nirougah, and Taleghan stations were used as the target stations. The performances of different models were assessed in relation to the simulation of dry and wet spells and short-term dependencies in precipitation time series. The results revealed that in TK station, a Markov model with a first-order spatial model could be selected as the best model, while in the other stations, a model with the order of two or three could be selected. The selected (i.e., best) models were assessed in relation to preserving the SLC between neighboring stations. The results depicted that these models were very capable in preserving the SLC between the reference station and any of the target stations. But, their performances were weaker when the SLC between the other stations were compared. In order to resolve this issue, spatially correlated random numbers were utilized instead of independent random numbers while generating synthetic time series using the Markov models. Although this method slightly reduced the model performances in relation to dry and wet spells and short-term dependencies, the improvements related to the simulation of the SLC between the other stations were substantial.

  16. Simulation of daily streamflows at gaged and ungaged locations within the Cedar River Basin, Iowa, using a Precipitation-Runoff Modeling System model

    USGS Publications Warehouse

    Christiansen, Daniel E.

    2012-01-01

    The U.S. Geological Survey, in cooperation with the Iowa Department of Natural Resources, conducted a study to examine techniques for estimation of daily streamflows using hydrological models and statistical methods. This report focuses on the use of a hydrologic model, the U.S. Geological Survey's Precipitation-Runoff Modeling System, to estimate daily streamflows at gaged and ungaged locations. The Precipitation-Runoff Modeling System is a modular, physically based, distributed-parameter modeling system developed to evaluate the impacts of various combinations of precipitation, climate, and land use on surface-water runoff and general basin hydrology. The Cedar River Basin was selected to construct a Precipitation-Runoff Modeling System model that simulates the period from January 1, 2000, to December 31, 2010. The calibration period was from January 1, 2000, to December 31, 2004, and the validation periods were from January 1, 2005, to December 31, 2010 and January 1, 2000 to December 31, 2010. A Geographic Information System tool was used to delineate the Cedar River Basin and subbasins for the Precipitation-Runoff Modeling System model and to derive parameters based on the physical geographical features. Calibration of the Precipitation-Runoff Modeling System model was completed using a U.S. Geological Survey calibration software tool. The main objective of the calibration was to match the daily streamflow simulated by the Precipitation-Runoff Modeling System model with streamflow measured at U.S. Geological Survey streamflow gages. The Cedar River Basin daily streamflow model performed with a Nash-Sutcliffe efficiency ranged from 0.82 to 0.33 during the calibration period, and a Nash-Sutcliffe efficiency ranged from 0.77 to -0.04 during the validation period. The Cedar River Basin model is meeting the criteria of greater than 0.50 Nash-Sutcliffe and is a good fit for streamflow conditions for the calibration period at all but one location, Austin, Minnesota

  17. Revision and proposed modification for a total maximum daily load model for Upper Klamath Lake, Oregon

    USGS Publications Warehouse

    Wherry, Susan A.; Wood, Tamara M.; Anderson, Chauncey W.

    2015-01-01

    Using the extended 1991–2010 external phosphorus loading dataset, the lake TMDL model was recalibrated following the same procedures outlined in the Phase 1 review. The version of the model selected for further development incorporated an updated sediment initial condition, a numerical solution method for the chlorophyll a model, changes to light and phosphorus factors limiting algal growth, and a new pH-model regression, which removed Julian day dependence in order to avoid discontinuities in pH at year boundaries. This updated lake TMDL model was recalibrated using the extended dataset in order to compare calibration parameters to those obtained from a calibration with the original 7.5-year dataset. The resulting algal settling velocity calibrated from the extended dataset was more than twice the value calibrated with the original dataset, and, because the calibrated values of algal settling velocity and recycle rate are related (more rapid settling required more rapid recycling), the recycling rate also was larger than that determined with the original dataset. These changes in calibration parameters highlight the uncertainty in critical rates in the Upper Klamath Lake TMDL model and argue for their direct measurement in future data collection to increase confidence in the model predictions.

  18. Bayesian multinomial probit modeling of daily windows of susceptibility for maternal PM2.5 exposure and congenital heart defects.

    PubMed

    Warren, Joshua L; Stingone, Jeanette A; Herring, Amy H; Luben, Thomas J; Fuentes, Montserrat; Aylsworth, Arthur S; Langlois, Peter H; Botto, Lorenzo D; Correa, Adolfo; Olshan, Andrew F

    2016-07-20

    Epidemiologic studies suggest that maternal ambient air pollution exposure during critical periods of pregnancy is associated with adverse effects on fetal development. In this work, we introduce new methodology for identifying critical periods of development during post-conception gestational weeks 2-8 where elevated exposure to particulate matter less than 2.5 µm (PM2.5 ) adversely impacts development of the heart. Past studies have focused on highly aggregated temporal levels of exposure during the pregnancy and have failed to account for anatomical similarities between the considered congenital heart defects. We introduce a multinomial probit model in the Bayesian setting that allows for joint identification of susceptible daily periods during pregnancy for 12 types of congenital heart defects with respect to maternal PM2.5 exposure. We apply the model to a dataset of mothers from the National Birth Defect Prevention Study where daily PM2.5 exposures from post-conception gestational weeks 2-8 are assigned using predictions from the downscaler pollution model. This approach is compared with two aggregated exposure models that define exposure as the average value over post-conception gestational weeks 2-8 and the average over individual weeks, respectively. Results suggest an association between increased PM2.5 exposure on post-conception gestational day 53 with the development of pulmonary valve stenosis and exposures during days 50 and 51 with tetralogy of Fallot. Significant associations are masked when using the aggregated exposure models. Simulation study results suggest that the findings are robust to multiple sources of error. The general form of the model allows for different exposures and health outcomes to be considered in future applications. Copyright © 2016 John Wiley & Sons, Ltd.

  19. Artificial neural network models for prediction of daily fine particulate matter concentrations in Algiers.

    PubMed

    Chellali, M R; Abderrahim, H; Hamou, A; Nebatti, A; Janovec, J

    2016-07-01

    Neural network (NN) models were evaluated for the prediction of suspended particulates with aerodynamic diameter less than 10-μm (PM10) concentrations. The model evaluation work considered the sequential hourly concentration time series of PM10, which were measured at El Hamma station in Algiers. Artificial neural network models were developed using a combination of meteorological and time-scale as input variables. The results were rather satisfactory, with values of the coefficient of correlation (R (2)) for independent test sets ranging between 0.60 and 0.85 and values of the index of agreement (IA) between 0.87 and 0.96. In addition, the root mean square error (RMSE), the mean absolute error (MAE), the normalized mean squared error (NMSE), the absolute relative percentage error (ARPE), the fractional bias (FB), and the fractional variance (FS) were calculated to assess the performance of the model. It was seen that the overall performance of model 3 was better than models 1 and 2.

  20. Performance of five surface energy balance models for estimating daily evapotranspiration in high biomass sorghum

    NASA Astrophysics Data System (ADS)

    Wagle, Pradeep; Bhattarai, Nishan; Gowda, Prasanna H.; Kakani, Vijaya G.

    2017-06-01

    Robust evapotranspiration (ET) models are required to predict water usage in a variety of terrestrial ecosystems under different geographical and agrometeorological conditions. As a result, several remote sensing-based surface energy balance (SEB) models have been developed to estimate ET over large regions. However, comparison of the performance of several SEB models at the same site is limited. In addition, none of the SEB models have been evaluated for their ability to predict ET in rain-fed high biomass sorghum grown for biofuel production. In this paper, we evaluated the performance of five widely used single-source SEB models, namely Surface Energy Balance Algorithm for Land (SEBAL), Mapping ET with Internalized Calibration (METRIC), Surface Energy Balance System (SEBS), Simplified Surface Energy Balance Index (S-SEBI), and operational Simplified Surface Energy Balance (SSEBop), for estimating ET over a high biomass sorghum field during the 2012 and 2013 growing seasons. The predicted ET values were compared against eddy covariance (EC) measured ET (ETEC) for 19 cloud-free Landsat image. In general, S-SEBI, SEBAL, and SEBS performed reasonably well for the study period, while METRIC and SSEBop performed poorly. All SEB models substantially overestimated ET under extremely dry conditions as they underestimated sensible heat (H) and overestimated latent heat (LE) fluxes under dry conditions during the partitioning of available energy. METRIC, SEBAL, and SEBS overestimated LE regardless of wet or dry periods. Consequently, predicted seasonal cumulative ET by METRIC, SEBAL, and SEBS were higher than seasonal cumulative ETEC in both seasons. In contrast, S-SEBI and SSEBop substantially underestimated ET under too wet conditions, and predicted seasonal cumulative ET by S-SEBI and SSEBop were lower than seasonal cumulative ETEC in the relatively wetter 2013 growing season. Our results indicate the necessity of inclusion of soil moisture or plant water stress

  1. Reliability and Stability of VLBI-Derived Sub-Daily EOP Models

    NASA Technical Reports Server (NTRS)

    Artz, Thomas; Boeckmann, Sarah; Jensen, Laura; Nothnagel, Axel; Steigenberger, Peter

    2010-01-01

    Recent investigations have shown significant shortcomings in the model which is proposed by the IERS to account for the variations in the Earth s rotation with periods around one day and less. To overcome this, an empirical model can be estimated more or less directly from the observations of space geodetic techniques. The aim of this paper is to evaluate the quality and reliability of such a model based on VLBI observations. Therefore, the impact of the estimation method and the analysis options as well as the temporal stability are investigated. It turned out that, in order to provide a realistic accuracy measure of the model coefficients, the formal errors should be inflated by a factor of three. This coincides with the noise floor and the repeatability of the model coefficients and it captures almost all of the differences that are caused by different estimation techniques. The impact of analysis options is small but significant when changing troposphere parameterization or including harmonic station position variations.

  2. Daily air quality index forecasting with hybrid models: A case in China.

    PubMed

    Zhu, Suling; Lian, Xiuyuan; Liu, Haixia; Hu, Jianming; Wang, Yuanyuan; Che, Jinxing

    2017-09-19

    Air quality is closely related to quality of life. Air pollution forecasting plays a vital role in air pollution warnings and controlling. However, it is difficult to attain accurate forecasts for air pollution indexes because the original data are non-stationary and chaotic. The existing forecasting methods, such as multiple linear models, autoregressive integrated moving average (ARIMA) and support vector regression (SVR), cannot fully capture the information from series of pollution indexes. Therefore, new effective techniques need to be proposed to forecast air pollution indexes. The main purpose of this research is to develop effective forecasting models for regional air quality indexes (AQI) to address the problems above and enhance forecasting accuracy. Therefore, two hybrid models (EMD-SVR-Hybrid and EMD-IMFs-Hybrid) are proposed to forecast AQI data. The main steps of the EMD-SVR-Hybrid model are as follows: the data preprocessing technique EMD (empirical mode decomposition) is utilized to sift the original AQI data to obtain one group of smoother IMFs (intrinsic mode functions) and a noise series, where the IMFs contain the important information (level, fluctuations and others) from the original AQI series. LS-SVR is applied to forecast the sum of the IMFs, and then, S-ARIMA (seasonal ARIMA) is employed to forecast the residual sequence of LS-SVR. In addition, EMD-IMFs-Hybrid first separately forecasts the IMFs via statistical models and sums the forecasting results of the IMFs as EMD-IMFs. Then, S-ARIMA is employed to forecast the residuals of EMD-IMFs. To certify the proposed hybrid model, AQI data from June 2014 to August 2015 collected from Xingtai in China are utilized as a test case to investigate the empirical research. In terms of some of the forecasting assessment measures, the AQI forecasting results of Xingtai show that the two proposed hybrid models are superior to ARIMA, SVR, GRNN, EMD-GRNN, Wavelet-GRNN and Wavelet-SVR. Therefore, the

  3. Modelling wet and dry spells for daily rainfall data series: an application to irrigation management in North-West Italy

    NASA Astrophysics Data System (ADS)

    Ferraris, Stefano; Agnese, Carmelo; Baiamonte, Giorgio; Cat Berro, Daniele; Mercalli, Luca

    2016-04-01

    Rainfall time variability is relevant for agricultural production. The daily time scale is often used in modelling crop and soil water balance. In this work a novel statistical analysis of wet and dry spells is presented, together with an application in an Italian area characterised by a relevant climate spatial variability, due to the presence of both high mountains (e.g.: Mont Blanc) and of the Mediterranean Sea. Statistical analysis of the sequences of rainy days, wet spells (WS), and that of no-rainy days, dry spells (DS), could be carried out separately (as widely applied in the past) or jointly, by introducing the inter-arrival time (IT), representing the time elapsed between two subsequent rainy days. Investigating on daily rainfall data series recorded in Sicily, Agnese et al. (2014) found that IT statistics can be described by the 3-parameter Lerch distribution; in turn, WS and DS distributions can be easily derived from IT distribution. Alternatively, the knowledge of both WS and DS distributions allow deriving IT distribution; in this case, WScan be described by the well-accepted geometric distribution, whereas the 2-parameter polylogarithm distribution can be used for DS, as recently suggested (Agnese et al., 2012) in place of the previously used 1-parameter logarithmic distribution (Chatfield, 1966). In this work, by using some daily rainfall data series recorded in Alpine and Sub-Alpine Areas, the equivalence between the above-mentioned approaches is showed. Furthermore, some interesting relationships between respective parameters are also illustrated. A simple soil water model is then used, using this rainfall statistical model, in order to evaluate the irrigation efficiency as a consequence of variations in the timing of surface irrigation, following the approach described in the paper of Canone et al. (2015). Agnese C., Baiamonte G., Cammalleri C., Cat Berro D., Ferraris S., Mercalli L. (2012). "Statistical analysis of inter-arrival times of

  4. Daily water and sediment discharges from selected rivers of the eastern United States; a time-series modeling approach

    USGS Publications Warehouse

    Fitzgerald, Michael G.; Karlinger, Michael R.

    1983-01-01

    Time-series models were constructed for analysis of daily runoff and sediment discharge data from selected rivers of the Eastern United States. Logarithmic transformation and first-order differencing of the data sets were necessary to produce second-order, stationary time series and remove seasonal trends. Cyclic models accounted for less than 42 percent of the variance in the water series and 31 percent in the sediment series. Analysis of the apparent oscillations of given frequencies occurring in the data indicates that frequently occurring storms can account for as much as 50 percent of the variation in sediment discharge. Components of the frequency analysis indicate that a linear representation is reasonable for the water-sediment system. Models that incorporate lagged water discharge as input prove superior to univariate techniques in modeling and prediction of sediment discharges. The random component of the models includes errors in measurement and model hypothesis and indicates no serial correlation. An index of sediment production within or between drain-gage basins can be calculated from model parameters.

  5. The impact of stochastic physics on tropical rainfall variability in global climate models on daily to weekly time scales

    NASA Astrophysics Data System (ADS)

    Watson, Peter A. G.; Berner, Judith; Corti, Susanna; Davini, Paolo; von Hardenberg, Jost; Sanchez, Claudio; Weisheimer, Antje; Palmer, Tim N.

    2017-06-01

    Many global atmospheric models have too little precipitation variability in the tropics on daily to weekly time scales and also a poor representation of tropical precipitation extremes associated with intense convection. Stochastic parameterizations have the potential to mitigate this problem by representing unpredictable subgrid variability that is left out of deterministic models. We evaluate the impact on the statistics of tropical rainfall of two stochastic schemes: the stochastically perturbed parameterization tendency scheme (SPPT) and stochastic kinetic energy backscatter scheme (SKEBS), in three climate models: EC-Earth, the Met Office Unified Model, and the Community Atmosphere Model, version 4. The schemes generally improve the statistics of simulated tropical rainfall variability, particularly by increasing the frequency of heavy rainfall events, reducing its persistence and increasing the high-frequency component of its variability. There is a large range in the size of the impact between models, with EC-Earth showing the largest improvements. The improvements are greater than those obtained by increasing horizontal resolution to ˜20 km. Stochastic physics also strongly affects projections of future changes in the frequency of extreme tropical rainfall in EC-Earth. This indicates that small-scale variability that is unresolved and unpredictable in these models has an important role in determining tropical climate variability statistics. Using these schemes, and improved schemes currently under development, is therefore likely to be important for producing good simulations of tropical variability and extremes in the present day and future.

  6. Daily evapotranspiration estimates by scaling instantaneous latent heat flux derived from a two-source model

    USDA-ARS?s Scientific Manuscript database

    Radiometric brightness temperature can be used in energy balance models that estimate sensible and latent heat fluxes of the land surface. However, brightness temperature is usually available only at one time of day when acquired from aircraft, fine-scale satellite platforms, or infrared thermometer...

  7. A population based statistical model for daily geometric variations in the thorax.

    PubMed

    Szeto, Yenny Z; Witte, Marnix G; van Herk, Marcel; Sonke, Jan-Jakob

    2017-04-01

    To develop a population based statistical model of the systematic interfraction geometric variations between the planning CT and first treatment week of lung cancer patients for inclusion as uncertainty term in future probabilistic planning. Deformable image registrations between the planning CT and first week CBCTs of 235 lung cancer patients were used to generate deformation vector fields (DVFs) representing the geometric variations of lung cancer patients. Using a second deformable registration step, the average DVF per patient was mapped to an average patient CT. Subsequently, the dominant modes of systematic geometric variations were extracted using Principal Component Analysis (PCA). For evaluation a leave-one-out cross-validation was performed. The first three PCA components mainly described cranial-caudal, anterior-posterior, and left-right variations, respectively. Fifty and 112 components were needed to describe correspondingly 75% and 90% of the variance. An overall systematic variation of 3.6mm SD was observed and could be described with an accuracy of about 1.0mm with the PCA model. A PCA based model for systematic geometric variations in the thorax was developed, and its accuracy determined. Such a model can serve as a basis for probability based treatment planning in lung cancer patients. Copyright © 2017 Elsevier B.V. All rights reserved.

  8. Estimation of genetic parameters for average daily gain using models with competition effects

    USDA-ARS?s Scientific Manuscript database

    Components of variance for ADG with models including competition effects were estimated from data provided by Pig Improvement Company on 11,235 pigs from 4 selected lines of swine. Fifteen pigs with average age of 71 d were randomly assigned to a pen by line and sex and taken off test after approxi...

  9. Daily Stress and Alcohol Consumption: Modeling Between-Person and Within-Person Ethnic Variation in Coping Behavior*

    PubMed Central

    Aldridge-Gerry, Arianna A.; Roesch, Scott C.; Villodas, Feion; McCabe, Cameron; Leung, Queenie K.; Da Costa, Morgan

    2011-01-01

    Objective: Using a daily diary approach, the current study evaluated the relationship between coping and alcohol consumption using a large, multiethnic sample. The primary goals of this study were to (a) identify coping strategies that are either protective or risk factors for alcohol consumption and (b) model between-ethnic and within-ethnic group variation for these relations. Method: College students (N = 365, 69.0% female) were recruited via flyers, course/club presentations, and university seminars. Participants completed Internet-based daily diaries over the course of 5 days and reported specifically on a target stressful event, how they coped with the stressful event, and the amount of alcohol consumed on a daily level. Results: Use of more avoidance-oriented coping strategies (minimization of stressor, emotional rumination) and social support were significantly associated with more alcohol consumption. Ethnicity, however, did moderate some coping—alcohol associations. Use of religious coping was associated with less alcohol consumption and minimization of the stressor was associated with more alcohol consumption in African Americans; use of social support was associated with more alcohol consumption in Asian Americans; and use of problem-focused coping was associated with less alcohol consumption in Whites. Conclusions: Three maladaptive or risky coping strategies with respect to alcohol consumption were identified using an ecologically valid methodology. However, ethnic-specific variation of these risky (and protective) coping factors was identified. The findings highlight the importance of considering both between-ethnic and within-ethnic group variation with respect to the stress/coping and alcohol consumption. PMID:21138719

  10. Video Modeling for Teaching Daily Living Skills to Children with Autism Spectrum Disorder: A Pilot Study

    ERIC Educational Resources Information Center

    Meister, Christine; Salls, Joyce

    2015-01-01

    This pilot study investigated the efficacy of point-of-view video modeling as an intervention strategy to improve self-help skills in children with autism spectrum disorder (ASD). A single-subject A-B design was implemented with eight school-aged children ages 7.5 years to 13.5 years. Six of the students participated in general education classes…

  11. Video Modeling for Teaching Daily Living Skills to Children with Autism Spectrum Disorder: A Pilot Study

    ERIC Educational Resources Information Center

    Meister, Christine; Salls, Joyce

    2015-01-01

    This pilot study investigated the efficacy of point-of-view video modeling as an intervention strategy to improve self-help skills in children with autism spectrum disorder (ASD). A single-subject A-B design was implemented with eight school-aged children ages 7.5 years to 13.5 years. Six of the students participated in general education classes…

  12. A statistical scheme to forecast the daily lightning threat over southern Africa using the Unified Model

    NASA Astrophysics Data System (ADS)

    Gijben, Morné; Dyson, Liesl L.; Loots, Mattheus T.

    2017-09-01

    Cloud-to-ground lightning data from the Southern Africa Lightning Detection Network and numerical weather prediction model parameters from the Unified Model are used to develop a lightning threat index (LTI) for South Africa. The aim is to predict lightning for austral summer days (September to February) by means of a statistical approach. The austral summer months are divided into spring and summer seasons and analysed separately. Stepwise logistic regression techniques are used to select the most appropriate model parameters to predict lightning. These parameters are then utilized in a rare-event logistic regression analysis to produce equations for the LTI that predicts the probability of the occurrence of lightning. Results show that LTI forecasts have a high sensitivity and specificity for spring and summer. The LTI is less reliable during spring, since it over-forecasts the occurrence of lightning. However, during summer, the LTI forecast is reliable, only slightly over-forecasting lightning activity. The LTI produces sharp forecasts during spring and summer. These results show that the LTI will be useful early in the morning in areas where lightning can be expected during the day.

  13. Daily runoff prediction using the linear and non-linear models.

    PubMed

    Sharifi, Alireza; Dinpashoh, Yagob; Mirabbasi, Rasoul

    2017-08-01

    Runoff prediction, as a nonlinear and complex process, is essential for designing canals, water management and planning, flood control and predicting soil erosion. There are a number of techniques for runoff prediction based on the hydro-meteorological and geomorphological variables. In recent years, several soft computing techniques have been developed to predict runoff. There are some challenging issues in runoff modeling including the selection of appropriate inputs and determination of the optimum length of training and testing data sets. In this study, the gamma test (GT), forward selection and factor analysis were used to determine the best input combination. In addition, GT was applied to determine the optimum length of training and testing data sets. Results showed the input combination based on the GT method with five variables has better performance than other combinations. For modeling, among four techniques: artificial neural networks, local linear regression, an adaptive neural-based fuzzy inference system and support vector machine (SVM), results indicated the performance of the SVM model is better than other techniques for runoff prediction in the Amameh watershed.

  14. Hierarchical Bayesian modeling of heterogeneous variances in average daily weight gain of commercial feedlot cattle.

    PubMed

    Cernicchiaro, N; Renter, D G; Xiang, S; White, B J; Bello, N M

    2013-06-01

    Variability in ADG of feedlot cattle can affect profits, thus making overall returns more unstable. Hence, knowledge of the factors that contribute to heterogeneity of variances in animal performance can help feedlot managers evaluate risks and minimize profit volatility when making managerial and economic decisions in commercial feedlots. The objectives of the present study were to evaluate heteroskedasticity, defined as heterogeneity of variances, in ADG of cohorts of commercial feedlot cattle, and to identify cattle demographic factors at feedlot arrival as potential sources of variance heterogeneity, accounting for cohort- and feedlot-level information in the data structure. An operational dataset compiled from 24,050 cohorts from 25 U. S. commercial feedlots in 2005 and 2006 was used for this study. Inference was based on a hierarchical Bayesian model implemented with Markov chain Monte Carlo, whereby cohorts were modeled at the residual level and feedlot-year clusters were modeled as random effects. Forward model selection based on deviance information criteria was used to screen potentially important explanatory variables for heteroskedasticity at cohort- and feedlot-year levels. The Bayesian modeling framework was preferred as it naturally accommodates the inherently hierarchical structure of feedlot data whereby cohorts are nested within feedlot-year clusters. Evidence for heterogeneity of variance components of ADG was substantial and primarily concentrated at the cohort level. Feedlot-year specific effects were, by far, the greatest contributors to ADG heteroskedasticity among cohorts, with an estimated ∼12-fold change in dispersion between most and least extreme feedlot-year clusters. In addition, identifiable demographic factors associated with greater heterogeneity of cohort-level variance included smaller cohort sizes, fewer days on feed, and greater arrival BW, as well as feedlot arrival during summer months. These results support that

  15. Daily potential evapotranspiration and diurnal climate forcings: influence on the numerical modelling of soil water dynamics and evapotranspiration

    NASA Astrophysics Data System (ADS)

    Liu, Siqing; Graham, Wendy D.; Jacobs, Jennifer M.

    2005-07-01

    A physically based, variably saturated flow model was developed to predict soil water dynamics, evapotranspiration (ET) from the vadose zone, and recharge to (or exfiltration from) the saturated zone using mean daily atmospheric forcings and to identify the value of diurnal climate forcings on those predictions. The vadose zone flow is modelled using the Galerkin finite element technique to solve Richards' equation in one-dimension. The model was able to accurately predict measured soil moisture, water table elevation and actual ET at Paynes Prairie State Preserve in Florida. The forecast Nash-Sutcliffe efficiencies of actual ET, water table and average soil moisture content increased modestly, from 0.605-0.653, 0.888-0.916 to 0.902-0.913, respectively, when the average daily ET forcing was replaced with a diurnal evaporation cycle. Several additional numerical experiments were conducted to evaluate the influence of the evaporation cycle disaggregation approach on modelled ET and soil moisture content for different soil textures, vegetation surfaces, and water table depth. The results show that the enhanced predictive value of the diurnal ET cycle increases with decreasing vegetation, decreasing clay content, and increasing water table depth. Using numerical studies, actual evaporation is shown to be higher for daily average evaporation as compared to the diurnal cycle evaporation for specific ranges of shallow water table depth. For clay soils, this range occurs from approximately 40 to 300 cm below land surface for bare soils and from approximately 40 to well below 300 cm below land surface for vegetated soils. The range for sandy soils is approximately 80-200 cm below land surface for both bare and vegetated soils. Within this range, the maximum difference of the actual to potential evapotranspiration ratio for the clay soil, resulting from using different forcing methods, is 20 and 10% for bare soil and vegetated conditions, respectively. The forcing method

  16. Simulation of daily streamflow for nine river basins in eastern Iowa using the Precipitation-Runoff Modeling System

    USGS Publications Warehouse

    Haj, Adel E.; Christiansen, Daniel E.; Hutchinson, Kasey J.

    2015-10-14

    The accuracy of Precipitation-Runoff Modeling System model streamflow estimates of nine river basins in eastern Iowa as compared to measured values at U.S. Geological Survey streamflow-gaging stations varied. The Precipitation-Runoff Modeling System models of nine river basins in eastern Iowa were satisfactory at estimating daily streamflow at 57 of the 79 calibration sites and 13 of the 14 validation sites based on statistical results. Unsatisfactory performance can be contributed to several factors: (1) low flow, no flow, and flashy flow conditions in headwater subbasins having a small drainage area; (2) poor representation of the groundwater and storage components of flow within a basin; (3) lack of accounting for basin withdrawals and water use; and (4) the availability and accuracy of meteorological input data. The Precipitation- Runoff Modeling System models of nine river basins in eastern Iowa will provide water-resource managers with a consistent and documented method for estimating streamflow at ungaged sites and aid in environmental studies, hydraulic design, water management, and water-quality projects.

  17. An operational model to estimate hourly and daily crop evapotranspiration in hilly terrain: validation on wheat and oat crops

    NASA Astrophysics Data System (ADS)

    Rana, Gianfranco; Katerji, Nader; Ferrara, Rossana M.; Martinelli, Nicola

    2011-03-01

    In this paper, we present an operational model to estimate the actual evapotranspiration (ET) of crops cultivated on hilly terrains. This new model has the following three characteristics: (1) ET modelling is based on a Penman-Monteith (PM) type equation (Monteith 1965) where canopy resistance is simulated by following an approach already illustrated by Katerji and Perrier (Agronomie 3(6):513-521, 1983); (2) the estimation of ET, by means of the PM equation, is made by using meteorological variables simulated on sloped sites as input; (3) these variables are simulated by using simple relationships linking the variables measured at a reference site on plane to the topographic characteristics of the site (slope, orientation, altitude as difference between reference, and sloped sites). This approach presents two advantages if compared with previously proposed models: Not only computation steps are greatly simplified but also error sources due to the simulation of climatic variables in sloped sites and the ET estimation are well distinguished. This model was validated at hourly and daily scales at four sites cultivated with wheat and oats offering a wide range of slope and orientation values: a reference site on plane, site 1 (9° sloping, NW orientation, 7 m from the reference site in plane), site 2 (6°, SE, 12 m) and site 3 (1°, SE, 18 m). At hourly scale, the new model performed well at all sites studied. The observed slope of the linear relationships between estimated and measured ET values ranged between 0.93 and 1.03, with coefficients of determination, r 2, between 0.80 and 0.98. At daily scale, the slopes of the linear relationships between measured and estimated ET for the sites on plane and the sloped sites were practically the same (0.98 ± 0.01); however, the coefficient of determination r 2 observed in the site on plane was clearly greater (0.98) than that observed in the sloped sites (0.83). The presented analysis does not show any significant

  18. Bayesian distributed lag models: estimating effects of particulate matter air pollution on daily mortality.

    PubMed

    Welty, L J; Peng, R D; Zeger, S L; Dominici, F

    2009-03-01

    A distributed lag model (DLagM) is a regression model that includes lagged exposure variables as covariates; its corresponding distributed lag (DL) function describes the relationship between the lag and the coefficient of the lagged exposure variable. DLagMs have recently been used in environmental epidemiology for quantifying the cumulative effects of weather and air pollution on mortality and morbidity. Standard methods for formulating DLagMs include unconstrained, polynomial, and penalized spline DLagMs. These methods may fail to take full advantage of prior information about the shape of the DL function for environmental exposures, or for any other exposure with effects that are believed to smoothly approach zero as lag increases, and are therefore at risk of producing suboptimal estimates. In this article, we propose a Bayesian DLagM (BDLagM) that incorporates prior knowledge about the shape of the DL function and also allows the degree of smoothness of the DL function to be estimated from the data. We apply our BDLagM to its motivating data from the National Morbidity, Mortality, and Air Pollution Study to estimate the short-term health effects of particulate matter air pollution on mortality from 1987 to 2000 for Chicago, Illinois. In a simulation study, we compare our Bayesian approach with alternative methods that use unconstrained, polynomial, and penalized spline DLagMs. We also illustrate the connection between BDLagMs and penalized spline DLagMs. Software for fitting BDLagM models and the data used in this article are available online.

  19. Integration of Satellite Estimates of Daily Inundation Extent into a Land Surface Ecosystem-Atmosphere Gas Exchange Model: Impacts on Methane Modeling

    NASA Astrophysics Data System (ADS)

    Galantowicz, J. F.; Wei, L. H.; Samanta, A.; Picton, J.; Zhang, B.; Lu, C.; Yang, J.; Tian, H.; Eluszkiewicz, J.; Nehrkorn, T.; Mountain, M.

    2013-12-01

    Soil moisture and the spatial extent of soil saturation, transient inundation, and wetland ecosystems are key determinants of greenhouse gas (GHG, e.g., methane) emissions from the land surface to the atmosphere. We are investigating how near-daily surface water and soil moisture observations such as those expected from NASA's planned Soil Moisture Active-Passive (SMAP) mission could be integrated into an ecosystem-atmosphere gas exchange model to improve its estimates of GHG fluxes. SMAP, to be launched in November 2014, will combine ~3-km resolution synthetic aperture radar (SAR), ~40-km-resolution L-band radiometry, and 3-day revisit period to make a novel dataset expected to provide inundation and soil moisture estimates superior to alternative methods at that temporal-spatial scale. We test the potential impact of this new data source using the Dynamic Land Surface Ecosystem Model (DLEM). DLEM quantifies regional fluxes of methane (CH4), carbon dioxide (CO2), and nitrous oxide (N2O) given atmospheric forcing data, with soil saturation as a prognostic variable. In this presentation, we discuss the results of integrating DLEM CH4 emission model products with time-varying subgrid inundation extent estimates from satellite remote sensing observations of North America. To emulate SMAP observations, we have derived a new daily inundation fraction dataset for 2008-2010 using data from NASA's Advanced Microwave Scanning Radiometer-EOS (AMSR-E). To test data-model integration, we created a testbed composed of two separate multi-year DLEM runs in which subgrid land cover conditions were artificially prescribed: one run with maximum wetlands coverage and one with no wetlands. We can combine CH4 products from the two runs using our daily inundation fraction estimates or other inundation representations such that the combination approximates CH4 flux results from a model with explicit inundation forcing. The testbed allows us to simulate a larger array of mixed-grid cases

  20. [Inversion Model and Daily Variation of Total Phosphorus Concentrations in Taihu Lake Based on GOCI Data].

    PubMed

    Du, Cheng-gong; Li, Yun-mei; Wang, Qiao; Zhu, Li; Lü, Heng

    2016-03-15

    The TP concentration is an important index of water quality and an important influencing factor of eutrophication and algae blooms. Remote sensing technology has advantages of wide scope and high time limited efficacy. Monitoring the concentration of TP by satellite remote sensing is important for the study of water quality and eutrophication. In situ datasets collected during the three times of experiments in Taihu Lake between 2013 and 2014 were used to develop the TP inversion model based on GOCI data. The GOCI data in spring, summer, autumn and winter in 2014 were selected to analyze the time and space changes of TP concentration in Taihu Lake. The results showed that the TP algorithm was built up based on the variables, which was to use the eight band combination of GOCI data as variable, and build model using Multi factor linear regression method. The algorithm achieved more accurate TP estimation with R² = 0.898, MAPE = 14.296%, RMSE = 0.026 mg · L⁻¹. Meantime, a analysis on the precision of the model by using the measured sample points and the synchronous satellite images with MAPE = 33.642%, 22.551%, RMSE = 0.076 mg · L⁻¹, 0.028 mg · L⁻¹ on August 5, 2014 and October 24, 2014. Through the analysis of the 30 images on the four days of the four seasons, it showed that the absolute concentration of total phosphorus was different in different seasons. But temporal and spatial distribution of total phosphorus concentration was similar in the morning and afternoon. In spatial distribution, the TP concentration in Meiliang Bay, Zhushan Bay, Gonghu Bay, Xiaomei Port and Changdou Port in the southwest coast was at a continuously high position. The TP concentration change in different regions was influenced by wind direction, wind speed and other factors. The TP concentration highest in the morning, and then gradually decreased, this phenomenon reflected that the TP concentration was affected by temperature and light.

  1. Evaluation of statistical and rainfall-runoff models for predicting historical daily streamflow time series in the Des Moines and Iowa River watersheds

    USGS Publications Warehouse

    Farmer, William H.; Knight, Rodney R.; Eash, David A.; Kasey J. Hutchinson,; Linhart, S. Mike; Christiansen, Daniel E.; Archfield, Stacey A.; Over, Thomas M.; Kiang, Julie E.

    2015-08-24

    Daily records of streamflow are essential to understanding hydrologic systems and managing the interactions between human and natural systems. Many watersheds and locations lack streamgages to provide accurate and reliable records of daily streamflow. In such ungaged watersheds, statistical tools and rainfall-runoff models are used to estimate daily streamflow. Previous work compared 19 different techniques for predicting daily streamflow records in the southeastern United States. Here, five of the better-performing methods are compared in a different hydroclimatic region of the United States, in Iowa. The methods fall into three classes: (1) drainage-area ratio methods, (2) nonlinear spatial interpolations using flow duration curves, and (3) mechanistic rainfall-runoff models. The first two classes are each applied with nearest-neighbor and map-correlated index streamgages. Using a threefold validation and robust rank-based evaluation, the methods are assessed for overall goodness of fit of the hydrograph of daily streamflow, the ability to reproduce a daily, no-fail storage-yield curve, and the ability to reproduce key streamflow statistics. As in the Southeast study, a nonlinear spatial interpolation of daily streamflow using flow duration curves is found to be a method with the best predictive accuracy. Comparisons with previous work in Iowa show that the accuracy of mechanistic models with at-site calibration is substantially degraded in the ungaged framework.

  2. Evaluating the Impacts of NASA/SPoRT Daily Greenness Vegetation Fraction on Land Surface Model and Numerical Weather Forecasts

    NASA Technical Reports Server (NTRS)

    Bell, Jordan R.; Case, Jonathan L.; LaFontaine, Frank J.; Kumar, Sujay V.

    2012-01-01

    The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed a Greenness Vegetation Fraction (GVF) dataset, which is updated daily using swaths of Normalized Difference Vegetation Index data from the Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the NASA EOS Aqua and Terra satellites. NASA SPoRT began generating daily real-time GVF composites at 1-km resolution over the Continental United States (CONUS) on 1 June 2010. The purpose of this study is to compare the National Centers for Environmental Prediction (NCEP) climatology GVF product (currently used in operational weather models) to the SPoRT-MODIS GVF during June to October 2010. The NASA Land Information System (LIS) was employed to study the impacts of the SPoRT-MODIS GVF dataset on a land surface model (LSM) apart from a full numerical weather prediction (NWP) model. For the 2010 warm season, the SPoRT GVF in the western portion of the CONUS was generally higher than the NCEP climatology. The eastern CONUS GVF had variations both above and below the climatology during the period of study. These variations in GVF led to direct impacts on the rates of heating and evaporation from the land surface. In the West, higher latent heat fluxes prevailed, which enhanced the rates of evapotranspiration and soil moisture depletion in the LSM. By late Summer and Autumn, both the average sensible and latent heat fluxes increased in the West as a result of the more rapid soil drying and higher coverage of GVF. The impacts of the SPoRT GVF dataset on NWP was also examined for a single severe weather case study using the Weather Research and Forecasting (WRF) model. Two separate coupled LIS/WRF model simulations were made for the 17 July 2010 severe weather event in the Upper Midwest using the NCEP and SPoRT GVFs, with all other model parameters remaining the same. Based on the sensitivity results, regions with higher GVF in the SPoRT model runs had higher evapotranspiration and

  3. A model for the distribution of daily number of births in obstetric clinics based on a descriptive retrospective study

    PubMed Central

    Gam, Christiane M B; Tanniou, Julien; Keiding, Niels; Løkkegaard, Ellen L

    2013-01-01

    Objective To test whether the relatively unpredictable nature of labour onset can be described by the Poisson distribution. Design A descriptive retrospective study. Setting From the Danish Birth Registry, we identified births from all seven obstetric clinics in the capital region of Denmark (n=211 290) between 2000 and the end of 2009. On each date, the number of births at each department was registered. Births are categorised based on whether an elective caesarean section or induction of labour has been performed, and among the remaining ‘non-elective births’, acute caesareans were registered. Methods After the exclusion of elective caesarean sections and births after induction of labour, only ‘non-elective’ births (n=171 009) were included for the main statistical analysis. Simple descriptive plots and one-way analysis of variance were used to analyse the distribution of ‘non-elective’ births for each day of the week. Main outcome measures The daily number of ‘non-elective’ births. Results The number of ‘non-elective’ births varies considerably over the days of the week and over the year for each obstetric clinic regardless of clinic size. However, for each fixed day of the week, the variation over the year is well described by a Poisson distribution, allowing simple prediction of the variability. For births at each fixed day of the week, the Poisson distribution is indistinguishable from a normal distribution. Conclusions The number of ‘non-elective’ births for each day of the week is well described by a Poisson distribution. Consequently, the Poisson model is suitable for estimating the variation in the daily number of ‘non-elective’ births and could be used for planning of staffing in obstetric clinics. The model can be used in smaller as well as larger clinics. PMID:23996815

  4. West African monsoon intraseasonal activity and its daily precipitation indices in regional climate models: diagnostics and challenges

    NASA Astrophysics Data System (ADS)

    Poan, E. D.; Gachon, P.; Dueymes, G.; Diaconescu, E.; Laprise, R.; Seidou Sanda, I.

    2016-11-01

    The West African monsoon intraseasonal variability has huge socio-economic impacts on local populations but understanding and predicting it still remains a challenge for the weather prediction and climate scientific community. This paper analyses an ensemble of simulations from six regional climate models (RCMs) taking part in the coordinated regional downscaling experiment, the ECMWF ERA-Interim reanalysis (ERAI) and three satellite-based and observationally-constrained daily precipitation datasets, to assess the performance of the RCMs with regard to the intraseasonal variability. A joint analysis of seasonal-mean precipitation and the total column water vapor (also called precipitable water— PW) suggests the existence of important links at different timescales between these two variables over the Sahel and highlights the relevance of using PW to follow the monsoon seasonal cycle. RCMs that fail to represent the seasonal-mean position and amplitude of the meridional gradient of PW show the largest discrepancies with respect to seasonal-mean observed precipitation. For both ERAI and RCMs, spectral decompositions of daily PW as well as rainfall show an overestimation of low-frequency activity (at timescales longer than 10 days) at the expense of the synoptic (timescales shorter than 10 days) activity. Consequently, the effects of the African Easterly Waves and the associated mesoscale convective systems are substantially underestimated, especially over continental regions. Finally, the study investigates the skill of the models with respect to hydro-climatic indices related to the occurrence, intensity and frequency of precipitation events at the intraseasonal scale. Although most of these indices are generally better reproduced with RCMs than reanalysis products, this study indicates that RCMs still need to be improved (especially with respect to their subgrid-scale parameterization schemes) to be able to reproduce the intraseasonal variance spectrum adequately.

  5. A hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags

    PubMed Central

    Hocking, Daniel J.; O’Neil, Kyle; Whiteley, Andrew R.; Nislow, Keith H.; O’Donnell, Matthew J.

    2016-01-01

    Water temperature is a primary driver of stream ecosystems and commonly forms the basis of stream classifications. Robust models of stream temperature are critical as the climate changes, but estimating daily stream temperature poses several important challenges. We developed a statistical model that accounts for many challenges that can make stream temperature estimation difficult. Our model identifies the yearly period when air and water temperature are synchronized, accommodates hysteresis, incorporates time lags, deals with missing data and autocorrelation and can include external drivers. In a small stream network, the model performed well (RMSE = 0.59°C), identified a clear warming trend (0.63 °C decade−1) and a widening of the synchronized period (29 d decade−1). We also carefully evaluated how missing data influenced predictions. Missing data within a year had a small effect on performance (∼0.05% average drop in RMSE with 10% fewer days with data). Missing all data for a year decreased performance (∼0.6 °C jump in RMSE), but this decrease was moderated when data were available from other streams in the network. PMID:26966662

  6. A hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags

    USGS Publications Warehouse

    Letcher, Benjamin; Hocking, Daniel; O'Neil, Kyle; Whiteley, Andrew R.; Nislow, Keith H.; O'Donnell, Matthew

    2016-01-01

    Water temperature is a primary driver of stream ecosystems and commonly forms the basis of stream classifications. Robust models of stream temperature are critical as the climate changes, but estimating daily stream temperature poses several important challenges. We developed a statistical model that accounts for many challenges that can make stream temperature estimation difficult. Our model identifies the yearly period when air and water temperature are synchronized, accommodates hysteresis, incorporates time lags, deals with missing data and autocorrelation and can include external drivers. In a small stream network, the model performed well (RMSE = 0.59°C), identified a clear warming trend (0.63 °C decade−1) and a widening of the synchronized period (29 d decade−1). We also carefully evaluated how missing data influenced predictions. Missing data within a year had a small effect on performance (∼0.05% average drop in RMSE with 10% fewer days with data). Missing all data for a year decreased performance (∼0.6 °C jump in RMSE), but this decrease was moderated when data were available from other streams in the network.

  7. Identification of the best architecture of a multilayer perceptron in modeling daily total ozone concentration over Kolkata, India

    NASA Astrophysics Data System (ADS)

    De, Syam; De, Barin; Chattopadhyay, Goutami; Paul, Suman; Haldar, Dilip; Chakrabarty, Dipak

    2011-04-01

    Autoregressive neural network (AR-NN) models of various orders have been generated in this work for the daily total ozone (TO) time series over Kolkata (22.56°N, 88.5°E). Artificial neural network in the form of multilayer perceptron (MLP) is implemented in order to generate the AR-NN models of orders varying from 1 to 13. An extensive variable selection method through multiple linear regression (MLR) is implemented while developing the AR-NNs. The MLPs are characterized by sigmoid non-linearity. The optimum size of the hidden layer is identified in each model and prediction are produced by validating it over the test cases using the coefficient of determination (R 2) and Willmott's index (WI). It is observed that AR-NN model of order 7 having 6 nodes in the hidden layer has maximum prediction capacity. It is further observed that any increase in the orders of AR-NN leads to less accurate prediction.

  8. Changes in sub-daily precipitation extremes in a global climate model with super-parameterization under CO2 warming

    NASA Astrophysics Data System (ADS)

    Khairoutdinov, Marat; Zhou, Xin

    2015-04-01

    Virtually all of the projections for future change of extreme precipitation statistics under CO2 warming have been made using global climate models (GCMs) in which clouds and, in particular, convective cloud systems are not explicitly resolved, but rather parameterized. In our study, a different kind of a GCM, a super-parameterized Community Atmosphere Model (SP-CAM), is employed. In SP-CAM, all the conventional cloud parameterizations are replaced with a small-domain cloud resolving model (CRM), called super-parameterization (SP). The SP is embedded in each grid column of the host GCM. The resolution of each embedded CRM is 4 km, which is generally sufficient to explicitly represent deep convection, which is mostly responsible for extreme precipitation events. In this study, we use the SP-CAM to contrast to the present and to conventional climate model, CAM, the sub-daily extreme precipitation statistics in response to the sea-surface temperatures (SSTs) and CO2 levels as projected for the end of 21st century in response to the IPCC AR5 RCP8.5 emission scenario. Different mechanisms for extreme precipitation changes are discussed.

  9. A hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags.

    PubMed

    Letcher, Benjamin H; Hocking, Daniel J; O'Neil, Kyle; Whiteley, Andrew R; Nislow, Keith H; O'Donnell, Matthew J

    2016-01-01

    Water temperature is a primary driver of stream ecosystems and commonly forms the basis of stream classifications. Robust models of stream temperature are critical as the climate changes, but estimating daily stream temperature poses several important challenges. We developed a statistical model that accounts for many challenges that can make stream temperature estimation difficult. Our model identifies the yearly period when air and water temperature are synchronized, accommodates hysteresis, incorporates time lags, deals with missing data and autocorrelation and can include external drivers. In a small stream network, the model performed well (RMSE = 0.59°C), identified a clear warming trend (0.63 °C decade(-1)) and a widening of the synchronized period (29 d decade(-1)). We also carefully evaluated how missing data influenced predictions. Missing data within a year had a small effect on performance (∼0.05% average drop in RMSE with 10% fewer days with data). Missing all data for a year decreased performance (∼0.6 °C jump in RMSE), but this decrease was moderated when data were available from other streams in the network.

  10. The perfectionism model of binge eating: testing unique contributions, mediating mechanisms, and cross-cultural similarities using a daily diary methodology.

    PubMed

    Sherry, Simon B; Sabourin, Brigitte C; Hall, Peter A; Hewitt, Paul L; Flett, Gordon L; Gralnick, Tara M

    2014-12-01

    The perfectionism model of binge eating (PMOBE) is an integrative model explaining the link between perfectionism and binge eating. This model proposes socially prescribed perfectionism confers risk for binge eating by generating exposure to 4 putative binge triggers: interpersonal discrepancies, low interpersonal esteem, depressive affect, and dietary restraint. The present study addresses important gaps in knowledge by testing if these 4 binge triggers uniquely predict changes in binge eating on a daily basis and if daily variations in each binge trigger mediate the link between socially prescribed perfectionism and daily binge eating. Analyses also tested if proposed mediational models generalized across Asian and European Canadians. The PMOBE was tested in 566 undergraduate women using a 7-day daily diary methodology. Depressive affect predicted binge eating, whereas anxious affect did not. Each binge trigger uniquely contributed to binge eating on a daily basis. All binge triggers except for dietary restraint mediated the relationship between socially prescribed perfectionism and change in daily binge eating. Results suggested cross-cultural similarities, with the PMOBE applying to both Asian and European Canadian women. The present study advances understanding of the personality traits and the contextual conditions accompanying binge eating and provides an important step toward improving treatments for people suffering from eating binges and associated negative consequences.

  11. Validating the simulation of optical reflectance by a vertically resolved canopy biophysics model with MODIS daily observations

    NASA Astrophysics Data System (ADS)

    Drewry, D. T.; Duveiller, G.

    2012-12-01

    , hydrological and energetic (ie. biochemical, water stress, energy balance) states and fluxes and remotely sensed reflectances across the visible and near infrared bands observed by MODIS. Special care is made to ensure that the satellite observations match adequately, in both time and space, with the coupled model simulations. To do so, daily MODIS observations are used for which the exact time when they have been calculated is known by the satellite orbits. A previously-developed model of the spatial response of the sensor is also used to ensure that each footprint of every observation is within the target fields. This latter step is necessary for using daily MODIS data, as the observation footprint is often much larger than the 250 meter pixel it is encoded in, with further variation according to the orbit. This study focuses on the agro-ecosystem of the Central US, characterized by large-scale annual plantations of soybean and maize, for which MLCan has been rigorously validated.

  12. Using a spatio-temporal dynamic state-space model with the EM algorithm to patch gaps in daily riverflow series

    NASA Astrophysics Data System (ADS)

    Amisigo, B. A.; van de Giesen, N. C.

    2005-09-01

    A spatio-temporal linear dynamic model has been developed for patching short gaps in daily river runoff series. The model was cast in a state-space form in which the state variable was estimated using the Kalman smoother (RTS smoother). The EM algorithm was used to concurrently estimate both parameter and missing runoff values. Application of the model to daily runoff series in the Volta Basin of West Africa showed that the model was capable of providing good estimates of missing runoff values at a gauging station from the remaining time series at the station and at spatially correlated stations in the same sub-basin.

  13. Comparison of Two Stochastic Daily Rainfall Models and their Ability to Preserve Multi-year Rainfall Variability

    NASA Astrophysics Data System (ADS)

    Kamal Chowdhury, AFM; Lockart, Natalie; Willgoose, Garry; Kuczera, George; Kiem, Anthony; Parana Manage, Nadeeka

    2016-04-01

    Stochastic simulation of rainfall is often required in the simulation of streamflow and reservoir levels for water security assessment. As reservoir water levels generally vary on monthly to multi-year timescales, it is important that these rainfall series accurately simulate the multi-year variability. However, the underestimation of multi-year variability is a well-known issue in daily rainfall simulation. Focusing on this issue, we developed a hierarchical Markov Chain (MC) model in a traditional two-part MC-Gamma Distribution modelling structure, but with a new parameterization technique. We used two parameters of first-order MC process (transition probabilities of wet-to-wet and dry-to-dry days) to simulate the wet and dry days, and two parameters of Gamma distribution (mean and standard deviation of wet day rainfall) to simulate wet day rainfall depths. We found that use of deterministic Gamma parameter values results in underestimation of multi-year variability of rainfall depths. Therefore, we calculated the Gamma parameters for each month of each year from the observed data. Then, for each month, we fitted a multi-variate normal distribution to the calculated Gamma parameter values. In the model, we stochastically sampled these two Gamma parameters from the multi-variate normal distribution for each month of each year and used them to generate rainfall depth in wet days using the Gamma distribution. In another study, Mehrotra and Sharma (2007) proposed a semi-parametric Markov model. They also used a first-order MC process for rainfall occurrence simulation. But, the MC parameters were modified by using an additional factor to incorporate the multi-year variability. Generally, the additional factor is analytically derived from the rainfall over a pre-specified past periods (e.g. last 30, 180, or 360 days). They used a non-parametric kernel density process to simulate the wet day rainfall depths. In this study, we have compared the performance of our

  14. Modelling a model?!! Prediction of observed and calculated daily pan evaporation in New Mexico, U.S.A.

    NASA Astrophysics Data System (ADS)

    Beriro, D. J.; Abrahart, R. J.; Nathanail, C. P.

    2012-04-01

    Data-driven modelling is most commonly used to develop predictive models that will simulate natural processes. This paper, in contrast, uses Gene Expression Programming (GEP) to construct two alternative models of different pan evaporation estimations by means of symbolic regression: a simulator, a model of a real-world process developed on observed records, and an emulator, an imitator of some other model developed on predicted outputs calculated by that source model. The solutions are compared and contrasted for the purposes of determining whether any substantial differences exist between either option. This analysis will address recent arguments over the impact of using downloaded hydrological modelling datasets originating from different initial sources i.e. observed or calculated. These differences can be easily be overlooked by modellers, resulting in a model of a model developed on estimations derived from deterministic empirical equations and producing exceptionally high goodness-of-fit. This paper uses different lines-of-evidence to evaluate model output and in so doing paves the way for a new protocol in machine learning applications. Transparent modelling tools such as symbolic regression offer huge potential for explaining stochastic processes, however, the basic tenets of data quality and recourse to first principles with regard to problem understanding should not be trivialised. GEP is found to be an effective tool for the prediction of observed and calculated pan evaporation, with results supported by an understanding of the records, and of the natural processes concerned, evaluated using one-at-a-time response function sensitivity analysis. The results show that both architectures and response functions are very similar, implying that previously observed differences in goodness-of-fit can be explained by whether models are applied to observed or calculated data.

  15. Combining remote sensing and GIS climate modelling to estimate daily forest evapotranspiration in a Mediterranean mountain area

    NASA Astrophysics Data System (ADS)

    Cristóbal, J.; Poyatos, R.; Ninyerola, M.; Llorens, P.; Pons, X.

    2011-05-01

    Evapotranspiration monitoring allows us to assess the environmental stress on forest and agricultural ecosystems. Nowadays, Remote Sensing and Geographical Information Systems (GIS) are the main techniques used for calculating evapotranspiration at catchment and regional scales. In this study we present a methodology, based on the energy balance equation (B-method), that combines remote sensing imagery with GIS-based climate modelling to estimate daily evapotranspiration (ETd) for several dates between 2003 and 2005. The three main variables needed to compute ETd were obtained as follows: (i) Land surface temperature by means of the Landsat-5 TM and Landsat-7 ETM+ thermal band, (ii) air temperature by means of multiple regression analysis and spatial interpolation from meteorological ground stations data at satellite pass, and (iii) net radiation by means of the radiative balance. We calculated ETd using remote sensing data at different spatial and temporal scales (Landsat-7 ETM+, Landsat-5 TM and TERRA/AQUA MODIS, with a spatial resolution of 60, 120 and 1000 m, respectively) and combining three different approaches to calculate the B parameter, which represents an average bulk conductance for the daily-integrated sensible heat flux. We then compared these estimates with sap flow measurements from a Scots pine (Pinus sylvestris L.) stand in a Mediterranean mountain area. This procedure allowed us to better understand the limitations of ETd modelling and how it needs to be improved, especially in heterogeneous forest areas. The method using Landsat data resulted in a good agreement, R2 test of 0.89, with a mean RMSE value of about 0.6 mm day-1 and an estimation error of ±30 %. The poor agreement obtained using TERRA/AQUA MODIS, with a mean RMSE value of 1.8 and 2.4 mm day-1 and an estimation error of about ±57 and 50 %, respectively. This reveals that ETd retrieval from coarse resolution remote sensing data is troublesome in these heterogeneous areas, and

  16. Effect of lower-limb joint models on subject-specific musculoskeletal models and simulations of daily motor activities.

    PubMed

    Valente, Giordano; Pitto, Lorenzo; Stagni, Rita; Taddei, Fulvia

    2015-12-16

    Understanding the validity of using musculoskeletal models is critical, making important to assess how model parameters affect predictions. In particular, assumptions on joint models can affect predictions from simulations of movement, and the identification of image-based joints is unavoidably affected by uncertainty that can decrease the benefits of increasing model complexity. We evaluated the effect of different lower-limb joint models on muscle and joint contact forces during four motor tasks, and assessed the sensitivity to the uncertainties in the identification of anatomical four-bar-linkage joints. Three MRI-based musculoskeletal models having different knee and ankle joint models were created and used for the purpose. Model predictions were compared against a baseline model including simpler and widely-adopted joints. In addition, a probabilistic analysis was performed by perturbing four-bar-linkage joint parameters according to their uncertainty. The differences between models depended on the motor task analyzed, and there could be marked differences at peak loading (up to 2.40 BW at the knee and 1.54 BW at the ankle), although they were rather small over the motor task cycles (up to 0.59 BW at the knee and 0.31 BW at the ankle). The model including more degrees of freedom showed more discrepancies in predicted muscle activations compared to measured muscle activity. Further, including image-based four-bar-linkages was robust to simulate walking, chair rise and stair ascent, but not stair descent (peak standard deviation of 2.66 BW), suggesting that joint model complexity should be set according to the imaging dataset available and the intended application, performing sensitivity analyses.

  17. Evaluation of five lactation curve models fitted for fat:protein ratio of milk and daily energy balance.

    PubMed

    Buttchereit, N; Stamer, E; Junge, W; Thaller, G

    2010-04-01

    Selection for milk yield increases the metabolic load of dairy cows. The fat:protein ratio of milk (FPR) could serve as a measure of the energy balance status and might be used as a selection criterion to improve metabolic stability. The fit of different fixed and random regression models describing FPR and daily energy balance was tested to establish appropriate models for further genetic analyses. In addition, the relationship between both traits was evaluated for the best fitting model. Data were collected on a dairy research farm running a bull dam performance test. Energy balance was calculated using information on milk yield, feed intake per day, and live weight. Weekly FPR measurements were available. Three data sets were created containing records of 577 primiparous cows with observations from lactation d 11 to 180 as well as records of 613 primiparous cows and 96 multiparous cows with observations from lactation d 11 to 305. Five well-established parametric functions of days in milk (Ali and Schaeffer, Guo and Swalve, Wilmink, Legendre polynomials of third and fourth degree) were chosen for modeling the lactation curves. Evaluation of goodness of fit was based on the corrected Akaike information criterion, the Bayesian information criterion, correlation between the real observation and the estimated value, and on inspection of the residuals plotted against days in milk. The best model was chosen for estimation of correlations between both traits at different lactation stages. Random regression models were superior compared with the fixed regression models. In general, the Ali and Schaeffer function appeared most suitable for modeling both the fixed and the random regression part of the mixed model. The FPR is greatest in the initial lactation period when energy deficit is most pronounced. Energy balance stabilizes at the same point as the decrease in FPR stops. The inverted patterns indicate a causal relationship between the 2 traits. A common pattern was

  18. Development of total maximum daily loads for bacteria impaired watershed using the comprehensive hydrology and water quality simulation model.

    PubMed

    Kim, Sang M; Brannan, Kevin M; Zeckoski, Rebecca W; Benham, Brian L

    2014-01-01

    The objective of this study was to develop bacteria total maximum daily loads (TMDLs) for the Hardware River watershed in the Commonwealth of Virginia, USA. The TMDL program is an integrated watershed management approach required by the Clean Water Act. The TMDLs were developed to meet Virginia's water quality standard for bacteria at the time, which stated that the calendar-month geometric mean concentration of Escherichia coli should not exceed 126 cfu/100 mL, and that no single sample should exceed a concentration of 235 cfu/100 mL. The bacteria impairment TMDLs were developed using the Hydrological Simulation Program-FORTRAN (HSPF). The hydrology and water quality components of HSPF were calibrated and validated using data from the Hardware River watershed to ensure that the model adequately simulated runoff and bacteria concentrations. The calibrated and validated HSPF model was used to estimate the contributions from the various bacteria sources in the Hardware River watershed to the in-stream concentration. Bacteria loads were estimated through an extensive source characterization process. Simulation results for existing conditions indicated that the majority of the bacteria came from livestock and wildlife direct deposits and pervious lands. Different source reduction scenarios were evaluated to identify scenarios that meet both the geometric mean and single sample maximum E. coli criteria with zero violations. The resulting scenarios required extreme and impractical reductions from livestock and wildlife sources. Results from studies similar to this across Virginia partially contributed to a reconsideration of the standard's applicability to TMDL development.

  19. A comparison of daily precipitation metrics downscaled using SDSM and WRF + WRFDA models over the Iberian Peninsula.

    NASA Astrophysics Data System (ADS)

    José González-Rojí, Santos; Wilby, Robert L.; Sáenz, Jon; Ibarra-Berastegi, Gabriel

    2017-04-01

    Downscaling via the Statistical DownScaling Model (SDSM) version 5.2 and two different configurations of the dynamical WRF model (with and without 3DVAR data assimilation) was evaluated for the estimation of daily precipitation over 21 sites across the Iberian Peninsula during the period 2010-2014. Six different strategies were used to calibrate the SDSM model. These options cover (1) use of NCEP/NCAR R1 Reanalysis and (2) ERA Interim data for downscaling predictor variables calibrated with data from periods (3) 1948-2009 (NCEP/NCAR R1) and (4) 1979-2009 (NCEP/NCAR R1 and ERA Interim). Additionally, for the ERA Interim case, two different grid resolutions have been used, (5) 2.5° and (6) 0.75°. On the other side, for the NCEP/NCAR R1 case, only the 2.5° resolution has been used. Configuring the SDSM model in this way allows testing the sensitivity of the results to different origins of the predictors, fit to different calibration periods and use of different reanalysis resolutions. On the other hand, ERA Interim data at the highest resolution was used as the initial/boundary conditions to run WRF simulations with a 15 km x 15 km horizontal resolution over the Iberian Peninsula, for two different configurations. The first experiment (N) was run using the same configuration typically used for numerical downscaling, with information being fed through the boundaries of the domain. The second experiment (D) was run using 3DVAR data assimilation at 00UTC, 06UTC, 12UTC and 18UTC. In both cases, WRF simulations were run over the period 2009-2014, using the first year (2009) as spin-up for the soil model. Results from the WRF N and D runs and comparable SDSM set up for the period 2010-2014 were evaluated using observations from ECA and E-OBS datasets. In each case, model skill was assessed using seven daily precipitation metrics (absolute mean, wet-day intensity, 90th percentile, maximum 5-day total, maximum number of consecutive dry days, fraction of total from heavy

  20. A Conceptual Model for Teaching the Relationship of Daily Life and Human Environmental Impact to Ecological Function

    ERIC Educational Resources Information Center

    Wyner, Yael

    2013-01-01

    In the general activity of daily life, it is easy to miss our dependency on the Earth's ecology. At the same time that people are living apparently separate from the environment, our impact on the Earth is increasing. This study seeks to understand how teachers can bridge this persistent disconnect of daily life from ecology and human impact.…

  1. The Role of Competence and Value Beliefs in Students' Daily Emotional Experiences: A Multilevel Test of a Transactional Model

    ERIC Educational Resources Information Center

    Ahmed, Wondimu; Minnaert, Alexander; van der Werf, Greetje; Kuyper, Hans

    2010-01-01

    This study examined the mutual influence of relatively stable personal competence and value beliefs and lesson specific appraisals of competence and value on daily emotional experiences of students in the classroom context. Personal competence and value beliefs were measured by means of questionnaire whereas appraisals and daily emotions were…

  2. The Role of Competence and Value Beliefs in Students' Daily Emotional Experiences: A Multilevel Test of a Transactional Model

    ERIC Educational Resources Information Center

    Ahmed, Wondimu; Minnaert, Alexander; van der Werf, Greetje; Kuyper, Hans

    2010-01-01

    This study examined the mutual influence of relatively stable personal competence and value beliefs and lesson specific appraisals of competence and value on daily emotional experiences of students in the classroom context. Personal competence and value beliefs were measured by means of questionnaire whereas appraisals and daily emotions were…

  3. Robust effects of cloud superparameterization on simulated daily rainfall intensity statistics across multiple versions of the Community Earth System Model

    DOE PAGES

    Kooperman, Gabriel J.; Pritchard, Michael S.; Burt, Melissa A.; ...

    2016-02-01

    This study evaluates several important statistics of daily rainfall based on frequency and amount distributions as simulated by a global climate model whose precipitation does not depend on convective parameterization—Super-Parameterized Community Atmosphere Model (SPCAM). Three superparameterized and conventional versions of CAM, coupled within the Community Earth System Model (CESM1 and CCSM4), are compared against two modern rainfall products (GPCP 1DD and TRMM 3B42) to discriminate robust effects of superparameterization that emerge across multiple versions. The geographic pattern of annual-mean rainfall is mostly insensitive to superparameterization, with only slight improvements in the double-ITCZ bias. However, unfolding intensity distributions reveal several improvementsmore » in the character of rainfall simulated by SPCAM. The rainfall rate that delivers the most accumulated rain (i.e., amount mode) is systematically too weak in all versions of CAM relative to TRMM 3B42 and does not improve with horizontal resolution. It is improved by superparameterization though, with higher modes in regions of tropical wave, Madden-Julian Oscillation, and monsoon activity. Superparameterization produces better representations of extreme rates compared to TRMM 3B42, without sensitivity to horizontal resolution seen in CAM. SPCAM produces more dry days over land and fewer over the ocean. Updates to CAM’s low cloud parameterizations have narrowed the frequency peak of light rain, converging toward SPCAM. Poleward of 50°, where more rainfall is produced by resolved-scale processes in CAM, few differences discriminate the rainfall properties of the two models. Lastly, these results are discussed in light of their implication for future rainfall changes in response to climate forcing.« less

  4. Robust effects of cloud superparameterization on simulated daily rainfall intensity statistics across multiple versions of the Community Earth System Model

    NASA Astrophysics Data System (ADS)

    Kooperman, Gabriel J.; Pritchard, Michael S.; Burt, Melissa A.; Branson, Mark D.; Randall, David A.

    2016-03-01

    This study evaluates several important statistics of daily rainfall based on frequency and amount distributions as simulated by a global climate model whose precipitation does not depend on convective parameterization—Super-Parameterized Community Atmosphere Model (SPCAM). Three superparameterized and conventional versions of CAM, coupled within the Community Earth System Model (CESM1 and CCSM4), are compared against two modern rainfall products (GPCP 1DD and TRMM 3B42) to discriminate robust effects of superparameterization that emerge across multiple versions. The geographic pattern of annual-mean rainfall is mostly insensitive to superparameterization, with only slight improvements in the double-ITCZ bias. However, unfolding intensity distributions reveal several improvements in the character of rainfall simulated by SPCAM. The rainfall rate that delivers the most accumulated rain (i.e., amount mode) is systematically too weak in all versions of CAM relative to TRMM 3B42 and does not improve with horizontal resolution. It is improved by superparameterization though, with higher modes in regions of tropical wave, Madden-Julian Oscillation, and monsoon activity. Superparameterization produces better representations of extreme rates compared to TRMM 3B42, without sensitivity to horizontal resolution seen in CAM. SPCAM produces more dry days over land and fewer over the ocean. Updates to CAM's low cloud parameterizations have narrowed the frequency peak of light rain, converging toward SPCAM. Poleward of 50°, where more rainfall is produced by resolved-scale processes in CAM, few differences discriminate the rainfall properties of the two models. These results are discussed in light of their implication for future rainfall changes in response to climate forcing.

  5. Evaluating the Impacts of NASA/SPoRT Daily Greenness Vegetation Fraction on Land Surface Model and Numerical Weather Forecasts

    NASA Technical Reports Server (NTRS)

    Bell, Jordan R.; Case, Jonathan L.; Molthan, Andrew L.

    2011-01-01

    The NASA Short-term Prediction Research and Transition (SPoRT) Center develops new products and techniques that can be used in operational meteorology. The majority of these products are derived from NASA polar-orbiting satellite imagery from the Earth Observing System (EOS) platforms. One such product is a Greenness Vegetation Fraction (GVF) dataset, which is produced from Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the NASA EOS Aqua and Terra satellites. NASA SPoRT began generating daily real-time GVF composites at 1-km resolution over the Continental United States (CONUS) on 1 June 2010. The purpose of this study is to compare the National Centers for Environmental Prediction (NCEP) climatology GVF product (currently used in operational weather models) to the SPoRT-MODIS GVF during June to October 2010. The NASA Land Information System (LIS) was employed to study the impacts of the new SPoRT-MODIS GVF dataset on land surface models apart from a full numerical weather prediction (NWP) model. For the 2010 warm season, the SPoRT GVF in the western portion of the CONUS was generally higher than the NCEP climatology. The eastern CONUS GVF had variations both above and below the climatology during the period of study. These variations in GVF led to direct impacts on the rates of heating and evaporation from the land surface. The second phase of the project is to examine the impacts of the SPoRT GVF dataset on NWP using the Weather Research and Forecasting (WRF) model. Two separate WRF model simulations were made for individual severe weather case days using the NCEP GVF (control) and SPoRT GVF (experimental), with all other model parameters remaining the same. Based on the sensitivity results in these case studies, regions with higher GVF in the SPoRT model runs had higher evapotranspiration and lower direct surface heating, which typically resulted in lower (higher) predicted 2-m temperatures (2-m dewpoint temperatures). The opposite was true

  6. Daily and compulsive internet use and well-being in adolescence: a diathesis-stress model based on big five personality traits.

    PubMed

    van der Aa, Niels; Overbeek, Geertjan; Engels, Rutger C M E; Scholte, Ron H J; Meerkerk, Gert-Jan; Van den Eijnden, Regina J J M

    2009-07-01

    This study examined the associations between adolescents' daily Internet use and low well-being (i.e., loneliness, low self-esteem, and depressive moods). We hypothesized that (a) linkages between high levels of daily Internet use and low well-being would be mediated by compulsive Internet use (CIU), and (b) that adolescents with low levels of agreeableness and emotional stability, and high levels of introversion would be more likely to develop CIU and lower well-being. Data were used from a sample of 7888 Dutch adolescents (11-21 years). Results from structural equation modeling analyses showed that daily Internet use was indirectly related to low well-being through CIU. In addition, daily Internet use was found to be more strongly related to CIU in introverted, low-agreeable, and emotionally less-stable adolescents. In turn, again, CIU was more strongly linked to loneliness in introverted, emotionally less-stable, and less agreeable adolescents.

  7. From daily to sub-daily time steps - Creating a high temporal and spatial resolution climate reference data set for hydrological modeling and bias-correction of RCM data

    NASA Astrophysics Data System (ADS)

    Willkofer, Florian; Wood, Raul R.; Schmid, Josef; von Trentini, Fabian; Ludwig, Ralf

    2016-04-01

    The ClimEx project (Climate change and hydrological extreme events - risks and perspectives for water management in Bavaria and Québec) focuses on the effects of climate change on hydro-meteorological extreme events and their implications for water management in Bavaria and Québec. It builds on the conjoint analysis of a large ensemble of the CRCM5, driven by 50 members of the CanESM2, and the latest information provided through the CORDEX-initiative, to better assess the influence of natural climate variability and climatic change on the dynamics of extreme events. A critical point in the entire project is the preparation of a meteorological reference dataset with the required temporal (1-6h) and spatial (500m) resolution to be able to better evaluate hydrological extreme events in mesoscale river basins. For Bavaria a first reference data set (daily, 1km) used for bias-correction of RCM data was created by combining raster based data (E-OBS [1], HYRAS [2], MARS [3]) and interpolated station data using the meteorological interpolation schemes of the hydrological model WaSiM [4]. Apart from the coarse temporal and spatial resolution, this mosaic of different data sources is considered rather inconsistent and hence, not applicable for modeling of hydrological extreme events. Thus, the objective is to create a dataset with hourly data of temperature, precipitation, radiation, relative humidity and wind speed, which is then used for bias-correction of the RCM data being used as driver for hydrological modeling in the river basins. Therefore, daily data is disaggregated to hourly time steps using the 'Method of fragments' approach [5], based on available training stations. The disaggregation chooses fragments of daily values from observed hourly datasets, based on similarities in magnitude and behavior of previous and subsequent events. The choice of a certain reference station (hourly data, provision of fragments) for disaggregating daily station data (application

  8. Using spatially detailed water-quality data and solute-transport modeling to improve support total maximum daily load development

    USGS Publications Warehouse

    Walton-Day, Katherine; Runkel, Robert L.; Kimball, Briant A.

    2012-01-01

    Spatially detailed mass-loading studies and solute-transport modeling using OTIS (One-dimensional Transport with Inflow and Storage) demonstrate how natural attenuation and loading from distinct and diffuse sources control stream water quality and affect load reductions predicted in total maximum daily loads (TMDLs). Mass-loading data collected during low-flow from Cement Creek (a low-pH, metal-rich stream because of natural and mining sources, and subject to TMDL requirements) were used to calibrate OTIS and showed spatially variable effects of natural attenuation (instream reactions) and loading from diffuse (groundwater) and distinct sources. OTIS simulations of the possible effects of TMDL-recommended remediation of mine sites showed less improvement to dissolved zinc load and concentration (14% decrease) than did the TMDL (53-63% decrease). The TMDL (1) assumed conservative transport, (2) accounted for loads removed by remediation by subtracting them from total load at the stream mouth, and (3) did not include diffuse-source loads. In OTIS, loads were reduced near their source; the resulting concentration was decreased by natural attenuation and increased by diffuse-source loads during downstream transport. Thus, by not including natural attenuation and loading from diffuse sources, the TMDL overestimated remediation effects at low flow. Use of the techniques presented herein could improve TMDLs by incorporating these processes during TMDL development.

  9. Long-term outcome and prediction models of activities of daily living in Alzheimer disease with cholinesterase inhibitor treatment.

    PubMed

    Wattmo, Carina; Wallin, Åsa K; Londos, Elisabet; Minthon, Lennart

    2011-01-01

    In untreated patients with Alzheimer disease (AD) the functional ability is gradually lost. What happens to the patients after continuous long-term cholinesterase inhibitor (ChEI) treatment is less investigated. The objective of this study was to describe the longitudinal functional outcome and analyze factors affecting the outcome in ChEI-treated patients. In an open, 3-year, nonrandomized, prospective, multicenter study in a routine clinical setting, 790 patients were treated with either donepezil, rivastigmine, or galantamine. At baseline and every 6 months, they were assessed with several rating scales including Instrumental Activities of Daily Living (IADL), Physical Self-Maintenance Scale (PSMS), and Mini-Mental State Examination (MMSE). A faster functional decline was associated with lower cognitive ability at baseline, older age, and the interaction of higher education and longer time in the study. The patients residing with a spouse or relative showed slower deterioration in IADL score. A higher mean dose of ChEI, regardless of drug agent, was also related to slower instrumental ADL decline. Prediction models for longitudinal functional outcome were provided. AD severity at baseline is a key factor in obtaining reliable clinical prognoses of the long-term ADL ability. The dosage of ChEI treatment could possibly lead to a different functional outcome.

  10. Daily intake of Jeju groundwater improves the skin condition of the model mouse for human atopic dermatitis.

    PubMed

    Tanaka, Akane; Jung, Kyungsook; Matsuda, Akira; Jang, Hyosun; Kajiwara, Naoki; Amagai, Yosuke; Oida, Kumiko; Ahn, Ginnae; Ohmori, Keitaro; Kang, Kyung-goo; Matsuda, Hiroshi

    2013-03-01

    Drinking water is an important nutrient for human health. The mineral ingredients included in drinking water may affect the physical condition of people. Various kinds of natural water are in circulation as bottled water in developed countries; however, its influence on clinical conditions of patients with certain diseases has not been fully evaluated. In this study, effects of the natural groundwater from Jeju Island on clinical symptoms and skin barrier function in atopic dermatitis (AD) were evaluated. NC/Tnd mice, a model for human AD, with moderate to severe dermatitis were used. Mice were given different natural groundwater or tap water for 8 weeks from 4 weeks of age. Clinical skin severity scores were recorded every week. Scratching analysis and measurement of transepidermal water loss were performed every other week. The pathological condition of the dorsal skin was evaluated histologically. Real-time polymerase chain reaction analysis was performed for cytokine expression in the affected skin. The epidermal hyperplasia and allergic inflammation were reduced in atopic mice supplied with Jeju groundwater when compared to those supplied with tap water or other kinds of natural groundwater. The increase in scratching behavior with the aggravation of clinical severity of dermatitis was favorably controlled. Moreover, transepidermal water loss that reflects skin barrier function was recovered. The early inflammation and hypersensitivity in the atopic skin was alleviated in mice supplied with Jeju groundwater, suggesting its profitable potential on the daily care of patients with skin troubles including AD.

  11. A cross-cultural study of relationships between daily social interaction and the five-factor model of personality.

    PubMed

    Nezlek, John B; Schütz, Astrid; Schröder-Abé, Michela; Smith, C Veronica

    2011-08-01

    Two studies, one in the United States (N = 130) and another in Germany (N = 100), examined relationships between daily social interaction and the traits of the Five-Factor Model. In both studies, student participants described their social interactions for 2 weeks using the Rochester Interaction Record. In both countries, Agreeableness and Conscientiousness were positively related to reactions to social interaction, whereas Neuroticism was unrelated to reactions to interactions. In the United States, Extraversion and Openness were positively related to reactions to interactions, whereas these factors were not related to reactions to interactions in Germany. In the United States, Extraversion was positively related to how socially active participants were, whereas none of the FFM traits was related to amount of social interaction in the German sample. In both countries, Extraversion was positively related to percent of interactions involving friends. The results highlight the importance of taking into account the sociocultural milieus within which personality unfolds. © 2011 The Authors. Journal Compilation © 2011, Wiley Periodicals, Inc.

  12. A Hybrid Framework to Bias Correct and Empirically Downscale Daily Temperature and Precipitation from Regional Climate Models

    NASA Astrophysics Data System (ADS)

    Tan, P.; Abraham, Z.; Winkler, J. A.; Perdinan, P.; Zhong, S. S.; Liszewska, M.

    2013-12-01

    Bias correction and statistical downscaling are widely used approaches for postprocessing climate simulations generated by global and/or regional climate models. The skills of these approaches are typically assessed in terms of their ability to reproduce historical climate conditions as well as the plausibility and consistency of the derived statistical indicators needed by end users. Current bias correction and downscaling approaches often do not adequately satisfy the two criteria of accurate prediction and unbiased estimation. To overcome this limitation, a hybrid regression framework was developed to both minimize prediction errors and preserve the distributional characteristics of climate observations. Specifically, the framework couples the loss functions of standard (linear or nonlinear) regression methods with a regularization term that penalizes for discrepancies between the predicted and observed distributions. The proposed framework can also be extended to generate physically-consistent outputs across multiple response variables, and to incorporate both reanalysis-driven and GCM-driven RCM outputs into a unified learning framework. The effectiveness of the framework is demonstrated using daily temperature and precipitation simulations from the North American Regional Climate Change Program (NARCCAP) . The accuracy of the framework is comparable to standard regression methods, but, unlike the standard regression methods, the proposed framework is able to preserve many of the distribution properties of the response variables, akin to bias correction approaches such as quantile mapping and bivariate geometric quantile mapping.

  13. Antihypertensive and antioxidant effects of a single daily dose of sodium nitrite in a model of renovascular hypertension.

    PubMed

    Montenegro, Marcelo F; Pinheiro, Lucas C; Amaral, Jefferson H; Marçal, Diogo M O; Palei, Ana C T; Costa-Filho, Antonio J; Tanus-Santos, Jose E

    2012-05-01

    Dietary nitrite and nitrate have been reported as alternative sources of nitric oxide (NO). In this regard, we reported previously that sodium nitrite added to drinking water was able to exert antihypertensive effects in an experimental model of hypertension in a dose-dependent manner. Taking into consideration that nitrite is continuously converted to nitrate in the bloodstream, here we expanded our previous report and evaluate whether a single daily dose of sodium nitrite could exert antihypertensive effects in 2 kidney-1 clip (2K1C) hypertensive rats. Sham-operated and 2K1C rats were treated with vehicle or sodium nitrite (15 mg/kg/day) for 4 weeks. We evaluated the effects induced by sodium nitrite treatment on systolic blood pressure (SBP) and NO markers such as plasma nitrite, nitrite + nitrate (NOx), cGMP, and blood levels of nitrosyl-hemoglobin. In addition, we also evaluated effects of nitrite on oxidative stress and antioxidant enzymes. Dihydroethidium (DHE) was used to evaluate aortic reactive oxygen species (ROS) production by fluorescence microscopy, and plasma levels of thiobarbituric acid-reactive species (TBARS) were measured in plasma samples from all experimental groups. Red blood cell superoxide dismutase (SOD) and catalase activity were evaluated with commercial kits. Sodium nitrite treatment reduced SBP in 2K1C rats (P < 0.05). We found lower plasma nitrite and NOx levels in 2K1C rats compared with normotensive controls (both P < 0.05). Nitrite treatment restored the lower levels of nitrite and NOx. While no change was found in the blood levels of nitrosyl-hemoglobin (P > 0.05), nitrite treatment increased the plasma levels of cGMP in 2K1C rats (P < 0.05). Higher plasma TBARS levels and aortic ROS levels were found in hypertensive rats compared with controls (P < 0.05), and nitrite blunted these alterations. Lower SOD and catalase activities were found in 2K1C hypertensive rats compared with controls (both P < 0

  14. Future daily PM10 concentrations prediction by combining regression models and feedforward backpropagation models with principle component analysis (PCA)

    NASA Astrophysics Data System (ADS)

    Ul-Saufie, Ahmad Zia; Yahaya, Ahmad Shukri; Ramli, Nor Azam; Rosaida, Norrimi; Hamid, Hazrul Abdul

    2013-10-01

    Future PM10 concentration prediction is very important because it can help local authorities to enact preventative measures to reduce the impact of air pollution. The aims of this study are to improve prediction of Multiple Linear Regression (MLR) and Feedforward backpropagation (FFBP) by combining them with principle component analysis for predicting future (next day, next two-day and next three-day) PM10 concentration in Negeri Sembilan, Malaysia. Annual hourly observations for PM10 in Negeri Sembilan, Malaysia from January 2003 to December 2010 were selected for predicting PM10 concentration level. Eighty percent of the monitoring records were used for training and twenty percent were used for validation of the models. Three accuracy measures - Prediction Accuracy (PA), Coefficient of Determination (R2) and Index of Agreement (IA), as well as two error measures - Normalized Absolute Error (NAE) and Root Mean Square Error (RMSE) were used to evaluate the performance of the models. Results show that PCA models combined with MLR and PCA with FFBP improved MLR and FFBP models for all three days in advance of predicting PM10 concentration, with reduced errors by as much as 18.1% (PCA-MLR) and 17.68% (PCA-FFBP) for next day, 19.2% (PCA-MLR) and 22.1% (PCA-FFBP) for next two-day and 18.7% (PCA-MLR) and 22.79% (PCA-FFBP) for next three-day predictions. Including PCA improved the accuracy of the models by as much as by 12.9% (PCA-MLR) and 13.3% (PCA-FFBP) for next day, 32.3% (PCA-MLR) and 14.7% (PCA-FFBP) for next two-day and 46.1% (PCA-MLR) and 19.3% (PCA-FFBP) for next three-day predictions.

  15. Spatio-temporal models to estimate daily concentrations of fine particulate matter in Montreal: Kriging with external drift and inverse distance-weighted approaches.

    PubMed

    Ramos, Yuddy; St-Onge, Benoît; Blanchet, Jean-Pierre; Smargiassi, Audrey

    2016-06-01

    Air pollution is a major environmental and health problem, especially in urban agglomerations. Estimating personal exposure to fine particulate matter (PM2.5) remains a great challenge because it requires numerous point measurements to explain the daily spatial variation in pollutant levels. Furthermore, meteorological variables have considerable effects on the dispersion and distribution of pollutants, which also depends on spatio-temporal emission patterns. In this study we developed a hybrid interpolation technique that combined the inverse distance-weighted (IDW) method with Kriging with external drift (KED), and applied it to daily PM2.5 levels observed at 10 monitoring stations. This provided us with downscaled high-resolution maps of PM2.5 for the Island of Montreal. For the KED interpolation, we used spatio-temporal daily meteorological estimates and spatial covariates as land use and vegetation density. Different KED and IDW daily estimation models for the year 2010 were developed for each of the six synoptic weather classes. These clusters were developed using principal component analysis and unsupervised hierarchical classification. The results of the interpolation models were assessed with a leave-one-station-out cross-validation. The performance of the hybrid model was better than that of the KED or the IDW alone for all six synoptic weather classes (the daily estimate for R(2) was 0.66-0.93 and for root mean square error (RMSE) 2.54-1.89 μg/m(3)).

  16. Daily exposure to summer circadian cycles affects spermatogenesis, but not fertility in an in vivo rabbit model.

    PubMed

    Sabés-Alsina, Maria; Planell, Núria; Torres-Mejia, Elen; Taberner, Ester; Maya-Soriano, Maria José; Tusell, Llibertat; Ramon, Josep; Dalmau, Antoni; Piles, Miriam; Lopez-Bejar, Manel

    2015-01-15

    Heat stress (HS) in mammals is a determining factor in the deterioration of spermatogenesis and can cause infertility. The aim of this study was to evaluate the effect of continuous summer circadian cycles on semen production, sperm cell features, fertility, prolificacy, and fecal cortisol metabolites from rabbits kept under an in vivo HS model. We split randomly 60 New Zealand White rabbits into two temperature-controlled rooms: The control group was maintained at comfort temperature (18 °C-22 °C) and an HS group, where the environmental temperature was programmed to increase from 22 °C to 31 °C and be maintained for 3 hours to this temperature at the central part of the day. Fecal cortisol metabolites were assessed to evaluate the stress conditions. Seminal parameters were analyzed. Although animals exposed to HS showed higher values of fecal cortisol metabolites (P = 0.0003), no differences were detected in fertility or prolificacy. Semen samples from HS males showed a significant decrease (P < 0.05) with respect to the controls in the percentage of viable spermatozoa (80.71% vs. 74.21%), and a significant (P ≤ 0.01) increase in the percentage of acrosomic abnormalities (22.57% vs. 36.96%) and tailless spermatozoa (7.91% vs. 12.83). Among motility parameters, no differences were found. This study describes a model of HS simulating a continuous summer daily cycle that allows periods of time to recover as it occurs under natural conditions. Although negative effects have been detected in several sperm parameters, fertility and prolificacy were not affected, suggesting a recovery of the reproductive function when normal conditions are reestablished.

  17. Combining remote sensing and GIS climate modelling to estimate daily forest evapotranspiration in a Mediterranean mountain area

    NASA Astrophysics Data System (ADS)

    Cristóbal, J.; Poyatos, R.; Ninyerola, M.; Llorens, P.; Pons, X.

    2011-01-01

    Evapotranspiration monitoring allows us to assess the environmental stress on forest and agricultural ecosystems. Nowadays, Remote Sensing and Geographical Information Systems (GIS) are the main techniques used for calculating evapotranspiration at catchment and regional scales. In this study we present a methodology, based on the energy balance equation (B-method), that combines remote sensing imagery with GIS climate modelling to estimate daily evapotranspiration (ETd) for several dates between 2003 and 2005. The three main variables needed to compute ETd were obtained as follows: (i) Land surface temperature by means of the Landsat-5 TM and Landsat-7 ETM+ thermal band, (ii) air temperature by means of multiple regression analysis and spatial interpolation from meteorological ground stations data at satellite pass, and (iii) net radiation by means of the radiative balance. We calculated ETd using remote sensing data at different spatial and temporal scales (TERRA/AQUA MODIS and Landsat-5 TM/Landsat-7 ETM+) and combining three different approaches to calculate the B parameter. We then compared these estimates with sap flow measurements from a Scots pine (Pinus sylvestris L.) stand in a Mediterranean mountain area. This procedure allowed us to better understand the limitations of ETd modelling and how it needs to be improved, especially in heterogeneous forest areas. The method using Landsat data resulted in a good agreement, with a mean RMSE value of about 0.6 mm day-1 and an estimation error of ±30%. The poor agreement obtained using MODIS data reveals that ETd retrieval from coarse resolution remote sensing data is troublesome in these heterogeneous areas, and therefore further research is necessary on this issue.

  18. Review of revised Klamath River Total Maximum Daily Load models from Link River Dam to Keno Dam, Oregon

    USGS Publications Warehouse

    Rounds, Stewart A.; Sullivan, Annett B.

    2013-01-01

    Flow and water-quality models are being used to support the development of Total Maximum Daily Load (TMDL) plans for the Klamath River downstream of Upper Klamath Lake (UKL) in south-central Oregon. For riverine reaches, the RMA-2 and RMA-11 models were used, whereas the CE-QUAL-W2 model was used to simulate pooled reaches. The U.S. Geological Survey (USGS) was asked to review the most upstream of these models, from Link River Dam at the outlet of UKL downstream through the first pooled reach of the Klamath River from Lake Ewauna to Keno Dam. Previous versions of these models were reviewed in 2009 by USGS. Since that time, important revisions were made to correct several problems and address other issues. This review documents an assessment of the revised models, with emphasis on the model revisions and any remaining issues. The primary focus of this review is the 19.7-mile Lake Ewauna to Keno Dam reach of the Klamath River that was simulated with the CE-QUAL-W2 model. Water spends far more time in the Lake Ewauna to Keno Dam reach than in the 1-mile Link River reach that connects UKL to the Klamath River, and most of the critical reactions affecting water quality upstream of Keno Dam occur in that pooled reach. This model review includes assessments of years 2000 and 2002 current conditions scenarios, which were used to calibrate the model, as well as a natural conditions scenario that was used as the reference condition for the TMDL and was based on the 2000 flow conditions. The natural conditions scenario included the removal of Keno Dam, restoration of the Keno reef (a shallow spot that was removed when the dam was built), removal of all point-source inputs, and derivation of upstream boundary water-quality inputs from a previously developed UKL TMDL model. This review examined the details of the models, including model algorithms, parameter values, and boundary conditions; the review did not assess the draft Klamath River TMDL or the TMDL allocations

  19. Validation of modeled daily erythemal exposure along tropical and subtropical shipping routes by ship-based and satellite-based measurements

    NASA Astrophysics Data System (ADS)

    Feister, Uwe; Meyer, Gabriele; Laschewski, Gudrun; Boettcher, Christopher

    2015-05-01

    The Personal ERythemal EXposure (PEREX) model for seafarers working on decks of vessels has been developed to be used for retrospective estimates of personal occupational erythemal exposure in dependence of work profile, time period, and sea route. Extremely high UV index values up to 22 and daily erythemal exposure up to 89 standard erythemal dose have been derived from ship-based measurements in tropical oceans. Worldwide climatological maps of daily solar erythemal exposure derived from 10 year (2004-2013) hourly grid point radiative transfer model calculations for both cloudless sky and cloudy sky serve as the database of PEREX. The PEREX database is compared with ship-based measurements taken along four routes of merchant vessels, continuous UV radiation measurements taken on the research vessel Meteor on its mainly tropical and subtropical routes for 2 years, daily cloudless-sky erythemal exposure derived from 10 min LibRadtran radiative transfer model calculations, and 2 years of satellite-based erythemal exposure data of the Ozone Monitoring Instrument on the Aura satellite along the ship routes. Systematic differences between PEREX model data, ship-based data, and satellite-based daily erythemal exposure for all-sky conditions are only 1 to 3%, while short-term variations of cloudiness result in standard deviations of differences around 30%. Measured ratios between cloudless-sky erythemal radiation at vertical to horizontal incidence decrease with decreasing solar zenith angle, while clouds flatten their diurnal course.

  20. Using a Hero as a Model in Video Instruction to Improve the Daily Living Skills of an Elementary-Aged Student with Autism Spectrum Disorder: A Pilot Study

    ERIC Educational Resources Information Center

    Ohtake, Yoshihisa

    2015-01-01

    The present pilot study investigated the impact of video hero modelling (VHM) on the daily living skills of an elementary-aged student with autism spectrum disorder. The VHM, in which a character much admired by the student exhibited a correct response, was shown to the participant immediately before the situation where he needed to exhibit the…

  1. Toward the Development of an Inventory of Daily Widowed Life (IDWL): Guided by the Dual Process Model of Coping with Bereavement

    ERIC Educational Resources Information Center

    Caserta, Michael S.; Lund, Dale A.

    2007-01-01

    "The Dual Process Model of Coping with Bereavement" (M. Stroebe & H. Schut, 1999) suggests that the most effective adaptation involves oscillation between 2 coping processes: loss-orientation (LO) and restoration-orientation (RO). A 22-item Inventory of Daily Widowed Life (IDWL) was developed to measure these processes and the…

  2. Use of Video Modeling and Video Prompting Interventions for Teaching Daily Living Skills to Individuals with Autism Spectrum Disorders: A Review

    ERIC Educational Resources Information Center

    Gardner, Stephanie; Wolfe, Pamela

    2013-01-01

    Identifying methods to increase the independent functioning of individuals with autism spectrum disorders (ASD) is vital in enhancing their quality of life; teaching students with ASD daily living skills can foster independent functioning. This review examines interventions that implement video modeling and/or prompting to teach individuals with…

  3. Use of Video Modeling and Video Prompting Interventions for Teaching Daily Living Skills to Individuals with Autism Spectrum Disorders: A Review

    ERIC Educational Resources Information Center

    Gardner, Stephanie; Wolfe, Pamela

    2013-01-01

    Identifying methods to increase the independent functioning of individuals with autism spectrum disorders (ASD) is vital in enhancing their quality of life; teaching students with ASD daily living skills can foster independent functioning. This review examines interventions that implement video modeling and/or prompting to teach individuals with…

  4. Estimation of daily global solar radiation using wavelet regression, ANN, GEP and empirical models: A comparative study of selected temperature-based approaches

    NASA Astrophysics Data System (ADS)

    Sharifi, Sayed Saber; Rezaverdinejad, Vahid; Nourani, Vahid

    2016-11-01

    Although the sunshine-based models generally have a better performance than temperature-based models for estimating solar radiation, the limited availability of sunshine duration records makes the development of temperature-based methods inevitable. This paper presents a comparative study between Artificial Neural Networks (ANNs), Gene Expression Programming (GEP), Wavelet Regression (WR) and 5 selected temperature-based empirical models for estimating the daily global solar radiation. A new combination of inputs including four readily accessible parameters have been employed: daily mean clearness index (KT), temperature range (ΔT), theoretical sunshine duration (N) and extraterrestrial radiation (Ra). Ten statistical indicators in a form of GPI (Global Performance Indicator) is used to ascertain the suitability of the models. The performance of selected models across the range of solar radiation values, was depicted by the quantile-quantile (Q-Q) plots. Comparing these plots makes it evident that ANNs can cover a broader range of solar radiation values. The results shown indicate that the performance of ANN model was clearly superior to the other models. The findings also demonstrated that WR model performed well and presented high accuracy in estimations of daily global solar radiation.

  5. Estimation of Daily Reference Evapotranspiration using Temperature Based Models and Remotely Sensed Data over Indian River Basin

    NASA Astrophysics Data System (ADS)

    R, Shwetha H.; D, Nagesh Kumar

    2015-04-01

    Reference evapotranspiration (ETo) is the most significant component of the hydrological budget. Accurate quantification of ETo is vital for proper water management, efficient agricultural activities, irrigation planning and irrigation scheduling. FAO Penman Montieth (FAO-PM) is the widely accepted and used method for the ETo estimation under all climatic conditions, but needs numerous inputs which are difficult to acquire in developing countries. In such conditions, temperature based models such as Hargreaves-Samani (HS) equation and Penman Montieth temperature (PMT) can be used, where only maximum and minimum temperatures are required. Spatial interpolation of meteorological parameters to calculate spatial variation of ETo results in inaccurate estimations at lowly densed weather stations. Hence, there is a necessity of simple and easy method to estimate spatial distribution of ETo. In this regard, remotely sensed data provides viable alternative approach to obtain continuous spatio-temporal ETo. In this study, we used temperature based ETo models with remotely sensed LST data to estimate spatio-temporal variation of ETo. Day and night LST (MYD11A1) data of the year 2010 for the Cauvery basin on a daily basis were obtained from MODIS sensor of Aqua satellite. Firstly, day and night land surface temperatures (LST) with HS and PMT methods were applied to estimate ETo. Secondly, maximum and minimum air temperatures were estimated from day and night LST respectively using simple linear regression and these air temperature data were used to estimate ETo. Estimated results were validated with the ETo calculated using meteorological data obtained from Automatic Weather Stations (AWS) by applying standard FAO-PM. The preliminary results revealed that, HS method with LST overestimated ETo in the study region. Statistical analysis showed PMT method with both LST and air temperatures performed better than the HS method. These two temperature based methods are often used for

  6. The challenge of modelling nitrogen management at the field scale: simulation and sensitivity analysis of N2O fluxes across nine experimental sites using DailyDayCent

    NASA Astrophysics Data System (ADS)

    Fitton, N.; Datta, A.; Hastings, A.; Kuhnert, M.; Topp, C. F. E.; Cloy, J. M.; Rees, R. M.; Cardenas, L. M.; Williams, J. R.; Smith, K.; Chadwick, D.; Smith, P.

    2014-09-01

    The United Kingdom currently reports nitrous oxide emissions from agriculture using the IPCC default Tier 1 methodology. However Tier 1 estimates have a large degree of uncertainty as they do not account for spatial variations in emissions. Therefore biogeochemical models such as DailyDayCent (DDC) are increasingly being used to provide a spatially disaggregated assessment of annual emissions. Prior to use, an assessment of the ability of the model to predict annual emissions should be undertaken, coupled with an analysis of how model inputs influence model outputs, and whether the modelled estimates are more robust that those derived from the Tier 1 methodology. The aims of the study were (a) to evaluate if the DailyDayCent model can accurately estimate annual N2O emissions across nine different experimental sites, (b) to examine its sensitivity to different soil and climate inputs across a number of experimental sites and (c) to examine the influence of uncertainty in the measured inputs on modelled N2O emissions. DailyDayCent performed well across the range of cropland and grassland sites, particularly for fertilized fields indicating that it is robust for UK conditions. The sensitivity of the model varied across the sites and also between fertilizer/manure treatments. Overall our results showed that there was a stronger correlation between the sensitivity of N2O emissions to changes in soil pH and clay content than the remaining input parameters used in this study. The lower the initial site values for soil pH and clay content, the more sensitive DDC was to changes from their initial value. When we compared modelled estimates with Tier 1 estimates for each site, we found that DailyDayCent provided a more accurate representation of the rate of annual emissions.

  7. Combining a two-sourcepatch model with satellite data to monitor daily evapotranspiration at a regional scale

    USDA-ARS?s Scientific Manuscript database

    In this work, we present a micro-meteorological approach for estimating surface energy fluxes that can be operationally used together with satellite images to monitor surface energy fluxes at a regional scale. In particular we will focus on the retrieval of daily evapotranspiration. The feasibility ...

  8. Calibration procedure for a daily flow model of small watersheds with snowmelt runoff in the Green River coal region of Colorado

    USGS Publications Warehouse

    Norris, J.M.; Parker, R.S.

    1985-01-01

    A calibration procedure was developed for the U.S. Geological Survey 's Precipitation-Runoff Modeling System for watersheds in which snowmelt is the major contributor to runoff. The model uses daily values of air temperature and precipitation as input and the output is mean daily discharge. The procedure appears sufficient to calibrate both streamflow volume and the timing of mean daily discharge if other model parameters are reasonably estimated. Model structure and sensitivity analysis suggest that one of the most important parameters is the available water-holding capacity of the soil (SMAX). Changing this parameter through a series of iterations, the calibration procedure minimizes the error between observed and predicted annual discharge. The calibration suggests that the single parameter SMAX may be sufficient for optimizing both the volumes and the timing of runoff, assuming other model parameters are adequately estimated. Additional optimization on parameters sensitive to timing does not appear to improve prediction. This indicates that these parameters were estimated accurately prior to calibration. Further investigation is needed on more watersheds to determine SMAX 's ability to calibrate volume and timing with a constant set of other model parameter values. (USGS)

  9. Daily exercise vs. caloric restriction for prevention of nonalcoholic fatty liver disease in the OLETF rat model.

    PubMed

    Rector, R Scott; Uptergrove, Grace M; Morris, E Matthew; Borengasser, Sarah J; Laughlin, M Harold; Booth, Frank W; Thyfault, John P; Ibdah, Jamal A

    2011-05-01

    The maintenance of normal body weight either through dietary modification or being habitually more physically active is associated with reduced incidence of nonalcoholic fatty liver disease (NAFLD). However, the means by which weight gain is prevented and potential mechanisms activated remain largely unstudied. Here, we sought to determine the effects of obesity prevention by daily exercise vs. caloric restriction on NAFLD in the hyperphagic, Otsuka Long-Evans Tokushima Fatty (OLETF) rat. At 4 wk of age, male OLETF rats (n = 7-8/group) were randomized to groups of ad libitum fed, sedentary (OLETF-SED), voluntary wheel running exercise (OLETF-EX), or caloric restriction (OLETF-CR; 70% of SED) until 40 wk of age. Nonhyperphagic, control strain Long-Evans Tokushima Otsuka (LETO) rats were kept in sedentary cage conditions for the duration of the study (LETO-SED). Both daily exercise and caloric restriction prevented obesity and the development of type 2 diabetes observed in the OLETF-SED rats, with glucose tolerance during a glucose tolerance test improved to a greater extent in the OLETF-EX animals (30-50% lower glucose and insulin areas under the curve, P < 0.05). Both daily exercise and caloric restriction also prevented excess hepatic triglyceride and diacylglycerol accumulation (P < 0.001), hepatocyte ballooning and nuclear displacement, and the increased perivenular fibrosis and collagen deposition that occurred in the obese OLETF-SED animals. However, despite similar hepatic phenotypes, OLETF-EX rats also exhibited increased hepatic mitochondrial fatty acid oxidation, enhanced oxidative enzyme function and protein content, and further suppression of hepatic de novo lipogenesis proteins compared with OLETF-CR. Prevention of obesity by either daily exercise or caloric restriction attenuates NAFLD development in OLETF rats. However, daily exercise may offer additional health benefits on glucose homeostasis and hepatic mitochondrial function compared with

  10. Modelling the impact of riparian forest changes on daily sediment yield: A case study in a meso-scale catchment in SE Germany.

    NASA Astrophysics Data System (ADS)

    Keesstra, Saskia; Temme, Arnaud; Feger, Karl-Heinz; van Miltenburg, Saskia

    2010-05-01

    The newly developed sediment delivery model LAPSUS-D has been tested in the meso-scale catchment (60km2) of the Wilde Weisseritz in South-East Germany. LAPSUS-D is the first sediment delivery model that runs with a daily time step and only uses the following input parameters: a DEM, a land use map, a soil map and daily precipitation and discharge data. As the model is new and was calibrated only for a catchment in South-West Poland, the model is now run simultaneous with a widely used sediment delivery model WaTEM/SEDEM (developed in Leuven, Belgium) which simulates erosion and deposition processes on a yearly basis. After a first assessment of the model performance in the German catchment, two scenarios to reduce the sediment yield at the outlet were run. The scenarios were made based on actual river restoration projects elsewhere in similar river settings, to make the scenarios a realistic option for the future. These scenarios were used to run both models to test how the new LAPSUS-D model performs. The comparison reveals the contrast between a yearly and RUSLE based model and the water balance model LAPSUS-D using daily input. The water balance approach includes the effects of the water storage capacity. Locally decreasing water storage capacity causes increased run-off and erosion at lower positions in the landscape. This effect is not visible with the RUSLE approach. Furthermore, the position of the riparian forest scenarios results in differences in the sediment yield simulated by the LAPSUS-D model. While modeling the riparian forest scenarios at different locations in the catchment by the WaTEM/SEDEM causes no difference in sedimentation yield.

  11. Myocardial infarction, ST-elevation and non-ST-elevation myocardial infarction and modelled daily pollution concentrations: a case-crossover analysis of MINAP data

    PubMed Central

    Butland, Barbara K; Atkinson, Richard W; Milojevic, Ai; Heal, Mathew R; Doherty, Ruth M; Armstrong, Ben G; MacKenzie, Ian A; Vieno, Massimo; Lin, Chun; Wilkinson, Paul

    2016-01-01

    Objectives To investigate associations between daily concentrations of air pollution and myocardial infarction (MI), ST-elevation MI (STEMI) and non-ST-elevation MI (NSTEMI). Methods Modelled daily ground-level gaseous, total and speciated particulate pollutant concentrations and ground-level daily mean temperature, all at 5 km×5 km horizontal resolution, were linked to 202 550 STEMI and 322 198 NSTEMI events recorded on the England and Wales Myocardial Ischaemia National Audit Project (MINAP) database. The study period was 2003–2010. A case-crossover design was used, stratified by year, month and day of the week. Data were analysed using conditional logistic regression, with pollutants modelled as unconstrained distributed lags 0–2 days. Results are presented as percentage change in risk per 10 µg/m3 increase in the pollutant relevant metric, having adjusted for daily mean temperature, public holidays, weekly influenza consultation rates and a sine-cosine annual cycle. Results There was no evidence of an association between MI or STEMI and any of O3, NO2, PM2.5, PM10 or selected PM2.5 components (sulfate and elemental carbon). For NSTEMI, there was a positive association with daily maximum 1-hour NO2 (0.27% (95% CI 0.01% to 0.54%)), which persisted following adjustment for O3 and adjustment for PM2.5. The association appeared to be confined to the midland and southern regions of England and Wales. Conclusions The study found no evidence of an association between the modelled pollutants (including components) investigated and STEMI but did find some evidence of a positive association between NO2 and NSTEMI. Confirmation of this association in other studies is required. PMID:27621827

  12. Performance of the Angstrom-Prescott Model (A-P) and SVM and ANN techniques to estimate daily global solar irradiation in Botucatu/SP/Brazil

    NASA Astrophysics Data System (ADS)

    da Silva, Maurício Bruno Prado; Francisco Escobedo, João; Juliana Rossi, Taiza; dos Santos, Cícero Manoel; da Silva, Sílvia Helena Modenese Gorla

    2017-07-01

    This study describes the comparative study of different methods for estimating daily global solar irradiation (H): Angstrom-Prescott (A-P) model and two Machine Learning techniques (ML) - Support Vector Machine (SVM) and Artificial Neural Network (ANN). The H database was measured from 1996 to 2011 in Botucatu/SP/Brazil. Different combinations of input variables were adopted. MBE, RMSE, d Willmott, r and r2 statistical indicators obtained in the validation of A-P and SVM and ANN models showed that: SVM technique has better performance in estimating H than A-P and ANN models. A-P model has better performance in estimating H than ANN.

  13. Nonparametric temporal downscaling with event-based population generating algorithm for RCM daily precipitation to hourly: Model development and performance evaluation

    NASA Astrophysics Data System (ADS)

    Lee, Taesam; Park, Taewoong

    2017-04-01

    It is critical to downscale temporally coarse GCM or RCM outputs (e.g., monthly or daily) to fine time scales, such as sub-daily or hourly. Recently, a temporal downscaling model employing a nonparametric framework (NTD) with k-nearest resampling and a genetic algorithm has been developed to preserve key statistics as well as the diurnal cycle. However, this model's usage can be limited in estimating precipitation for design storms or floods because the key statistics of annual maximum precipitation (AMP), especially for longer hourly durations, present a systematic bias that cannot be preserved due to the discontinuity of multiday consecutive precipitation events in the downscaling procedure. In the current study, we develop an approach to downscale a consecutive daily precipitation at once focusing on the reproduction of AMP totals for different durations instead of day-by-day downscaling. The proposed model has been verified with the precipitation datasets for the 60 stations across South Korea over the period 1979-2005. Additionally, two validation studies were performed with the recent datasets of 2006-2014 and nearest neighbor stations. The verification and the two validation tests conclude that the population-based NTD (PNTD) model proposed in the current study is superior to the existing NTD model in preserving the key statistics of the observed AMP series and suitable for downscaling future climate scenarios.

  14. Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis.

    PubMed

    Alam, Md Saniul; McNabola, Aonghus

    2015-05-01

    Estimation of daily average exposure to PM10 (particulate matter with an aerodynamic diameter<10 μm) using the available fixed-site monitoring stations (FSMs) in a city poses a great challenge. This is because typically FSMs are limited in number when considering the spatial representativeness of their measurements and also because statistical models of citywide exposure have yet to be explored in this context. This paper deals with the later aspect of this challenge and extends the widely used land use regression (LUR) approach to deal with temporal changes in air pollution and the influence of transboundary air pollution on short-term variations in PM10. Using the concept of multiple linear regression (MLR) modeling, the average daily concentrations of PM10 in two European cities, Vienna and Dublin, were modeled. Models were initially developed using the standard MLR approach in Vienna using the most recently available data. Efforts were subsequently made to (i) assess the stability of model predictions over time; (ii) explores the applicability of nonparametric regression (NPR) and artificial neural networks (ANNs) to deal with the nonlinearity of input variables. The predictive performance of the MLR models of the both cities was demonstrated to be stable over time and to produce similar results. However, NPR and ANN were found to have more improvement in the predictive performance in both cities. Using ANN produced the highest result, with daily PM10 exposure predicted at R2=66% for Vienna and 51% for Dublin. In addition, two new predictor variables were also assessed for the Dublin model. The variables representing transboundary air pollution and peak traffic count were found to account for 6.5% and 12.7% of the variation in average daily PM10 concentration. The variable representing transboundary air pollution that was derived from air mass history (from back-trajectory analysis) and population density has demonstrated a positive impact on model performance

  15. Effects of stage of pregnancy on variance components, daily milk yields and 305-day milk yield in Holstein cows, as estimated by using a test-day model.

    PubMed

    Yamazaki, T; Hagiya, K; Takeda, H; Osawa, T; Yamaguchi, S; Nagamine, Y

    2016-08-01

    Pregnancy and calving are elements indispensable for dairy production, but the daily milk yield of cows decline as pregnancy progresses, especially during the late stages. Therefore, the effect of stage of pregnancy on daily milk yield must be clarified to accurately estimate the breeding values and lifetime productivity of cows. To improve the genetic evaluation model for daily milk yield and determine the effect of the timing of pregnancy on productivity, we used a test-day model to assess the effects of stage of pregnancy on variance component estimates, daily milk yields and 305-day milk yield during the first three lactations of Holstein cows. Data were 10 646 333 test-day records for the first lactation; 8 222 661 records for the second; and 5 513 039 records for the third. The data were analyzed within each lactation by using three single-trait random regression animal models: one model that did not account for the stage of pregnancy effect and two models that did. The effect of stage of pregnancy on test-day milk yield was included in the model by applying a regression on days pregnant or fitting a separate lactation curve for each days open (days from calving to pregnancy) class (eight levels). Stage of pregnancy did not affect the heritability estimates of daily milk yield, although the additive genetic and permanent environmental variances in late lactation were decreased by accounting for the stage of pregnancy effect. The effects of days pregnant on daily milk yield during late lactation were larger in the second and third lactations than in the first lactation. The rates of reduction of the 305-day milk yield of cows that conceived fewer than 90 days after the second or third calving were significantly (P<0.05) greater than that after the first calving. Therefore, we conclude that differences between the negative effects of early pregnancy in the first, compared with later, lactations should be included when determining the optimal number of days open

  16. Comparison between two statistically based methods, and two physically based models developed to compute daily mean streamflow at ungaged locations in the Cedar River Basin, Iowa

    USGS Publications Warehouse

    Linhart, S. Mike; Nania, Jon F.; Christiansen, Daniel E.; Hutchinson, Kasey J.; Sanders, Curtis L.; Archfield, Stacey A.

    2013-01-01

    A variety of individuals from water resource managers to recreational users need streamflow information for planning and decisionmaking at locations where there are no streamgages. To address this problem, two statistically based methods, the Flow Duration Curve Transfer method and the Flow Anywhere method, were developed for statewide application and the two physically based models, the Precipitation Runoff Modeling-System and the Soil and Water Assessment Tool, were only developed for application for the Cedar River Basin. Observed and estimated streamflows for the two methods and models were compared for goodness of fit at 13 streamgages modeled in the Cedar River Basin by using the Nash-Sutcliffe and the percent-bias efficiency values. Based on median and mean Nash-Sutcliffe values for the 13 streamgages the Precipitation Runoff Modeling-System and Soil and Water Assessment Tool models appear to have performed similarly and better than Flow Duration Curve Transfer and Flow Anywhere methods. Based on median and mean percent bias values, the Soil and Water Assessment Tool model appears to have generally overestimated daily mean streamflows, whereas the Precipitation Runoff Modeling-System model and statistical methods appear to have underestimated daily mean streamflows. The Flow Duration Curve Transfer method produced the lowest median and mean percent bias values and appears to perform better than the other models.

  17. Proposed Hydrodynamic Model Increases the Ability of Land-Surface Models to Capture Intra-Daily Dynamics of Transpiration and Canopy Structure Effects

    NASA Astrophysics Data System (ADS)

    Matheny, A. M.; Bohrer, G.; Mirfenderesgi, G.; Schafer, K. V.; Ivanov, V. Y.

    2014-12-01

    Hydraulic limitations are known to control transpiration in forest ecosystems when the soil is drying or when the vapor pressure deficit between the air and stomata is very large, but they can also impact stomatal apertures under conditions of adequate soil moisture and lower evaporative demand. We use the NACP dataset of latent heat flux measurements and model observations for multiple sites and models to demonstrate models' difficulties in capturing intra-daily hysteresis. We hypothesize that this is a result of un-resolved afternoon stomata closure due to hydrodynamic stresses. The current formulations for stomatal conductance and the empirical coupling between stomatal conductance and soil moisture used by these models does not resolve the hydrodynamic process of water movement from the soil to the leaves. This approach does not take advantage of advances in our understanding of water flow and storage in the trees, or of tree and canopy structure. A more thorough representation of the tree-hydrodynamic processes could potentially remedy this significant source of model error. In a forest plot at the University of Michigan Biological Station, we use measurements of sap flux and leaf water potential to demonstrate that trees of similar type - late successional deciduous trees - have very different hydrodynamic strategies that lead to differences in their temporal patterns of stomatal conductance and thus hysteretic cycles of transpiration. These differences will lead to large differences in conductance and water use based on the species composition of the forest. We also demonstrate that the size and shape of the tree branching system leads to differences in extent of hydrodynamic stress, which may change the forest respiration patterns as the forest grows and ages. We propose a framework to resolve tree hydrodynamics in global and regional models based on the Finite-Elements Tree-Crown Hydrodynamics model (FETCH) -a hydrodynamic model that can resolve the fast

  18. Application of random regression model to estimate genetic parameters for average daily gains in Lori-Bakhtiari sheep breed of Iran.

    PubMed

    Farhangfar, H; Naeemipour, H; Zinvand, B

    2007-07-15

    A random regression model was applied to estimate (co) variances, heritabilities and additive genetic correlations among average daily gains. The data was a total of 10876 records belonging to 1828 lambs (progenies of 123 sires and 743 dams) born between 1995 and 2001 in a single large size flock of Lori-Bakhtiari sheep breed in Iran. In the model, fixed environmental effects of year-season of birth, sex, birth type, age of dam and random effects of direct and maternal additive genetic and permanent environment were included. Orthogonal polynomial regression (on the Legendre scale) of third order (cubic) was utilized to model the genetic and permanent environmental (co) variance structure throughout the growth trajectory. Direct and maternal heritability estimates of average daily gains ranged from 0.011 to 0.131 and 0.008 to 0.181, respectively in which pre-weaning average daily gain (0-3 in months) had the lowest direct and highest maternal heritability estimates among the other age groups. The highest and lowest positive direct additive genetic correlations were found to be 0.993 and 0.118 between ADG (0-9) and ADG (0-12) and between ADG (0-3) and ADG (0-12), respectively. The direct additive genetic correlations between adjacent age groups were more closely than between remote age groups.

  19. Temporal disaggregation of daily precipitation for hydrological modelling under data scarcity conditions by using data from neighboring station as a reference for Chaohu, China.

    NASA Astrophysics Data System (ADS)

    Janabi, Firas Al; Bista, Anukampa; Helm, Bjoern; Krebs, Peter; Bernhofer, Christian

    2017-04-01

    The demand for high-resolution precipitation data at temporal scales fluctuating from daily to hourly or even higher resolution is an enormous problem for hydrological modelling. For many locations around the globe, rainfall data quality and quantity are very poor, and consistent measurements are only available at a coarse time resolution. Models for spatially interpolating hourly precipitation data and temporally disaggregating daily precipitation to hourly data have developed for application to multisite scenarios at Chaohu watershed scale. The specialized tool for rainfall disaggregation, in particular at fine time scales, has been examined in more detail. Disaggregation tool called DiMoN based on multiplicative random cascade model used to disaggregate rain data from Chaohu, China whose meteorological data are scarce. A special disaggregation technique, which, instead of using simultaneously both coarser and finer time scales in one mathematical expression, couples independent stochastic model, at each time scale, have been further analyzed. According to the absence of hourly data at Chaohu station, Data from a station called Luogang, approximately 57 kilometers from Chaohu have been used as a reference station for disaggregation of daily values of Chaohu into hourly and 15 minutes resolution. Correlation between observed and model generated data have been found to be 0.84 and 0.77 for hourly and 15 minutes resolution respectively. NSE (Nash-Sutcliffe Efficiency), RMSE (Root Mean Square Error), and RSR (RMSE-observation standard deviation ratio) shows that the model has generated data within an acceptable range. Improvement in the model performance has been demonstrated by the use of finer resolution of longer time series from Chaohu itself. Keywords: Disaggregation, Nash-Sutcliffe Efficiency, Root Mean Square Error, RSR, Uncertainty

  20. Pharmacodynamics of once-daily amikacin in various combinations with cefepime, aztreonam, and ceftazidime against Pseudomonas aeruginosa in an in vitro infection model.

    PubMed Central

    McGrath, B J; Bailey, E M; Lamp, K C; Rybak, M J

    1992-01-01

    The pharmacodynamics of once-daily amikacin administered as monotherapy and in combination with aztreonam, ceftazidime, and cefepime against Pseudomonas aeruginosa ATCC 27853 and clinical isolate 16690 (moderately susceptible to ceftazidime) were investigated with an in vitro model of infection over a 24-h period. Monotherapy with aztreonam, ceftazidime, and cefepime and combinations of aztreonam with cefepime or ceftazidime were also studied. MICs and MBCs were determined for viable organisms at 24 h to test for the development of resistance. Once-daily amikacin demonstrated killing activity over the initial 8 h superior to those of all other drugs administered as monotherapy against both strains tested (P < 0.01). Regrowth by 24 h was greatest for the amikacin regimen (P < 0.01) but was apparent for all monotherapy regimens against both strains. No changes in susceptibilities were observed. All combination therapies containing once-daily amikacin achieved 99.9% reductions in log10 CFU/ml by 2.0 h against both strains, with no regrowth of organisms at 24 h. Aztreonam-cefepime and -ceftazidime combinations required approximately 5 to 6 h to achieve a 99.9% reduction in log10 CFU/ml. Although there was no difference in time to the 99.9% kill between the aztreonam-cefepime and -ceftazidime regimens against either strain, the killing activity of these combinations was significantly less than those of regimens containing once-daily amikacin (P < 0.01). Minor differences in the initial susceptibilities of beta-lactams and the monobactam aztreonam against P. aeruginosa may not be important for therapeutic outcomes when used in combination with single-daily aminoglycoside therapy. PMID:1482142

  1. Daily precipitation analysis using the CLARIS-LPB models over hydrological basins and climate regions within the CORDEX South America domain

    NASA Astrophysics Data System (ADS)

    Remedio, A. C.; Carril, A. F.; Jacob, D.; Menendez, C.; Pfeifer, S.; Cavalcanti, I. F.; Solman, S.; Da Rocha, R.; Mourao, C. F.; Samuelsson, P.; Sanchez, E.; Li, L.; Berbery, E.; Marengo, J.

    2013-05-01

    Multiyear simulations of the South America daily precipitation are derived by six regional models and a stretched-grid general circulation model within the framework of the EU FP7 CLARIS-LPB Project. The different institution and models which participated in the coordinated project are SMHI RCA, MPI-M REMO, UCLM PROMES, USP RegCM3, CIMA MM5, IPSL LMDZ and INPE ETA. The setup of the different models attempts to follow the WCRP CORDEX protocol closely such as the horizontal resolution of about 50 km and size of the domain, which covers the whole continent of South America. These models have been integrated for the period of 1989-2008 using the ECMWF ERA-Interim reanalysis as initial and lateral boundary conditions. The daily precipitation observational dataset used is from the NOAA Climate Prediction Center (CPC) with a resolution of 0.5 degree. The precipitation estimates from the high resolution TRMM are also used to ascertain observational uncertainties. The skill of the models and ensemble are evaluated using the probabilistic distribution function over twelve regions with different hydrological and climate characteristics. The seven regions representing hydrological basins are South Amazon, Northeast Brazil, South Atlantic Convergence Zone, Paraguay, Upper Parana, Lower Parana and Uruguay. The other five regions have tropical humid, tropical wet-dry, dry semi-arid, subtropical humid and temperate oceanic climate based on the Koeppen-Trewartha classification. The regions are established to evaluate the simulated daily precipitation at areas of similar characteristics. The seasonal means and extreme indices based on percentile approach are also calculated The models have a relatively high skill in simulating the wintertime precipitation at the different regions. During summer, when the precipitation is mainly driven by convective activity, most of the models have relatively low skill. Attempts are made to explain the different biases among each region due to the

  2. Development of a daily mortality probability prediction model from Intensive Care Unit patients using a discrete-time event history analysis.

    PubMed

    Huang, Ying Che; Chang, Kuang Yi; Lin, Shih Pin; Chen, Kung; Chan, Kwok Hon; Chang, Polun

    2013-08-01

    As studies have pointed out, severity scores are imperfect at predicting individual clinical chance of survival. The clinical condition and pathophysiological status of these patients in the Intensive Care Unit might differ from or be more complicated than most predictive models account for. In addition, as the pathophysiological status changes over time, the likelihood of survival day by day will vary. Actually, it would decrease over time and a single prediction value cannot address this truth. Clearly, alternative models and refinements are warranted. In this study, we used discrete-time-event models with the changes of clinical variables, including blood cell counts, to predict daily probability of mortality in individual patients from day 3 to day 28 post Intensive Care Unit admission. Both models we built exhibited good discrimination in the training (overall area under ROC curve: 0.80 and 0.79, respectively) and validation cohorts (overall area under ROC curve: 0.78 and 0.76, respectively) to predict daily ICU mortality. The paper describes the methodology, the development process and the content of the models, and discusses the possibility of them to serve as the foundation of a new bedside advisory or alarm system.

  3. Investigating the impact of land-use land-cover change on Indian summer monsoon daily rainfall and temperature during 1951–2005 using a regional climate model

    SciTech Connect

    Halder, Subhadeep; Saha, Subodh K.; Dirmeyer, Paul A.; Chase, Thomas N.; Goswami, Bhupendra Nath

    2016-05-10

    Daily moderate rainfall events, which constitute a major portion of seasonal summer monsoon rainfall over central India, have decreased significantly during the period 1951 through 2005. On the other hand, mean and extreme near-surface daily temperature during the monsoon season have increased by a maximum of 1–1.5 °C. Using simulations made with a high-resolution regional climate model (RegCM4) and prescribed land cover of years 1950 and 2005, it is demonstrated that part of the changes in moderate rainfall events and temperature have been caused by land-use/land-cover change (LULCC), which is mostly anthropogenic. Model simulations show that the increase in seasonal mean and extreme temperature over central India coincides with the region of decrease in forest and increase in crop cover. Our results also show that LULCC alone causes warming in the extremes of daily mean and maximum temperatures by a maximum of 1–1.2 °C, which is comparable with the observed increasing trend in the extremes. Decrease in forest cover and simultaneous increase in crops not only reduces the evapotranspiration over land and large-scale convective instability, but also contributes toward decrease in moisture convergence through reduced surface roughness. These factors act together in reducing significantly the moderate rainfall events and the amount of rainfall in that category over central India. Additionally, the model simulations are repeated by removing the warming trend in sea surface temperatures over the Indian Ocean. As a result, enhanced warming at the surface and greater decrease in moderate rainfall events over central India compared to the earlier set of simulations are noticed. Results from these additional experiments corroborate our initial findings and confirm the contribution of LULCC in the decrease in moderate rainfall events and increase in daily mean and extreme temperature over India. Therefore, this study demonstrates the important implications of

  4. A New Hybrid Spatio-temporal Model for Estimating Daily Multi-year PM2.5 Concentrations Across Northeastern USA Using High Resolution Aerosol Optical Depth Data

    NASA Technical Reports Server (NTRS)

    Kloog, Itai; Chudnovsky, Alexandra A.; Just, Allan C.; Nordio, Francesco; Koutrakis, Petros; Coull, Brent A.; Lyapustin, Alexei; Wang, Yujie; Schwartz, Joel

    2014-01-01

    The use of satellite-based aerosol optical depth (AOD) to estimate fine particulate matter PM(sub 2.5) for epidemiology studies has increased substantially over the past few years. These recent studies often report moderate predictive power, which can generate downward bias in effect estimates. In addition, AOD measurements have only moderate spatial resolution, and have substantial missing data. We make use of recent advances in MODIS satellite data processing algorithms (Multi-Angle Implementation of Atmospheric Correction (MAIAC), which allow us to use 1 km (versus currently available 10 km) resolution AOD data.We developed and cross validated models to predict daily PM(sub 2.5) at a 1X 1 km resolution across the northeastern USA (New England, New York and New Jersey) for the years 2003-2011, allowing us to better differentiate daily and long term exposure between urban, suburban, and rural areas. Additionally, we developed an approach that allows us to generate daily high-resolution 200 m localized predictions representing deviations from the area 1 X 1 km grid predictions. We used mixed models regressing PM(sub 2.5) measurements against day-specific random intercepts, and fixed and random AOD and temperature slopes. We then use generalized additive mixed models with spatial smoothing to generate grid cell predictions when AOD was missing. Finally, to get 200 m localized predictions, we regressed the residuals from the final model for each monitor against the local spatial and temporal variables at each monitoring site. Our model performance was excellent (mean out-of-sample R(sup 2) = 0.88). The spatial and temporal components of the out-of-sample results also presented very good fits to the withheld data (R(sup 2) = 0.87, R(sup)2 = 0.87). In addition, our results revealed very little bias in the predicted concentrations (Slope of predictions versus withheld observations = 0.99). Our daily model results show high predictive accuracy at high spatial resolutions

  5. A New Hybrid Spatio-Temporal Model For Estimating Daily Multi-Year PM2.5 Concentrations Across Northeastern USA Using High Resolution Aerosol Optical Depth Data

    PubMed Central

    Kloog, Itai; Chudnovsky, Alexandra A.; Just, Allan C.; Nordio, Francesco; Koutrakis, Petros; Coull, Brent A.; Lyapustin, Alexei; Wang, Yujie; Schwartz, Joel

    2017-01-01

    Background The use of satellite-based aerosol optical depth (AOD) to estimate fine particulate matter (PM2.5) for epidemiology studies has increased substantially over the past few years. These recent studies often report moderate predictive power, which can generate downward bias in effect estimates. In addition, AOD measurements have only moderate spatial resolution, and have substantial missing data. Methods We make use of recent advances in MODIS satellite data processing algorithms (Multi-Angle Implementation of Atmospheric Correction (MAIAC), which allow us to use 1 km (versus currently available 10 km) resolution AOD data. We developed and cross validated models to predict daily PM2.5 at a 1×1km resolution across the northeastern USA (New England, New York and New Jersey) for the years 2003–2011, allowing us to better differentiate daily and long term exposure between urban, suburban, and rural areas. Additionally, we developed an approach that allows us to generate daily high-resolution 200 m localized predictions representing deviations from the area 1×1 km grid predictions. We used mixed models regressing PM2.5 measurements against day-specific random intercepts, and fixed and random AOD and temperature slopes. We then use generalized additive mixed models with spatial smoothing to generate grid cell predictions when AOD was missing. Finally, to get 200 m localized predictions, we regressed the residuals from the final model for each monitor against the local spatial and temporal variables at each monitoring site. Results Our model performance was excellent (mean out-of-sample R2=0.88). The spatial and temporal components of the out-of-sample results also presented very good fits to the withheld data (R2=0.87, R2=0.87). In addition, our results revealed very little bias in the predicted concentrations (Slope of predictions versus withheld observations = 0.99). Conclusion Our daily model results show high predictive accuracy at high spatial resolutions

  6. Investigating the impact of land-use land-cover change on Indian summer monsoon daily rainfall and temperature during 1951-2005 using a regional climate model

    NASA Astrophysics Data System (ADS)

    Halder, Subhadeep; Saha, Subodh K.; Dirmeyer, Paul A.; Chase, Thomas N.; Nath Goswami, Bhupendra

    2016-05-01

    Daily moderate rainfall events, which constitute a major portion of seasonal summer monsoon rainfall over central India, have decreased significantly during the period 1951 through 2005. On the other hand, mean and extreme near-surface daily temperature during the monsoon season have increased by a maximum of 1-1.5 °C. Using simulations made with a high-resolution regional climate model (RegCM4) and prescribed land cover of years 1950 and 2005, it is demonstrated that part of the changes in moderate rainfall events and temperature have been caused by land-use/land-cover change (LULCC), which is mostly anthropogenic. Model simulations show that the increase in seasonal mean and extreme temperature over central India coincides with the region of decrease in forest and increase in crop cover. Our results also show that LULCC alone causes warming in the extremes of daily mean and maximum temperatures by a maximum of 1-1.2 °C, which is comparable with the observed increasing trend in the extremes. Decrease in forest cover and simultaneous increase in crops not only reduces the evapotranspiration over land and large-scale convective instability, but also contributes toward decrease in moisture convergence through reduced surface roughness. These factors act together in reducing significantly the moderate rainfall events and the amount of rainfall in that category over central India. Additionally, the model simulations are repeated by removing the warming trend in sea surface temperatures over the Indian Ocean. As a result, enhanced warming at the surface and greater decrease in moderate rainfall events over central India compared to the earlier set of simulations are noticed. Results from these additional experiments corroborate our initial findings and confirm the contribution of LULCC in the decrease in moderate rainfall events and increase in daily mean and extreme temperature over India. Therefore, this study demonstrates the important implications of LULCC over

  7. Investigating the impact of land-use land-cover change on Indian summer monsoon daily rainfall and temperature during 1951–2005 using a regional climate model

    DOE PAGES

    Halder, Subhadeep; Saha, Subodh K.; Dirmeyer, Paul A.; ...

    2016-05-10

    Daily moderate rainfall events, which constitute a major portion of seasonal summer monsoon rainfall over central India, have decreased significantly during the period 1951 through 2005. On the other hand, mean and extreme near-surface daily temperature during the monsoon season have increased by a maximum of 1–1.5 °C. Using simulations made with a high-resolution regional climate model (RegCM4) and prescribed land cover of years 1950 and 2005, it is demonstrated that part of the changes in moderate rainfall events and temperature have been caused by land-use/land-cover change (LULCC), which is mostly anthropogenic. Model simulations show that the increase in seasonal mean and extreme temperature over centralmore » India coincides with the region of decrease in forest and increase in crop cover. Our results also show that LULCC alone causes warming in the extremes of daily mean and maximum temperatures by a maximum of 1–1.2 °C, which is comparable with the observed increasing trend in the extremes. Decrease in forest cover and simultaneous increase in crops not only reduces the evapotranspiration over land and large-scale convective instability, but also contributes toward decrease in moisture convergence through reduced surface roughness. These factors act together in reducing significantly the moderate rainfall events and the amount of rainfall in that category over central India. Additionally, the model simulations are repeated by removing the warming trend in sea surface temperatures over the Indian Ocean. As a result, enhanced warming at the surface and greater decrease in moderate rainfall events over central India compared to the earlier set of simulations are noticed. Results from these additional experiments corroborate our initial findings and confirm the contribution of LULCC in the decrease in moderate rainfall events and increase in daily mean and extreme temperature over India. Therefore, this study demonstrates the important

  8. Daily exercise routines

    NASA Technical Reports Server (NTRS)

    Anderson, Patrick L.; Amoroso, Michael T.

    1990-01-01

    Viewgraphs on daily exercise routines are presented. Topics covered include: daily exercise and periodic stress testings; exercise equipment; physiological monitors; exercise protocols; physiological levels; equipment control; control systems; and fuzzy logic control.

  9. Daily Propranolol Prevents Prolonged Mobilization of Hematopoietic Progenitor Cells in a Rat Model of Lung Contusion, Hemorrhagic Shock, and Chronic Stress

    PubMed Central

    Bible, Letitia E.; Pasupuleti, Latha V.; Gore, Amy V.; Sifri, Ziad C.; Kannan, Kolenkode B.; Mohr, Alicia M.

    2015-01-01

    Introduction Propranolol has been shown previously to decrease the mobilization of hematopoietic progenitor cells (HPC) after acute injury in rodent models; however, this acute injury model does not reflect the prolonged period of critical illness after severe trauma. Using our novel lung contusion/hemorrhagic shock/chronic restraint stress model, we hypothesize that daily propranolol (Prop) administration will decrease prolonged mobilization of HPC without worsening lung healing. Methods Male Sprague-Dawley rats underwent six days of restraint stress after undergoing lung contusion or lung contusion/hemorrhagic shock. Restraint stress consisted of a daily two hour period of restraint interrupted every 30 minutes by alarms and repositioning. Each day after the period of restraint stress, the rats received intraperitoneal propranolol (10mg/kg). On day seven, peripheral blood was analyzed for granulocyte-colony stimulating factor (G-CSF) and stromal cell-derived factor 1 (SDF-1) via ELISA and for mobilization of HPC using c-kit and CD71 flow cytometry. The lungs were examined histologically to grade injury. Results Seven days after lung contusion and lung contusion/hemorrhagic shock, the addition of chronic restraint stress significantly increased the mobilization of HPC, which was associated with persistently increased levels of G-CSF and increased lung injury scores. The addition of propranolol to lung contusion/chronic restraint stress and lung contusion/hemorrhagic shock/chronic restraint stress models significantly decreased HPC mobilization and restored G-CSF levels to that of naïve animals without worsening lung injury scores. Conclusions Daily propranolol administration after both lung contusion and lung contusion/hemorrhagic shock subjected to chronic restraint stress decreased the prolonged mobilization of HPC from the bone marrow and decreased plasma G-CSF levels. Despite the decrease in mobilization of HPC, lung healing did not worsen. Alleviating

  10. Unravelling daily human mobility motifs

    PubMed Central

    Schneider, Christian M.; Belik, Vitaly; Couronné, Thomas; Smoreda, Zbigniew; González, Marta C.

    2013-01-01

    Human mobility is differentiated by time scales. While the mechanism for long time scales has been studied, the underlying mechanism on the daily scale is still unrevealed. Here, we uncover the mechanism responsible for the daily mobility patterns by analysing the temporal and spatial trajectories of thousands of persons as individual networks. Using the concept of motifs from network theory, we find only 17 unique networks are present in daily mobility and they follow simple rules. These networks, called here motifs, are sufficient to capture up to 90 per cent of the population in surveys and mobile phone datasets for different countries. Each individual exhibits a characteristic motif, which seems to be stable over several months. Consequently, daily human mobility can be reproduced by an analytically tractable framework for Markov chains by modelling periods of high-frequency trips followed by periods of lower activity as the key ingredient. PMID:23658117

  11. Evaluating five remote sensing based single-source surface energy balance models for estimating daily evapotranspiration in a humid subtropical climate

    NASA Astrophysics Data System (ADS)

    Bhattarai, Nishan; Shaw, Stephen B.; Quackenbush, Lindi J.; Im, Jungho; Niraula, Rewati

    2016-07-01

    In the last two decades, a number of single-source surface energy balance (SEB) models have been proposed for mapping evapotranspiration (ET); however, there is no clear guidance on which models are preferable under different conditions. In this paper, we tested five models-Surface Energy Balance Algorithm for Land (SEBAL), Mapping ET at high Resolution with Internalized Calibration (METRIC), Simplified Surface Energy Balance Index (S-SEBI), Surface Energy Balance System (SEBS), and operational Simplified Surface Energy Balance (SSEBop)-to identify the single-source SEB models most appropriate for use in the humid southeastern United States. ET predictions from these models were compared with measured ET at four sites (marsh, grass, and citrus surfaces) for 149 cloud-free Landsat image acquisition days between 2000 and 2010. The overall model evaluation statistics showed that SEBS generally outperformed the other models in terms of estimating daily ET from different land covers (e.g., the root mean squared error (RMSE) was 0.74 mm day-1). SSEBop was consistently the worst performing model and overestimated ET at all sites (RMSE = 1.67 mm day-1), while the other models typically fell in between SSEBop and SEBS. However, for short grass conditions, SEBAL, METRIC, and S-SEBI appear to work much better than SEBS. Overall, our study suggests that SEBS may be the best SEB model in humid regions, although it may require modifications to work better over short vegetation.

  12. A mathematical model linking tree sap flow dynamics to daily stem diameter fluctuations and radial stem growth.

    PubMed

    Steppe, Kathy; De Pauw, Dirk J W; Lemeur, Raoul; Vanrolleghem, Peter A

    2006-03-01

    To date, models for simulating sap flow dynamics in individual trees with a direct link to stem diameter variation include only the diameter fluctuation driven by a change in stem water storage. This paper reports results obtained with a comprehensive flow and storage model using whole-tree leaf transpiration as the only input variable. The model includes radial stem growth based on Lockhart's equation for irreversible cell expansion. It was demonstrated that including growth is essential to obtaining good simulation results. To model sap flow dynamics, capacitance of storage tissues was assumed either constant (i.e., electrical analogue approach) or variable and dependent on the water content of the respective storage tissue (i.e., hydraulic system approach). These approaches resulted in different shapes for the desorption curve used to calculate the capacitance of storage tissues. Comparison of these methods allowed detection of specific differences in model simulation of sap flow at the stem base (F(stem)) and stem diameter variation (D). Sensitivity analysis was performed to select a limited subset of identifiable parameters driving most of the variability in model predictions of F(stem) and D Both the electrical analogue and the hydraulic system approach for the flow and storage model were successfully calibrated and validated for the case of a young beech tree (Fagus sylvatica L.). Use of an objective model selection criterion revealed that the flow and storage model based on the electrical analogue approach yielded better predictions.

  13. Chance-constrained overland flow modeling for improving conceptual distributed hydrologic simulations based on scaling representation of sub-daily rainfall variability.

    PubMed

    Han, Jing-Cheng; Huang, Guohe; Huang, Yuefei; Zhang, Hua; Li, Zhong; Chen, Qiuwen

    2015-08-15

    Lack of hydrologic process representation at the short time-scale would lead to inadequate simulations in distributed hydrological modeling. Especially for complex mountainous watersheds, surface runoff simulations are significantly affected by the overland flow generation, which is closely related to the rainfall characteristics at a sub-time step. In this paper, the sub-daily variability of rainfall intensity was considered using a probability distribution, and a chance-constrained overland flow modeling approach was proposed to capture the generation of overland flow within conceptual distributed hydrologic simulations. The integrated modeling procedures were further demonstrated through a watershed of China Three Gorges Reservoir area, leading to an improved SLURP-TGR hydrologic model based on SLURP. Combined with rainfall thresholds determined to distinguish various magnitudes of daily rainfall totals, three levels of significance were simultaneously employed to examine the hydrologic-response simulation. Results showed that SLURP-TGR could enhance the model performance, and the deviation of runoff simulations was effectively controlled. However, rainfall thresholds were so crucial for reflecting the scaling effect of rainfall intensity that optimal levels of significance and rainfall threshold were 0.05 and 10 mm, respectively. As for the Xiangxi River watershed, the main runoff contribution came from interflow of the fast store. Although slight differences of overland flow simulations between SLURP and SLURP-TGR were derived, SLURP-TGR was found to help improve the simulation of peak flows, and would improve the overall modeling efficiency through adjusting runoff component simulations. Consequently, the developed modeling approach favors efficient representation of hydrological processes and would be expected to have a potential for wide applications.

  14. A spatio-temporal statistical model of maximum daily river temperatures to inform the management of Scotland's Atlantic salmon rivers under climate change.

    PubMed

    Jackson, Faye L; Fryer, Robert J; Hannah, David M; Millar, Colin P; Malcolm, Iain A

    2017-09-14

    The thermal suitability of riverine habitats for cold water adapted species may be reduced under climate change. Riparian tree planting is a practical climate change mitigation measure, but it is often unclear where to focus effort for maximum benefit. Recent developments in data collection, monitoring and statistical methods have facilitated the development of increasingly sophisticated river temperature models capable of predicting spatial variability at large scales appropriate to management. In parallel, improvements in temporal river temperature models have increased the accuracy of temperature predictions at individual sites. This study developed a novel large scale spatio-temporal model of maximum daily river temperature (Twmax) for Scotland that predicts variability in both river temperature and climate sensitivity. Twmax was modelled as a linear function of maximum daily air temperature (Tamax), with the slope and intercept allowed to vary as a smooth function of day of the year (DoY) and further modified by landscape covariates including elevation, channel orientation and riparian woodland. Spatial correlation in Twmax was modelled at two scales; (1) river network (2) regional. Temporal correlation was addressed through an autoregressive (AR1) error structure for observations within sites. Additional site level variability was modelled with random effects. The resulting model was used to map (1) spatial variability in predicted Twmax under current (but extreme) climate conditions (2) the sensitivity of rivers to climate variability and (3) the effects of riparian tree planting. These visualisations provide innovative tools for informing fisheries and land-use management under current and future climate. Crown Copyright © 2017. Published by Elsevier B.V. All rights reserved.

  15. Teaching Daily Living Skills to Seven Individuals with Severe Intellectual Disabilities: A Comparison of Video Prompting to Video Modeling

    ERIC Educational Resources Information Center

    Cannella-Malone, Helen I.; Fleming, Courtney; Chung, Yi-Cheih; Wheeler, Geoffrey M.; Basbagill, Abby R.; Singh, Angella H.

    2011-01-01

    We conducted a systematic replication of Cannella-Malone et al. by comparing the effects of video prompting to video modeling for teaching seven students with severe disabilities to do laundry and wash dishes. The video prompting and video modeling procedures were counterbalanced across tasks and participants and compared in an alternating…

  16. Generalized Models for the Classification of Abnormal Movements in Daily Life and its Applicability to Epilepsy Convulsion Recognition.

    PubMed

    Villar, José R; Vergara, Paula; Menéndez, Manuel; de la Cal, Enrique; González, Víctor M; Sedano, Javier

    2016-09-01

    The identification and the modeling of epilepsy convulsions during everyday life using wearable devices would enhance patient anamnesis and monitoring. The psychology of the epilepsy patient penalizes the use of user-driven modeling, which means that the probability of identifying convulsions is driven through generalized models. Focusing on clonic convulsions, this pre-clinical study proposes a method for generating a type of model that can evaluate the generalization capabilities. A realistic experimentation with healthy participants is performed, each with a single 3D accelerometer placed on the most affected wrist. Unlike similar studies reported in the literature, this proposal makes use of [Formula: see text] cross-validation scheme, in order to evaluate the generalization capabilities of the models. Event-based error measurements are proposed instead of classification-error measurements, to evaluate the generalization capabilities of the model, and Fuzzy Systems are proposed as the generalization modeling technique. Using this method, the experimentation compares the most common solutions in the literature, such as Support Vector Machines, [Formula: see text]-Nearest Neighbors, Decision Trees and Fuzzy Systems. The event-based error measurement system records the results, penalizing those models that raise false alarms. The results showed the good generalization capabilities of Fuzzy Systems.

  17. Development of nonlinear empirical models to forecast daily PM2.5 and ozone levels in three large Chinese cities

    NASA Astrophysics Data System (ADS)

    Lv, Baolei; Cobourn, W. Geoffrey; Bai, Yuqi

    2016-12-01

    Empirical regression models for next-day forecasting of PM2.5 and O3 air pollution concentrations have been developed and evaluated for three large Chinese cities, Beijing, Nanjing and Guangzhou. The forecast models are empirical nonlinear regression models designed for use in an automated data retrieval and forecasting platform. The PM2.5 model includes an upwind air quality variable, PM24, to account for regional transport of PM2.5, and a persistence variable (previous day PM2.5 concentration). The models were evaluated in the hindcast mode with a two-year air quality and meteorological data set using a leave-one-month-out cross validation method, and in the forecast mode with a one-year air quality and forecasted weather dataset that included forecasted air trajectories. The PM2.5 models performed well in the hindcast mode, with coefficient of determination (R2) values of 0.54, 0.65 and 0.64, and normalized mean error (NME) values of 0.40, 0.26 and 0.23 respectively, for the three cities. The O3 models also performed well in the hindcast mode, with R2 values of 0.75, 0.55 and 0.73, and NME values of 0.29, 0.26 and 0.24 in the three cities. The O3 models performed better in summertime than in winter in Beijing and Guangzhou, and captured the O3 variations well all the year round in Nanjing. The overall forecast performance of the PM2.5 and O3 models during the test year varied from fair to good, depending on location. The forecasts were somewhat degraded compared with hindcasts from the same year, depending on the accuracy of the forecasted meteorological input data. For the O3 models, the model forecast accuracy was strongly dependent on the maximum temperature forecasts. For the critical forecasts, involving air quality standard exceedences, the PM2.5 model forecasts were fair to good, and the O3 model forecasts were poor to fair.

  18. Developing a model for understanding patient collection of observations of daily living: A qualitative meta-synthesis of the Project HealthDesign Program

    PubMed Central

    Cohen, Deborah J.; Keller, Sara R.; Hayes, Gillian R.; Dorr, David A.; Ash, Joan S.; Sittig, Dean F.

    2016-01-01

    We conducted a meta-synthesis of five different studies that developed, tested, and implemented new technologies for the purpose of collecting Observations of Daily Living (ODL). From this synthesis, we developed a model to explain user motivation as it relates to ODL collection. We describe this model that includes six factors that motivate patients’ collection of ODL data: usability, illness experience, relevance of ODLs, information technology infrastructure, degree of burden, and emotional activation. We show how these factors can act as barriers or facilitators to the collection of ODL data and how interacting with care professionals and sharing ODL data may also influence ODL collection, health-related awareness, and behavior change. The model we developed and used to explain ODL collection can be helpful to researchers and designers who study and develop new, personal health technologies to empower people to improve their health. PMID:26949381

  19. Developing a model for understanding patient collection of observations of daily living: A qualitative meta-synthesis of the Project HealthDesign Program.

    PubMed

    Cohen, Deborah J; Keller, Sara R; Hayes, Gillian R; Dorr, David A; Ash, Joan S; Sittig, Dean F

    2015-01-01

    We conducted a meta-synthesis of five different studies that developed, tested, and implemented new technologies for the purpose of collecting Observations of Daily Living (ODL). From this synthesis, we developed a model to explain user motivation as it relates to ODL collection. We describe this model that includes six factors that motivate patients' collection of ODL data: usability, illness experience, relevance of ODLs, information technology infrastructure, degree of burden, and emotional activation. We show how these factors can act as barriers or facilitators to the collection of ODL data and how interacting with care professionals and sharing ODL data may also influence ODL collection, health-related awareness, and behavior change. The model we developed and used to explain ODL collection can be helpful to researchers and designers who study and develop new, personal health technologies to empower people to improve their health.

  20. Daily modelling of total electron content with ionosonde observation to the benefit of the open source users

    NASA Astrophysics Data System (ADS)

    Tamara, Gulyaeva; Poustovalova, Ljubov; Stanislawska, Iwona

    It is clearly beneficial if the open source databases and models are available free for the users. However a newcomer needs a guidance concerning reliability/consistency of a suitable model and database. Such guidance is provided by the International Standardization Organization which has published Technical Specification, ISO TS16457 on "The Earth's ionosphere model: international reference ionosphere (IRI) model and extensions to the plasmasphere", 2009. One of the ISO TS16457 options is IRI-IZMIRAN ionosphere-plasmasphere model. This model is applied currently to generate vertical total electron content, TEC, at the altitude range [80:20,000] km at (http://www.izmiran.ru/services/iweather/). This approach uses solar activ-ity and geomagnetic activity driving the ionosphere-plasmasphere model, and the integration of electron density profile fitted to ionosonde observation of the F2 layer peak density and height. The paper provides validation of proposed technique and comparisons with GPS-TEC observations and topside ionospheric electron density extrapolation technique providing the ionospheric TEC.

  1. Investigating the impact of land-use land-cover change on Indian summer monsoon daily rainfall and temperature during 1951-2005 using a regional climate model

    NASA Astrophysics Data System (ADS)

    Halder, S.; Saha, S. K.; Dirmeyer, P. A.; Chase, T. N.; Goswami, B. N.

    2015-07-01

    Daily moderate rainfall events, that constitute a major portion of seasonal summer monsoon rainfall over central India, have decreased significantly during the period 1951 till 2005. Mean and extreme near surface daily temperature during the monsoon season have also increased by a maximum of 1-1.5 °C. Using simulations made with a high-resolution regional climate model (RegCM4) with prescribed vegetation cover of 1950 and 2005, it is demonstrated that part of the above observed changes in moderate rainfall events and temperature have been caused by land-use land-cover change (LULCC) which is mostly anthropogenic. Model simulations show that the increase in seasonal mean and extreme temperature over central India coincides with the region of decreased (increased) forest (crop) cover. The results also show that land-use land-cover alone causes warming in the extremes of daily mean and maximum temperatures by maximum of 1-1.2 °C, that is comparable with the observed increasing trend in the extremes. Decrease (increase) in forest (crop) cover reduces the evapotranspiration over land and large-scale convective instability, apart from decreasing the moisture convergence. These factors act together not only in reducing the moderate rainfall events over central India but also the amount of rainfall in that category, significantly. This is the most interesting result of this study. Additionally, the model simulations are repeated by removing the warming trend in sea surface temperatures. As a result, there is enhanced warming at the surface and decrease in moderate rainfall events over central India. Results from the additional experiments corroborate our initial findings and confirm the contribution of land-use land-cover change on increase in daily mean and extreme temperature and decrease in moderate rainfall events. This study not only demonstrates the important implications of LULCC over India, but also shows the necessity for inclusion of projected anthropogenic

  2. Selecting the optimal method to calculate daily global reference potential evaporation from CFSR reanalysis data for application in a hydrological model study

    NASA Astrophysics Data System (ADS)

    Sperna Weiland, F. C.; Tisseuil, C.; Dürr, H. H.; Vrac, M.; van Beek, L. P. H.

    2012-03-01

    Potential evaporation (PET) is one of the main inputs of hydrological models. Yet, there is limited consensus on which PET equation is most applicable in hydrological climate impact assessments. In this study six different methods to derive global scale reference PET daily time series from Climate Forecast System Reanalysis (CFSR) data are compared: Penman-Monteith, Priestley-Taylor and original and re-calibrated versions of the Hargreaves and Blaney-Criddle method. The calculated PET time series are (1) evaluated against global monthly Penman-Monteith PET time series calculated from CRU data and (2) tested on their usability for modeling of global discharge cycles. A major finding is that for part of the investigated basins the selection of a PET method may have only a minor influence on the resulting river flow. Within the hydrological model used in this study the bias related to the PET method tends to decrease while going from PET, AET and runoff to discharge calculations. However, the performance of individual PET methods appears to be spatially variable, which stresses the necessity to select the most accurate and spatially stable PET method. The lowest root mean squared differences and the least significant deviations (95% significance level) between monthly CFSR derived PET time series and CRU derived PET were obtained for a cell-specific re-calibrated Blaney-Criddle equation. However, results show that this re-calibrated form is likely to be unstable under changing climate conditions and less reliable for the calculation of daily time series. Although often recommended, the Penman-Monteith equation applied to the CFSR data did not outperform the other methods in a evaluation against PET derived with the Penman-Monteith equation from CRU data. In arid regions (e.g. Sahara, central Australia, US deserts), the equation resulted in relatively low PET values and, consequently, led to relatively high discharge values for dry basins (e.g. Orange, Murray and

  3. Projected increases in summer and winter UK sub-daily precipitation extremes from high-resolution regional climate models

    NASA Astrophysics Data System (ADS)

    Chan, S. C.; Kendon, E. J.; Fowler, H. J.; Blenkinsop, S.; Roberts, N. M.

    2014-08-01

    Summer (June-July-August JJA) UK precipitation extremes projections from two UK Met Office high-resolution (12 km and 1.5 km) regional climate models (RCMs) are shown to be resolution dependent. The 1.5 km RCM projects a uniform (\\approx 10%) increase in 1 h JJA precipitation intensities across a range of return periods. The 12 km RCM, in contrast, projects decreases in short return period (≦̸5 years) events but strong increases in long return period (⩾20 years) events. We have low physical and statistical confidence in the 12 km RCM projections for longer return periods. Both models show evidence for longer dry periods between events. In winter (December-January-February DJF), the models show larger return level increases (⩾40%). Both DJF projections are consistent with results from previous work based on coarser resolution models.

  4. A simple daily soil water balance model for estimating the spatial and temporal distribution of groundwater recharge in temperate humid areas

    NASA Astrophysics Data System (ADS)

    Dripps, W. R.; Bradbury, K. R.

    2007-05-01

    Quantifying the spatial and temporal distribution of natural groundwater recharge is usually a prerequisite for effective groundwater modeling and management. As flow models become increasingly utilized for management decisions, there is an increased need for simple, practical methods to delineate recharge zones and quantify recharge rates. Existing models for estimating recharge distributions are data intensive, require extensive parameterization, and take a significant investment of time in order to establish. The Wisconsin Geological and Natural History Survey (WGNHS) has developed a simple daily soil water balance (SWB) model that uses readily available soil, land cover, topographic, and climatic data in conjunction with a geographic information system (GIS) to estimate the temporal and spatial distribution of groundwater recharge at the watershed scale for temperate humid areas. To demonstrate the methodology and the applicability and performance of the model, two case studies are presented: one for the forested Trout Lake watershed of north central Wisconsin, USA and the other for the urban-agricultural Pheasant Branch Creek watershed of south central Wisconsin, USA. Overall, the SWB model performs well and presents modelers and planners with a practical tool for providing recharge estimates for modeling and water resource planning purposes in humid areas.

  5. A simple daily soil-water balance model for estimating the spatial and temporal distribution of groundwater recharge in temperate humid areas

    USGS Publications Warehouse

    Dripps, W.R.; Bradbury, K.R.

    2007-01-01

    Quantifying the spatial and temporal distribution of natural groundwater recharge is usually a prerequisite for effective groundwater modeling and management. As flow models become increasingly utilized for management decisions, there is an increased need for simple, practical methods to delineate recharge zones and quantify recharge rates. Existing models for estimating recharge distributions are data intensive, require extensive parameterization, and take a significant investment of time in order to establish. The Wisconsin Geological and Natural History Survey (WGNHS) has developed a simple daily soil-water balance (SWB) model that uses readily available soil, land cover, topographic, and climatic data in conjunction with a geographic information system (GIS) to estimate the temporal and spatial distribution of groundwater recharge at the watershed scale for temperate humid areas. To demonstrate the methodology and the applicability and performance of the model, two case studies are presented: one for the forested Trout Lake watershed of north central Wisconsin, USA and the other for the urban-agricultural Pheasant Branch Creek watershed of south central Wisconsin, USA. Overall, the SWB model performs well and presents modelers and planners with a practical tool for providing recharge estimates for modeling and water resource planning purposes in humid areas. ?? Springer-Verlag 2007.

  6. Integrating Map Algebra and Statistical Modeling for Spatio- Temporal Analysis of Monthly Mean Daily Incident Photosynthetically Active Radiation (PAR) over a Complex Terrain.

    PubMed

    Evrendilek, Fatih

    2007-12-12

    This study aims at quantifying spatio-temporal dynamics of monthly mean dailyincident photosynthetically active radiation (PAR) over a vast and complex terrain such asTurkey. The spatial interpolation method of universal kriging, and the combination ofmultiple linear regression (MLR) models and map algebra techniques were implemented togenerate surface maps of PAR with a grid resolution of 500 x 500 m as a function of fivegeographical and 14 climatic variables. Performance of the geostatistical and MLR modelswas compared using mean prediction error (MPE), root-mean-square prediction error(RMSPE), average standard prediction error (ASE), mean standardized prediction error(MSPE), root-mean-square standardized prediction error (RMSSPE), and adjustedcoefficient of determination (R²adj.). The best-fit MLR- and universal kriging-generatedmodels of monthly mean daily PAR were validated against an independent 37-year observeddataset of 35 climate stations derived from 160 stations across Turkey by the Jackknifingmethod. The spatial variability patterns of monthly mean daily incident PAR were moreaccurately reflected in the surface maps created by the MLR-based models than in thosecreated by the universal kriging method, in particular, for spring (May) and autumn(November). The MLR-based spatial interpolation algorithms of PAR described in thisstudy indicated the significance of the multifactor approach to understanding and mappingspatio-temporal dynamics of PAR for a complex terrain over meso-scales.

  7. The acute effects of daily nicotine intake on heart rate--a toxicokinetic and toxicodynamic modelling study.

    PubMed

    Gajewska, M; Worth, A; Urani, C; Briesen, H; Schramm, K-W

    2014-10-01

    Joint physiologically-based toxicokinetic and toxicodynamic (PBTK/TD) modelling was applied to simulate concentration-time profiles of nicotine, a well-known stimulant, in the human body following single and repeated dosing. Both kinetic and dynamic models were first calibrated by using in vivo literature data for the Caucasian population. The models were then used to estimate the blood and liver concentrations of nicotine in terms of the Area Under Curve (AUC) and the peak concentration (Cmax) for selected exposure scenarios based on inhalation (cigarette smoking), oral intake (nicotine lozenges) and dermal absorption (nicotine patches). The model simulations indicated that whereas frequent cigarette smoking gives rise to high AUC and Cmax in blood, the use of nicotine-rich dermal patches leads to high AUC and Cmax in the liver. Venous blood concentrations were used to estimate one of the most common acute effects, mean heart rate, both at rest and during exercise. These estimations showed that cigarette smoking causes a high peak heart rate, whereas dermal absorption causes a high mean heart rate over 48h. This study illustrates the potential of using PBTK/TD modelling in the safety assessment of nicotine-containing products.

  8. A thermal-based remote sensing modeling system for estimating daily evapotranspiration from field to global scales

    USDA-ARS?s Scientific Manuscript database

    Thermal-infrared (TIR) remote sensing of land surface temperature (LST) provides valuable information for quantifying root-zone water availability, evapotranspiration (ET) and crop condition as well as providing useful information for constraining prognostic land surface models. This presentation d...

  9. A Daily Diary Study of Coping in the Context of the Job Demands-Control-Support Model

    ERIC Educational Resources Information Center

    Daniels, Kevin; Harris, Claire

    2005-01-01

    We examined one of the processes thought to underpin Karasek and Theorell's job demands-control-support model (1990). This is that control and support accentuate better well-being by fostering problem-focused coping with work demands. We also examined whether other forms of coping implemented through control and support are related to indicators…

  10. MRO SOW Daily Script

    NASA Technical Reports Server (NTRS)

    Fisher, Forest E.; Khanampornpan, Teerapat; Gladden, Roy E.

    2008-01-01

    The MRO SOW daily script (wherein "MRO" signifies "Mars Reconnaissance Orbiter" and "SOW" signifies "sequence systems engineer of the week") is a computer program that automates portions of the MRO daily SOW procedure, which includes checking file-system sizes and automated sequence processor (ASP) log files. The MRO SOW daily script effects clear reporting of (1) the status of, and requirements imposed on, the file system and (2) the ASP log files.

  11. Pharmacological doses of daily ascorbate protect tumors from radiation damage after a single dose of radiation in an intracranial mouse glioma model.

    PubMed

    Grasso, Carole; Fabre, Marie-Sophie; Collis, Sarah V; Castro, M Leticia; Field, Cameron S; Schleich, Nanette; McConnell, Melanie J; Herst, Patries M

    2014-01-01

    Pharmacological ascorbate is currently used as an anti-cancer treatment, potentially in combination with radiation therapy, by integrative medicine practitioners. In the acidic, metal-rich tumor environment, ascorbate acts as a pro-oxidant, with a mode of action similar to that of ionizing radiation; both treatments kill cells predominantly by free radical-mediated DNA damage. The brain tumor, glioblastoma multiforme (GBM), is very resistant to radiation; radiosensitizing GBM cells will improve survival of GBM patients. Here, we demonstrate that a single fraction (6 Gy) of radiation combined with a 1 h exposure to ascorbate (5 mM) sensitized murine glioma GL261 cells to radiation in survival and colony-forming assays in vitro. In addition, we report the effect of a single fraction (4.5 Gy) of whole brain radiation combined with daily intraperitoneal injections of ascorbate (1 mg/kg) in an intracranial GL261 glioma mouse model. Tumor-bearing C57BL/6 mice were divided into four groups: one group received a single dose of 4.5 Gy to the brain 8 days after tumor implantation, a second group received daily intraperitoneal injections of ascorbate (day 8-45) after implantation, a third group received both treatments and a fourth control group received no treatment. While radiation delayed tumor progression, intraperitoneal ascorbate alone had no effect on tumor progression. Tumor progression was faster in tumor-bearing mice treated with radiation and daily ascorbate than in those treated with radiation alone. Histological analysis showed less necrosis in tumors treated with both radiation and ascorbate, consistent with a radio-protective effect of ascorbate in vivo. Discrepancies between our in vitro and in vivo results may be explained by differences in the tumor microenvironment, which determines whether ascorbate remains outside the cell, acting as a pro-oxidant, or whether it enters the cells and acts as an anti-oxidant.

  12. Impacts of cloud superparameterization on projected daily rainfall intensity climate changes in multiple versions of the Community Earth System Model

    DOE PAGES

    Kooperman, Gabriel J.; Pritchard, Michael S.; Burt, Melissa A.; ...

    2016-09-26

    Changes in the character of rainfall are assessed using a holistic set of statistics based on rainfall frequency and amount distributions in climate change experiments with three conventional and superparameterized versions of the Community Atmosphere Model (CAM and SPCAM). Previous work has shown that high-order statistics of present-day rainfall intensity are significantly improved with superparameterization, especially in regions of tropical convection. Globally, the two modeling approaches project a similar future increase in mean rainfall, especially across the Inter-Tropical Convergence Zone (ITCZ) and at high latitudes, but over land, SPCAM predicts a smaller mean change than CAM. Changes in high-order statisticsmore » are similar at high latitudes in the two models but diverge at lower latitudes. In the tropics, SPCAM projects a large intensification of moderate and extreme rain rates in regions of organized convection associated with the Madden Julian Oscillation, ITCZ, monsoons, and tropical waves. In contrast, this signal is missing in all versions of CAM, which are found to be prone to predicting increases in the amount but not intensity of moderate rates. Predictions from SPCAM exhibit a scale-insensitive behavior with little dependence on horizontal resolution for extreme rates, while lower resolution (~2°) versions of CAM are not able to capture the response simulated with higher resolution (~1°). Furthermore, moderate rain rates analyzed by the “amount mode” and “amount median” are found to be especially telling as a diagnostic for evaluating climate model performance and tracing future changes in rainfall statistics to tropical wave modes in SPCAM.« less

  13. Subseasonal to multidecadal variability of northeast monsoon daily rainfall over Peninsular Malaysia using a hidden Markov model

    NASA Astrophysics Data System (ADS)

    Tan, Wei Lun; Yusof, Fadhilah; Yusop, Zulkifli

    2017-07-01

    This study involves the modelling of a homogeneous hidden Markov model (HMM) on the northeast rainfall monsoon using 40 rainfall stations in Peninsular Malaysia for the period of 1975 to 2008. A six hidden states HMM was selected based on Bayesian information criterion (BIC), and every hidden state has distinct rainfall characteristics. Three of the states were found to correspond by wet conditions; while the remaining three states were found to correspond to dry conditions. The six hidden states were found to correspond with the associated atmospheric composites. The relationships between El Niño-Southern Oscillation (ENSO) and the sea surface temperatures (SST) in the Pacific Ocean are found regarding interannual variability. The wet (dry) states were found to be well correlated with a Niño 3.4 index which was used to characterize the intensity of an ENSO event. This model is able to assess the behaviour of the rainfall characteristics with the large scale atmospheric circulation; the monsoon rainfall is well correlated with the El Niño-Southern Oscillation in Peninsular Malaysia.

  14. Subseasonal to multidecadal variability of northeast monsoon daily rainfall over Peninsular Malaysia using a hidden Markov model

    NASA Astrophysics Data System (ADS)

    Tan, Wei Lun; Yusof, Fadhilah; Yusop, Zulkifli

    2016-04-01

    This study involves the modelling of a homogeneous hidden Markov model (HMM) on the northeast rainfall monsoon using 40 rainfall stations in Peninsular Malaysia for the period of 1975 to 2008. A six hidden states HMM was selected based on Bayesian information criterion (BIC), and every hidden state has distinct rainfall characteristics. Three of the states were found to correspond by wet conditions; while the remaining three states were found to correspond to dry conditions. The six hidden states were found to correspond with the associated atmospheric composites. The relationships between El Niño-Southern Oscillation (ENSO) and the sea surface temperatures (SST) in the Pacific Ocean are found regarding interannual variability. The wet (dry) states were found to be well correlated with a Niño 3.4 index which was used to characterize the intensity of an ENSO event. This model is able to assess the behaviour of the rainfall characteristics with the large scale atmospheric circulation; the monsoon rainfall is well correlated with the El Niño-Southern Oscillation in Peninsular Malaysia.

  15. Daily propranolol prevents prolonged mobilization of hematopoietic progenitor cells in a rat model of lung contusion, hemorrhagic shock, and chronic stress.

    PubMed

    Bible, Letitia E; Pasupuleti, Latha V; Gore, Amy V; Sifri, Ziad C; Kannan, Kolenkode B; Mohr, Alicia M

    2015-09-01

    Propranolol has been shown previously to decrease the mobilization of hematopoietic progenitor cells (HPCs) after acute injury in rodent models; however, this acute injury model does not reflect the prolonged period of critical illness after severe trauma. Using our novel lung contusion/hemorrhagic shock/chronic restraint stress model, we hypothesize that daily administration of propranolol will decrease prolonged mobilization of HPCs without worsening lung healing. Male Sprague-Dawley rats underwent 6 days of restraint stress after undergoing lung contusion or lung contusion/hemorrhagic shock. Restraint stress consisted of a daily 2-hour period of restraint interrupted every 30 minutes by alarms and repositioning. Each day after the period of restraint stress, the rats received intraperitoneal propranolol (10 mg/kg). On day 7, peripheral blood was analyzed for granulocyte-colony stimulating factor (G-CSF) and stromal cell-derived factor 1 via enzyme-linked immunosorbent assay and for mobilization of HPCs using c-kit and CD71 flow cytometry. The lungs were examined histologically to grade injury. Seven days after lung contusion and lung contusion/hemorrhagic shock, the addition of chronic restraint stress significantly increased the mobilization of HPC, which was associated with persistently increased levels of G-CSF and increased lung injury scores. The addition of propranolol to lung contusion/chronic restraint stress and lung contusion/hemorrhagic shock/chronic restraint stress models greatly decreased HPC mobilization and restored G-CSF levels to that of naïve animals without worsening lung injury scores. The daily administration of propranolol after both lung contusion and lung contusion/hemorrhagic shock subjected to chronic restraint stress decreased the prolonged mobilization of HPC from the bone marrow and decreased plasma G-CSF levels. Despite the decrease in mobilization of HPC, lung healing did not worsen. Alleviating chronic stress with propranolol

  16. Relationships Among Nightly Sleep Quality, Daily Stress, and Daily Affect.

    PubMed

    Blaxton, Jessica M; Bergeman, Cindy S; Whitehead, Brenda R; Braun, Marcia E; Payne, Jessic D

    2017-05-01

    We explored the prospective, microlevel relationship between nightly sleep quality (SQ) and the subsequent day's stress on positive (PA) and negative affect (NA) as well as the moderating relationships between nightly SQ, subsequent stress, and subsequent PA on NA. We investigated whether age moderated these relationships. We collected 56 days of sleep, stress, and affect data using daily diary questionnaires (N = 552). We used multilevel modeling to assess relationships at the between- and within-person levels. Daily increases in SQ and decreases in stress interacted to predict higher daily PA and lower daily NA. Better SQ in older adults enhanced the benefits of PA on the stress-NA relationship more during times of low stress, whereas better sleep in younger adults enhanced the benefits of PA more during times of high stress. Between-person effects were stronger predictors of well-being outcomes than within-person variability. The combination of good SQ and higher PA buffered the impact of stress on NA. The moderating impact of age suggests that sleep and stress play different roles across adulthood. Targeting intervention and prevention strategies to improve SQ and enhance PA could disrupt the detrimental relationship between daily stress and NA.

  17. Daily Coffee Intake Inhibits Pancreatic Beta Cell Damage and Nonalcoholic Steatohepatitis in a Mouse Model of Spontaneous Metabolic Syndrome, Tsumura-Suzuki Obese Diabetic Mice.

    PubMed

    Watanabe, Syunsuke; Takahashi, Tetsuyuki; Ogawa, Hirohisa; Uehara, Hisanori; Tsunematsu, Takaaki; Baba, Hayato; Morimoto, Yuki; Tsuneyama, Koichi

    2017-05-01

    Metabolic syndrome is one of the most important health issues worldwide. Obesity causes insulin resistance, hyperlipidemia, diabetes, and various diseases throughout the body. The liver phenotype, which is called nonalcoholic steatohepatitis (NASH), frequently progresses to hepatocellular carcinoma. We recently established a new animal model, Tsumura-Suzuki obese diabetic (TSOD) mice, which spontaneously exhibit obesity, diabetes, hyperlipidemia, and NASH with liver nodules. We examined the effects of coffee intake on various conditions of the metabolic syndrome using TSOD mice. The daily volume of coffee administered was limited so that it reflected the appropriate quantities consumed in humans. To clarify the effects of the specific components, animals were divided into two coffee-intake groups that included with and without caffeine. Coffee intake did not significantly affect obesity and hyperlipidemia in TSOD mice. In contrast, coffee intake caused various degrees of improvement in the pancreatic beta cell damage and steatohepatitis with liver carcinogenesis. Most of the effects were believed to be caused by a synergistic effect of caffeine with other components such as polyphenols. However, the antifibrotic effects of coffee appeared to be due to the polyphenols rather than the caffeine. A daily habit of drinking coffee could possibly play a role in the prevention of metabolic syndrome.

  18. Toward the development of an inventory of daily widowed life (IDWL): guided by the dual process model of coping with bereavement.

    PubMed

    Caserta, Michael S; Lund, Dale A

    2007-07-01

    The Dual Process Model of Coping with Bereavement (M. Stroebe & H. Schut, 1999) suggests that the most effective adaptation involves oscillation between 2 coping processes: loss-orientation (LO) and restoration-orientation (RO). A 22-item Inventory of Daily Widowed Life (IDWL) was developed to measure these processes and the oscillation between them and assessed by examining 163 bereaved widow(er)s, ages 45-94 years. The LO and RO subscales produced alpha coefficients of .90 and .79, respectively. The more recently widowed demonstrated a high degree of oscillation balance between the two processes, while there was a greater emphasis on restoration-orientation among those bereaved longer. Both subscales generated significant relationships with the bereavement outcome measures used in this study. Furthermore, restoration-orientation was directly related to the level of self-care and daily living skills as well as personal growth. We identify six dimensions of oscillation that warrant further consideration and encourage others to help develop and refine all features of the IDWL and make it adaptable to other loss relationships.

  19. Repeat topography surveys of geomorphic changes using digital surface models deriving from Formosat-2 daily revisit stereo pair with very narrow baseline

    NASA Astrophysics Data System (ADS)

    Liu, C.; Wen, H.; Liu, J.; Ko, M.; Yan, H.; Chang, L.

    2012-12-01

    translative offset between two similar images to be rapidly estimated. To meet the requirements in remote sensing and biomedical imaging, the technology of phase correlation has been extended to the sub-pixel level. Liu and Yan (2008) developed a robust phase correlation model using the based feature matching for image co-registration and DEM generation. Considering the fact that the Formosat-2 consecutive images are intrinsically stereo pairs with very narrow baselines, this innovative stereo-matching algorithm based on SPPC technique is employed to process Formosat-2 daily revisit stereo pairs with very narrow baselines. The detailed accuracy and efficiency analysis is investigated for the study area, Namasha, Kaohsiung, using the 50cm resolution aerial photo and the 2m resolution DEM derived from airborne LiDAR data. The archive of Formosat-2 images in Taiwan area collected from 2005 to 2012 was screened out, with the intention to select the consecutive pairs of those areas where major slope disasters occurred in the past eight years. This research encourages the repeated topography surveys of geomorphic changes using digital surface models deriving from Formosat-2 daily revisit stereo pair with very narrow baseline.

  20. Why Are Children Different in Their Daily Sedentariness? An Approach Based on the Mixed-Effects Location Scale Model.

    PubMed

    Gomes, Thayse Natacha; Hedeker, Donald; dos Santos, Fernanda Karina; Pereira, Sara; Katzmarzyk, Peter T; Maia, José A R

    2015-01-01

    This study aimed to investigate the between- and within-individual variability in sedentary time over seven days, using a mixed-effects location scale model. The sample comprised 686 Portuguese children (381 girls) aged 9-11 years, from 23 schools. Sedentary time was estimated by the Actigraph GT3X+ accelerometer, which was used 24 hours/day for 7 consecutive days; height, sitting height, and weight were measured, BMI was computed (WHO cut-points were used to classify subjects as normal weight or overweight/obese), and maturity offset was estimated. Information regarding the home environment was obtained by questionnaire. Results revealed that (i) children were more sedentary on Friday, but less so on Saturday and Sunday (compared to Monday), with significant variation between- and within-subjects (between-subject variance=0.800, within-subject variance=1.793, intra-subject correlation=0.308); (ii) there is a sex effect on sedentariness, with boys being less sedentary than girls (p<0.001), and the between-subject variance was 1.48 times larger for boys than girls; (iii) in terms of the within-subject variance, or erraticism, Tuesday, Wednesday and Friday have similar erraticism levels as Monday (Thursday has less, while Saturday and Sunday have more); in addition, girls (variance ratio=0.632, p<0.001), overweight/obese children (variance ratio=0.861, p=0.019), and those later mature (variance ratio=0.849, p=0.013) have less erraticism than their counterparts; (iv) the within-subject variance varied significantly across subjects (scale std dev=0.342±0.037, p<0.001); and (v) in the fixed part of the model, only biological maturation was positively related to sedentariness. This study demonstrated that there is significant between- and within-subject variability in sedentariness across a whole week. This implies that a focus on intra-individual variability, instead of only on mean values, would provide relevant information towards a more complete map of children

  1. Why Are Children Different in Their Daily Sedentariness? An Approach Based on the Mixed-Effects Location Scale Model

    PubMed Central

    Gomes, Thayse Natacha; Hedeker, Donald; dos Santos, Fernanda Karina; Pereira, Sara; Katzmarzyk, Peter T.; Maia, José A. R.

    2015-01-01

    This study aimed to investigate the between- and within-individual variability in sedentary time over seven days, using a mixed-effects location scale model. The sample comprised 686 Portuguese children (381 girls) aged 9–11 years, from 23 schools. Sedentary time was estimated by the Actigraph GT3X+ accelerometer, which was used 24 hours/day for 7 consecutive days; height, sitting height, and weight were measured, BMI was computed (WHO cut-points were used to classify subjects as normal weight or overweight/obese), and maturity offset was estimated. Information regarding the home environment was obtained by questionnaire. Results revealed that (i) children were more sedentary on Friday, but less so on Saturday and Sunday (compared to Monday), with significant variation between- and within-subjects (between-subject variance=0.800, within-subject variance=1.793, intra-subject correlation=0.308); (ii) there is a sex effect on sedentariness, with boys being less sedentary than girls (p<0.001), and the between-subject variance was 1.48 times larger for boys than girls; (iii) in terms of the within-subject variance, or erraticism, Tuesday, Wednesday and Friday have similar erraticism levels as Monday (Thursday has less, while Saturday and Sunday have more); in addition, girls (variance ratio=0.632, p<0.001), overweight/obese children (variance ratio=0.861, p=0.019), and those later mature (variance ratio=0.849, p=0.013) have less erraticism than their counterparts; (iv) the within-subject variance varied significantly across subjects (scale std dev=0.342±0.037, p<0.001); and (v) in the fixed part of the model, only biological maturation was positively related to sedentariness. This study demonstrated that there is significant between- and within-subject variability in sedentariness across a whole week. This implies that a focus on intra-individual variability, instead of only on mean values, would provide relevant information towards a more complete map of children

  2. Daily ingestion of the probiotic Lactobacillus paracasei ST11 decreases Vaccinia virus dissemination and lethality in a mouse model.

    PubMed

    Dos Santos Pereira Andrade, A C; Lima, M Teixeira; Oliveira, G Pereira; Calixto, R Silva; de Sales E Souza, É Lorenna; da Glória de Souza, D; de Almeida Leite, C M; Ferreira, J M Siqueira; Kroon, E G; de Oliveira, D Bretas; Dos Santos Martins, F; Abrahão, J S

    2017-02-07

    Vaccinia virus (VACV) is an important pathogen. Although studies have shown relationships between probiotics and viruses, the effect of probiotics on VACV infection is unknown. Therefore, this work aims to investigate the probiotics effects on VACV infection. Mice were divided into four groups, two non-infected groups, one receiving the probiotic, the other one not receiving it, and two groups infected intranasally with VACV Western Reserve (VACV-WR) receiving or not receiving the probiotic. Viral titres in organs and cytokine production in the lungs were analysed. Lung samples were also subjected to histological analysis. The intake of probiotic results in reduction in viral spread with a significant decrease of VACV titer on lung, liver and brain of treated group. In addition,treatment with the probiotic results in attenuated mice lung inflammation showing fewer lesions on histological findings and decreased lethality in mice infected with VACV. The ingestion of Lactobacillus paracasei ST11 (LPST11) after VACV infection resulted in 2/9 animal lethality compared with 4/9 in the VACV group. This is the first study on probiotics and VACV interactions, providing not only information about this interaction, but also proposing a model for future studies involving probiotics and other poxvirus.

  3. Sugar Deprivation Reduces Insemination of Anopheles gambiae (Diptera: Culicidae), Despite Daily Recruitment of Adults, and Predicts Decline in Model Populations

    PubMed Central

    STONE, C. M.; TAYLOR, R. M.; ROITBERG, B. D.; FOSTER, W. A.

    2009-01-01

    Our research tests the hypothesis that the inability to sugar-feed reduces the insemination rate in mosquito populations. To test this, we measured the effects of sugar availability on cumulative insemination performance of male Anopheles gambiae Giles s.s. (Diptera: Culicidae) during 10-d periods of continual emergence of equal numbers of both sexes, and we evaluated the implications at the population level with a matrix population model. On each day of each of four replicates, 20 newly emerged mosquitoes of each sex were recruited into the populations within two mesocosms, large walk-in enclosures with simulated natural conditions. Each mesocosm contained a cage to replicate the experiment on a small scale. Scented sucrose was absent or present (control). A human host was available nightly as a bloodmeal source in both mesocosms. Sugar availability and enclosure size significantly influenced female insemination. In the mesocosms, with sugar 49.7% of the females were inseminated, compared with 10.9% of the females without sugar. In the small cages, the insemination rates were 76.0 and 23.5%, respectively. In the mesocosms, cumulative survival of females after 10 d was 51.6% with sugar and 25.6% without sugar. In the cages, female survival was 95 and 73%, respectively. Sensitivity analysis of the population projection matrix shows that both reduced male survival and reduced mating capability due to a lack of sugar contributed to lower insemination rates in females, and in the absence of sugar the insemination rate was lowered to an extent that led to population decline. PMID:19960677

  4. Two-stage model for time-varying effects of discrete longitudinal covariates with applications in analysis of daily process data.

    PubMed

    Yang, Hanyu; Cranford, James A; Li, Runze; Buu, Anne

    2015-02-20

    This study proposes a generalized time-varying effect model that can be used to characterize a discrete longitudinal covariate process and its time-varying effect on a later outcome that may be discrete. The proposed method can be applied to examine two important research questions for daily process data: measurement reactivity and predictive validity. We demonstrate these applications using health risk behavior data collected from alcoholic couples through an interactive voice response system. The statistical analysis results show that the effect of measurement reactivity may only be evident in the first week of interactive voice response assessment. Moreover, the level of urge to drink before measurement reactivity takes effect may be more predictive of a later depression outcome. Our simulation study shows that the performance of the proposed method improves with larger sample sizes, more time points, and smaller proportions of zeros in the binary longitudinal covariate.

  5. A comprehensively quantitative method of evaluating the impact of drought on crop yield using daily multi-scale SPEI and crop growth process model.

    PubMed

    Wang, Qianfeng; Wu, Jianjun; Li, Xiaohan; Zhou, Hongkui; Yang, Jianhua; Geng, Guangpo; An, Xueli; Liu, Leizhen; Tang, Zhenghong

    2017-04-01

    The quantitative evaluation of the impact of drought on crop yield is one of the most important aspects in agricultural water resource management. To assess the impact of drought on wheat yield, the Environmental Policy Integrated Climate (EPIC) crop growth model and daily Standardized Precipitation Evapotranspiration Index (SPEI), which is based on daily meteorological data, are adopted in the Huang Huai Hai Plain. The winter wheat crop yields are estimated at 28 stations, after calibrating the cultivar coefficients based on the experimental site data, and SPEI data was taken 11 times across the growth season from 1981 to 2010. The relationship between estimated yield and multi-scale SPEI were analyzed. The optimum time scale SPEI to monitor drought during the crop growth period was determined. The reference yield was determined by averaging the yields from numerous non-drought years. From this data, we propose a comprehensive quantitative method which can be used to predict the impact of drought on wheat yields by combining the daily multi-scale SPEI and crop growth process model. This method was tested in the Huang Huai Hai Plain. The results suggested that estimation of calibrated EPIC was a good predictor of crop yield in the Huang Huai Hai Plain, with lower RMSE (15.4 %) between estimated yield and observed yield at six agrometeorological stations. The soil moisture at planting time was affected by the precipitation and evapotranspiration during the previous 90 days (about 3 months) in the Huang Huai Hai Plain. SPEIG90 was adopted as the optimum time scale SPEI to identify the drought and non-drought years, and identified a drought year in 2000. The water deficit in the year 2000 was significant, and the rate of crop yield reduction did not completely correspond with the volume of water deficit. Our proposed comprehensive method which quantitatively evaluates the impact of drought on crop yield is reliable. The results of this study further our

  6. A comprehensively quantitative method of evaluating the impact of drought on crop yield using daily multi-scale SPEI and crop growth process model

    NASA Astrophysics Data System (ADS)

    Wang, Qianfeng; Wu, Jianjun; Li, Xiaohan; Zhou, Hongkui; Yang, Jianhua; Geng, Guangpo; An, Xueli; Liu, Leizhen; Tang, Zhenghong

    2016-11-01

    The quantitative evaluation of the impact of drought on crop yield is one of the most important aspects in agricultural water resource management. To assess the impact of drought on wheat yield, the Environmental Policy Integrated Climate (EPIC) crop growth model and daily Standardized Precipitation Evapotranspiration Index (SPEI), which is based on daily meteorological data, are adopted in the Huang Huai Hai Plain. The winter wheat crop yields are estimated at 28 stations, after calibrating the cultivar coefficients based on the experimental site data, and SPEI data was taken 11 times across the growth season from 1981 to 2010. The relationship between estimated yield and multi-scale SPEI were analyzed. The optimum time scale SPEI to monitor drought during the crop growth period was determined. The reference yield was determined by averaging the yields from numerous non-drought years. From this data, we propose a comprehensive quantitative method which can be used to predict the impact of drought on wheat yields by combining the daily multi-scale SPEI and crop growth process model. This method was tested in the Huang Huai Hai Plain. The results suggested that estimation of calibrated EPIC was a good predictor of crop yield in the Huang Huai Hai Plain, with lower RMSE (15.4 %) between estimated yield and observed yield at six agrometeorological stations. The soil moisture at planting time was affected by the precipitation and evapotranspiration during the previous 90 days (about 3 months) in the Huang Huai Hai Plain. SPEIG90 was adopted as the optimum time scale SPEI to identify the drought and non-drought years, and identified a drought year in 2000. The water deficit in the year 2000 was significant, and the rate of crop yield reduction did not completely correspond with the volume of water deficit. Our proposed comprehensive method which quantitatively evaluates the impact of drought on crop yield is reliable. The results of this study further our understanding

  7. A comprehensively quantitative method of evaluating the impact of drought on crop yield using daily multi-scale SPEI and crop growth process model

    NASA Astrophysics Data System (ADS)

    Wang, Qianfeng; Wu, Jianjun; Li, Xiaohan; Zhou, Hongkui; Yang, Jianhua; Geng, Guangpo; An, Xueli; Liu, Leizhen; Tang, Zhenghong

    2017-04-01

    The quantitative evaluation of the impact of drought on crop yield is one of the most important aspects in agricultural water resource management. To assess the impact of drought on wheat yield, the Environmental Policy Integrated Climate (EPIC) crop growth model and daily Standardized Precipitation Evapotranspiration Index (SPEI), which is based on daily meteorological data, are adopted in the Huang Huai Hai Plain. The winter wheat crop yields are estimated at 28 stations, after calibrating the cultivar coefficients based on the experimental site data, and SPEI data was taken 11 times across the growth season from 1981 to 2010. The relationship between estimated yield and multi-scale SPEI were analyzed. The optimum time scale SPEI to monitor drought during the crop growth period was determined. The reference yield was determined by averaging the yields from numerous non-drought years. From this data, we propose a comprehensive quantitative method which can be used to predict the impact of drought on wheat yields by combining the daily multi-scale SPEI and crop growth process model. This method was tested in the Huang Huai Hai Plain. The results suggested that estimation of calibrated EPIC was a good predictor of crop yield in the Huang Huai Hai Plain, with lower RMSE (15.4 %) between estimated yield and observed yield at six agrometeorological stations. The soil moisture at planting time was affected by the precipitation and evapotranspiration during the previous 90 days (about 3 months) in the Huang Huai Hai Plain. SPEIG90 was adopted as the optimum time scale SPEI to identify the drought and non-drought years, and identified a drought year in 2000. The water deficit in the year 2000 was significant, and the rate of crop yield reduction did not completely correspond with the volume of water deficit. Our proposed comprehensive method which quantitatively evaluates the impact of drought on crop yield is reliable. The results of this study further our understanding

  8. Daily precipitation grids for Austria since 1961—development and evaluation of a spatial dataset for hydroclimatic monitoring and modelling

    NASA Astrophysics Data System (ADS)

    Hiebl, Johann; Frei, Christoph

    2017-03-01

    Spatial precipitation datasets that are long-term consistent, highly resolved and extend over several decades are an increasingly popular basis for modelling and monitoring environmental processes and planning tasks in hydrology, agriculture, energy resources management, etc. Here, we present a grid dataset of daily precipitation for Austria meant to promote such applications. It has a grid spacing of 1 km, extends back till 1961 and is continuously updated. It is constructed with the classical two-tier analysis, involving separate interpolations for mean monthly precipitation and daily relative anomalies. The former was accomplished by kriging with topographic predictors as external drift utilising 1249 stations. The latter is based on angular distance weighting and uses 523 stations. The input station network was kept largely stationary over time to avoid artefacts on long-term consistency. Example cases suggest that the new analysis is at least as plausible as previously existing datasets. Cross-validation and comparison against experimental high-resolution observations (WegenerNet) suggest that the accuracy of the dataset depends on interpretation. Users interpreting grid point values as point estimates must expect systematic overestimates for light and underestimates for heavy precipitation as well as substantial random errors. Grid point estimates are typically within a factor of 1.5 from in situ observations. Interpreting grid point values as area mean values, conditional biases are reduced and the magnitude of random errors is considerably smaller. Together with a similar dataset of temperature, the new dataset (SPARTACUS) is an interesting basis for modelling environmental processes, studying climate change impacts and monitoring the climate of Austria.

  9. Health beliefs affect the correct replacement of daily disposable contact lenses: Predicting compliance with the Health Belief Model and the Theory of Planned Behaviour.

    PubMed

    Livi, Stefano; Zeri, Fabrizio; Baroni, Rossella

    2017-02-01

    To assess the compliance of Daily Disposable Contact Lenses (DDCLs) wearers with replacing lenses at a manufacturer-recommended replacement frequency. To evaluate the ability of two different Health Behavioural Theories (HBT), The Health Belief Model (HBM) and The Theory of Planned Behaviour (TPB), in predicting compliance. A multi-centre survey was conducted using a questionnaire completed anonymously by contact lens wearers during the purchase of DDCLs. Three hundred and fifty-four questionnaires were returned. The survey comprised 58.5% females and 41.5% males (mean age 34±12years). Twenty-three percent of respondents were non-compliant with manufacturer-recommended replacement frequency (re-using DDCLs at least once). The main reason for re-using DDCLs was "to save money" (35%). Predictions of compliance behaviour (past behaviour or future intentions) on the basis of the two HBT was investigated through logistic regression analysis: both TPB factors (subjective norms and perceived behavioural control) were significant (p<0.01); HBM was less predictive with only the severity (past behaviour and future intentions) and perceived benefit (only for past behaviour) as significant factors (p<0.05). Non-compliance with DDCLs replacement is widespread, affecting 1 out of 4 Italian wearers. Results from the TPB model show that the involvement of persons socially close to the wearers (subjective norms) and the improvement of the procedure of behavioural control of daily replacement (behavioural control) are of paramount importance in improving compliance. With reference to the HBM, it is important to warn DDCLs wearers of the severity of a contact-lens-related eye infection, and to underline the possibility of its prevention. Copyright © 2016 British Contact Lens Association. Published by Elsevier Ltd. All rights reserved.

  10. User-Driven Workflow for Modeling, Monitoring, Product Development, and Flood Map Delivery Using Satellites for Daily Coverage Over Texas May-June 2015

    NASA Astrophysics Data System (ADS)

    Green, D. S.; Frye, S. W.; Wells, G. L.; Adler, R. F.; Brakenridge, R.; Bolten, J. D.; Murray, J. J.; Slayback, D. A.; Kirschbaum, D.; Wu, H.; Cappelaere, P. G.; Schumann, G.; Howard, T.; Flamig, Z.; Clark, R. A.; Stough, T.; Chini, M.; Matgen, P.

    2015-12-01

    Intense rainfall during late April and early May 2015 in Texas and Oklahoma led to widespread flooding in several river basins in that region. Texas state agencies were activated for the May-June floods and severe weather event that ensued for six weeks from May 8 until June 19 following Tropical Storm Bill. This poster depicts a case study where modeling flood potential informed decision making authorities for user-driven high resolution satellite acquisitions over the most critical areas and how experimental flood mapping techniques provided the capability for daily on-going monitoring of these events through the use of increased automation. Recent improvements in flood models resulting from higher frequency updates, better spatial resolution, and increased accuracy of now cast and forecast precipitation products coupled with advanced technology to improve situational awareness for decision makers. These advances enabled satellites to be tasked, data products to be developed and distributed, and feedback loops between the emergency authorities, satellite operators, and mapping researchers to deliver a daily stream of relevant products that informed deployment of emergency resources and improved management of the large-scale event across the local, state, and national levels. This collaboration was made possible through inter-agency cooperation on an international scale through the Committee on Earth Observation Satellites Flood Pilot activity that is supported in the USA by NASA, NOAA, and USGS and includes numerous civilian space agency assets from the European Space Agency along with national agencies from Italy, France, Germany, Japan, and others. The poster describes the inter-linking technology infrastructure, the development and delivery of mapping products, and the lessons learned for product improvement in the future.

  11. Forecasting peak daily ozone levels: part 2--A regression with time series errors model having a principal component trigger to forecast 1999 and 2002 ozone levels.

    PubMed

    Liu, Pao-Wen Grace; Johnson, Richard

    2003-12-01

    A modified time series approach, a Box-Jenkins regression with time series errors (RTSE) model plus a principal component (PC) trigger, has been developed to forecast peak daily 1-hr ozone (O3) in real time at the University of Wisconsin-Milwaukee North (UWM-N) during 1999 and 2002. The PC trigger acts as a predictor variable in the RTSE model. It tries to answer the question: will the next day be a possible high O3 day? To answer this question, three PC trigger rules were developed: (1) Hi-Low Checklist, (2) Discriminant Function Approach I, and (3) Discriminant Function Approach II. Also, a pure RTSE model without including the PC trigger and a persistence approach were tested for comparison. The RTSE model with DFA I successfully forecast the only two 1-hr federal exceedances (124 ppb), one in 1999 and one in 2002. In terms of the O3 100-ppb exceedances, 60-80% of the incorrect forecasts occurred with incorrect PC decisions. A few others may have been caused by unexpected O3-weather relations. When the three approaches used UWM-N data to forecast a 100-ppb exceedance somewhere in the Milwaukee, WI, metropolitan area, their performance was dramatically improved: the false alarm rate was reduced from 0.89 to 0.44, and the probability of detection was increased from 0.71 to 0.88.

  12. A model for developing job rotation schedules that eliminate sequential high workloads and minimize between-worker variability in cumulative daily workloads: Application to automotive assembly lines.

    PubMed

    Yoon, Sang-Young; Ko, Jeonghan; Jung, Myung-Chul

    2016-07-01

    The aim of study is to suggest a job rotation schedule by developing a mathematical model in order to reduce cumulative workload from the successive use of the same body region. Workload assessment using rapid entire body assessment (REBA) was performed for the model in three automotive assembly lines of chassis, trim, and finishing to identify which body part exposed to relatively high workloads at workstations. The workloads were incorporated to the model to develop a job rotation schedule. The proposed schedules prevent the exposure to high workloads successively on the same body region and minimized between-worker variance in cumulative daily workload. Whereas some of workers were successively assigned to high workload workstation under no job rotation and serial job rotation. This model would help to reduce the potential for work-related musculoskeletal disorders (WMSDs) without additional cost for engineering work, although it may need more computational time and relative complex job rotation sequences. Copyright © 2016 Elsevier Ltd and The Ergonomics Society. All rights reserved.

  13. The influence of synoptic airflow on UK daily precipitation extremes. Part II: regional climate model and E-OBS data validation

    NASA Astrophysics Data System (ADS)

    Maraun, Douglas; Osborn, Timothy J.; Rust, Henning W.

    2012-07-01

    We investigate how well the variability of extreme daily precipitation events across the United Kingdom is represented in a set of regional climate models and the E-OBS gridded data set. Instead of simply evaluating the climatologies of extreme precipitation measures, we develop an approach to validate the representation of physical mechanisms controlling extreme precipitation variability. In part I of this study we applied a statistical model to investigate the influence of the synoptic scale atmospheric circulation on extreme precipitation using observational rain gauge data. More specifically, airflow strength, direction and vorticity are used as predictors for the parameters of the generalised extreme value (GEV) distribution of local precipitation extremes. Here we employ this statistical model for our validation study. In a first step, the statistical model is calibrated against a gridded precipitation data set provided by the UK Met Office. In a second step, the same statistical model is calibrated against 14 ERA40 driven 25 km resolution RCMs from the ENSEMBLES project and the E-OBS gridded data set. Validation indices describing relevant physical mechanisms are derived from the statistical models for observations and RCMs and are compared using pattern standard deviation, pattern correlation and centered pattern root mean squared error as validation measures. The results for the different RCMs and E-OBS are visualised using Taylor diagrams. We show that the RCMs adequately simulate moderately extreme precipitation and the influence of airflow strength and vorticity on precipitation extremes, but show deficits in representing the influence of airflow direction. Also very rare extremes are misrepresented, but this result is afflicted with a high uncertainty. E-OBS shows considerable biases, in particular in regions of sparse data. The proposed approach might be used to validate other physical relationships in regional as well as global climate models.

  14. The effects of videotape modeling and daily feedback on residential electricity conservation, home temperature and humidity, perceived comfort, and clothing worn: Winter and summer

    PubMed Central

    Winett, Richard A.; Hatcher, Joseph W.; Fort, T. Richard; Leckliter, Ingrid N.; Love, Susan Q.; Riley, Anne W.; Fishback, James F.

    1982-01-01

    Two studies were conducted in all-electric townhouses and apartments in the winter (N = 83) and summer (N = 54) to ascertain how energy conservation strategies focusing on thermostat change and set-backs and other low-cost/no-cost approaches would affect overall electricity use and electricity used for heating and cooling, the home thermal environment, the perceived comfort of participants, and clothing that was worn. The studies assessed the effectiveness of videotape modeling programs that demonstrated these conservation strategies when used alone or combined with daily feedback on electricity use. In the winter, the results indicated that videotape modeling and/or feedback were effective relative to baseline and to a control group in reducing overall electricity use by about 15% and electricity used for heating by about 25%. Hygrothermographs, which accurately and continuously recorded temperature and humidity in the homes, indicated that participants were able to live with no reported loss in comfort and no change in attire at a mean temperature of about 62°F when home and about 59°F when asleep. The results were highly discrepant with prior laboratory studies indicating comfort at 75°F with the insulation value of the clothing worn by participants in this study. In the summer, a combination of strategies designed to keep a home cool with minimal or no air conditioning, in conjunction with videotape modeling and/or daily feedback, resulted in overall electricity reductions of about 15% with reductions on electricity for cooling of about 34%, but with feedback, and feedback and modeling more effective than modeling alone. Despite these electricity savings, hygrothermograph recordings indicated minimal temperature change in the homes, with no change in perceived comfort or clothing worn. The results are discussed in terms of discrepancies with laboratory studies, optimal combinations of video-media and personal contact to promote behavior change, and energy

  15. The effects of videotape modeling and daily feedback on residential electricity conservation, home temperature and humidity, perceived comfort, and clothing worn: Winter and summer.

    PubMed

    Winett, R A; Hatcher, J W; Fort, T R; Leckliter, I N; Love, S Q; Riley, A W; Fishback, J F

    1982-01-01

    Two studies were conducted in all-electric townhouses and apartments in the winter (N = 83) and summer (N = 54) to ascertain how energy conservation strategies focusing on thermostat change and set-backs and other low-cost/no-cost approaches would affect overall electricity use and electricity used for heating and cooling, the home thermal environment, the perceived comfort of participants, and clothing that was worn. The studies assessed the effectiveness of videotape modeling programs that demonstrated these conservation strategies when used alone or combined with daily feedback on electricity use. In the winter, the results indicated that videotape modeling and/or feedback were effective relative to baseline and to a control group in reducing overall electricity use by about 15% and electricity used for heating by about 25%. Hygrothermographs, which accurately and continuously recorded temperature and humidity in the homes, indicated that participants were able to live with no reported loss in comfort and no change in attire at a mean temperature of about 62 degrees F when home and about 59 degrees F when asleep. The results were highly discrepant with prior laboratory studies indicating comfort at 75 degrees F with the insulation value of the clothing worn by participants in this study. In the summer, a combination of strategies designed to keep a home cool with minimal or no air conditioning, in conjunction with videotape modeling and/or daily feedback, resulted in overall electricity reductions of about 15% with reductions on electricity for cooling of about 34%, but with feedback, and feedback and modeling more effective than modeling alone. Despite these electricity savings, hygrothermograph recordings indicated minimal temperature change in the homes, with no change in perceived comfort or clothing worn. The results are discussed in terms of discrepancies with laboratory studies, optimal combinations of video-media and personal contact to promote behavior

  16. Effects of climate change on daily minimum and maximum temperatures and cloudiness in the Shikoku region: a statistical downscaling model approach

    NASA Astrophysics Data System (ADS)

    Tatsumi, Kenichi; Oizumi, Tsutao; Yamashiki, Yosuke

    2015-04-01

    In this study, we present a detailed analysis of the effect of changes in cloudiness (CLD) between a future period (2071-2099) and the base period (1961-1990) on daily minimum temperature (TMIN) and maximum temperature (TMAX) in the same period for the Shikoku region, Japan. This analysis was performed using climate data obtained with the use of the Statistical DownScaling Model (SDSM). We calibrated the SDSM using the National Center for Environmental Prediction (NCEP) reanalysis dataset for the SDSM input and daily time series of temperature and CLD from 10 surface data points (SDP) in Shikoku. Subsequently, we validated the SDSM outputs, specifically, TMIN, TMAX, and CLD, obtained with the use of the NCEP reanalysis dataset and general circulation model (GCM) data against the SDP. The GCM data used in the validation procedure were those from the Hadley Centre Coupled Model, version 3 (HadCM3) for the Special Report on Emission Scenarios (SRES) A2 and B2 scenarios and from the third generation Coupled Global Climate Model (CGCM3) for the SRES A2 and A1B scenarios. Finally, the validated SDSM was run to study the effect of future changes in CLD on TMIN and TMAX. Our analysis showed that (1) the negative linear fit between changes in TMAX and those in CLD was statistically significant in winter while the relationship between the two changes was not evident in summer, (2) the dependency of future changes in TMAX and TMIN on future changes in CLD were more evident in winter than in other seasons with the present SDSM, (3) the diurnal temperature range (DTR) decreased in the southern part of Shikoku in summer in all the SDSM projections while DTR increased in the northern part of Shikoku in the same season in these projections, (4) the dependencies of changes in DTR on changes in CLD were unclear in summer and winter. Results of the SDSM simulations performed for climate change scenarios such as those from this study contribute to local-scale agricultural and

  17. Daily simulation using a three-dimensional atmosphere-ocean regional coupled model, CReSS-NHOES over the CINDY/DYNAMO observation region

    NASA Astrophysics Data System (ADS)

    Shinoda, T.; Yoshioka, M. K.; Aiki, H.; Kato, M.; Masunaga, H.; Smedstad, L. F.; Katsumata, M.; Yoneyama, K.; Higuchi, A.; Tsuboki, K.; Uyeda, H.

    2012-12-01

    We develop a three-dimensional atmosphere-ocean regional coupled-model with cloud-permitting scale; the atmosphere part is Cloud Resolving Storm Simulator (CReSS) and the ocean one is Non Hydrostatic Ocean model for the Earth Simulator (NHOES). This study shows results of daily simulation over the CINDY/DYNAMO observation region using CReSS-NHOES. Three types of sensitivity experiment are carried out to clarify the effect of the two-way coupled simulation and horizontal grid resolutions. One is the CReSS-NHOES two-way coupled simulation with horizontal grid spacing of 0.045 degree (approximately 4.8 km). Another two simulations are the CReSS simulations without coupling NHOES with horizontal grid spacing of 0.045 and 0.0225 degrees (approximately 2.4 km). The Global Spectral Model (GSM: Horizontal grid resolution is approximately 50 km) data provided by Japan Meteorological Agency (JMA) are used as the initial and boundary conditions of the atmosphere in CReSS and CReSS-NHOES. Three-dimensional Navy Coastal Ocean Model (NCOM) data provided by Naval Research Laboratory are used as the initial and boundary conditions of the ocean in CReSS-NHOES. The daily simulation is carried out for 36 hours from 12 UTC from October 1, 2011 to January 31, 2012 almost every day. We reproduce approximately 30-day surface pressure perturbation that should be related to the Madden-Julian Oscillation, half-day surface pressure perturbation that is related to the atmospheric tide, and the existence of low equivalent potential temperature airmass in the middle troposphere at a fixed observation point of the R/V Mirai (80.5E, 8S). However, the sharp vertical gradient of temperature and salinity at the bottom of the ocean mixed layer at the same point cannot be reproduced. The sensitivity of the coupling of the ocean model is not critical, because the difference of area-averaged sea surface temperature, sensible and latent heat fluxes from the sea surface is quite small. This should be

  18. Using a spatio-temporal dynamic state-space model with the EM algorithm to patch gaps in daily riverflow series, with examples from the Volta Basin, West Africa

    NASA Astrophysics Data System (ADS)

    Amisigo, B. A.; van de Giesen, N. C.

    2005-04-01

    A spatio-temporal linear dynamic model has been developed for patching short gaps in daily river runoff series. The model was cast in a state-space form in which the state variable was estimated using the Kalman smoother (RTS smoother). The EM algorithm was used to concurrently estimate both parameter and missing runoff values. Application of the model to daily runoff series in the Volta Basin of West Africa showed that the model was capable of providing good estimates of missing runoff values at a gauging station from the remaining series at the station and at spatially correlated stations in the same sub-basin.

  19. Tips for Daily Living

    MedlinePlus

    ... Tips and Gadgets for Daily Activities Dressing Tips Shopping Tips Modifying the Bathroom Driving After Stroke Medication ... and resources. Find a group in your area . Online Support If there is not a support group ...

  20. Regionalising rainfall-runoff modelling for predicting daily runoff: Comparing gridded spatial proximity and gridded integrated similarity approaches against their lumped counterparts

    NASA Astrophysics Data System (ADS)

    Li, Hongxia; Zhang, Yongqiang

    2017-07-01

    Rainfall-runoff models are widely used for regionalisation studies to predict daily runoff time series in ungauged catchments. Most studies focus mainly on a particular region or a small scale, and are applied in a lumped way. It is not clear how grid-based regionalisation methods perform at continental or global scale, particularly for data-sparse region. This study uses 605 unregulated catchments widely distributed across Australia to evaluate two grid-based regionalisation approaches-gridded spatial proximity (SP_g) and gridded integrated similarity (IS_g) - and their lumped counterparts (SP_l and IS_l). To test robustness of the regionalisation methods, each was tested using two rainfall-runoff models: SIMHYD and Xinanjiang. We found that overall the gridded and lumped regionalisation approaches are marginally different and the two models show consistent regionalisation results. However, the IS_g approach outperforms the others in the dry and sparsely located catchments, and it overcomes the unnatural tessellated effect obtained from the SP_g approach. It is promising to use the IS_g approach for runoff estimates and water accounts in the Australian continent and possibly in other parts of world.

  1. Estimating daily time series of streamflow using hydrological model calibrated based on satellite observations of river water surface width: Toward real world applications.

    PubMed

    Sun, Wenchao; Ishidaira, Hiroshi; Bastola, Satish; Yu, Jingshan

    2015-05-01

    Lacking observation data for calibration constrains applications of hydrological models to estimate daily time series of streamflow. Recent improvements in remote sensing enable detection of river water-surface width from satellite observations, making possible the tracking of streamflow from space. In this study, a method calibrating hydrological models using river width derived from remote sensing is demonstrated through application to the ungauged Irrawaddy Basin in Myanmar. Generalized likelihood uncertainty estimation (GLUE) is selected as a tool for automatic calibration and uncertainty analysis. Of 50,000 randomly generated parameter sets, 997 are identified as behavioral, based on comparing model simulation with satellite observations. The uncertainty band of streamflow simulation can span most of 10-year average monthly observed streamflow for moderate and high flow conditions. Nash-Sutcliffe efficiency is 95.7% for the simulated streamflow at the 50% quantile. These results indicate that application to the target basin is generally successful. Beyond evaluating the method in a basin lacking streamflow data, difficulties and possible solutions for applications in the real world are addressed to promote future use of the proposed method in more ungauged basins.

  2. Pharmacological Doses of Daily Ascorbate Protect Tumors from Radiation Damage after a Single Dose of Radiation in an Intracranial Mouse Glioma Model

    PubMed Central

    Grasso, Carole; Fabre, Marie-Sophie; Collis, Sarah V.; Castro, M. Leticia; Field, Cameron S.; Schleich, Nanette; McConnell, Melanie J.; Herst, Patries M.

    2014-01-01

    Pharmacological ascorbate is currently used as an anti-cancer treatment, potentially in combination with radiation therapy, by integrative medicine practitioners. In the acidic, metal-rich tumor environment, ascorbate acts as a pro-oxidant, with a mode of action similar to that of ionizing radiation; both treatments kill cells predominantly by free radical-mediated DNA damage. The brain tumor, glioblastoma multiforme (GBM), is very resistant to radiation; radiosensitizing GBM cells will improve survival of GBM patients. Here, we demonstrate that a single fraction (6 Gy) of radiation combined with a 1 h exposure to ascorbate (5 mM) sensitized murine glioma GL261 cells to radiation in survival and colony-forming assays in vitro. In addition, we report the effect of a single fraction (4.5 Gy) of whole brain radiation combined with daily intraperitoneal injections of ascorbate (1 mg/kg) in an intracranial GL261 glioma mouse model. Tumor-bearing C57BL/6 mice were divided into four groups: one group received a single dose of 4.5 Gy to the brain 8 days after tumor implantation, a second group received daily intraperitoneal injections of ascorbate (day 8–45) after implantation, a third group received both treatments and a fourth control group received no treatment. While radiation delayed tumor progression, intraperitoneal ascorbate alone had no effect on tumor progression. Tumor progression was faster in tumor-bearing mice treated with radiation and daily ascorbate than in those treated with radiation alone. Histological analysis showed less necrosis in tumors treated with both radiation and ascorbate, consistent with a radio-protective effect of ascorbate in vivo. Discrepancies between our in vitro and in vivo results may be explained by differences in the tumor microenvironment, which determines whether ascorbate remains outside the cell, acting as a pro-oxidant, or whether it enters the cells and acts as an anti-oxidant. PMID:25566497

  3. Carbon plate shows even distribution of stress, decreases screw loosening, and increases recovery of preoperative daily feed intake amount in a rabbit model of mandibular continuity defects.

    PubMed

    Lee, Sang-Woon; György, Szabó; Choi, Jae Bong; Choi, Je-Yong; Kim, Seong-Gon

    2014-07-01

    The aim of this study was to compare a carbon plate (CP) and a titanium mandibular reconstruction plate (TMRP) in finite element analysis and an animal model. Twelve rabbits were used for this experiment. After a mandible continuity defect was created, either a CP or a TMRP was used for mandibular reconstruction. Postoperatively, daily feed intake amount (DFIA) was measured for 4 weeks. Radiographic images were also acquired to evaluate screw loosening. For the analysis of the stress distribution, a simple continuity defect model was used, and finite element analysis was performed. The CP group had 0.80 ± 0.45 lost screws in an animal during the 4 weeks postoperative observation; however, the TMRP group had 1.86 ± 0.69 lost screws (p = 0.014). Overall, the 5 out of 5 of rabbits in the CP group and 3 out of 7 in the TMRP group exhibited preoperative levels of DFIA during the 4 week observation (p = 0.038). The finite element analysis showed that the stress was more evenly distributed in the CP than in the TMRP model. The CP group showed decreased screw loosening and increased recovery of preoperative DFIA compared to the TMRP group in a rabbit model of mandibular continuity defects. Perfect adaptation of CP during the operation could not be achieved in spite of reshaping to the mandibular curvature. This disadvantage of the CP system can be overcome by the prefabricated technique using a prototype model. Copyright © 2013 European Association for Cranio-Maxillo-Facial Surgery. Published by Elsevier Ltd. All rights reserved.

  4. Using the Enhanced Daily Load Stimulus Model to Quantify the Mechanical Load and Bone Mineral Density Changes Experienced by Crew Members on the International Space Station

    NASA Technical Reports Server (NTRS)

    Genc, K. O.; Gopalakrishnan, R.; Kuklis, M. M.; Maender, C. C.; Rice, A. J.; Cavanagh, P. R.

    2009-01-01

    Despite the use of exercise countermeasures during long-duration space missions, bone mineral density (BMD) and predicted bone strength of astronauts continue to show decreases in the lower extremities and spine. This site-specific bone adaptation is most likely caused by the effects of microgravity on the mechanical loading environment of the crew member. There is, therefore, a need to quantify the mechanical loading experienced on Earth and on-orbit to define the effect of a given "dose" of loading on bone homeostasis. Gene et al. recently proposed an enhanced DLS (EDLS) model that, when used with entire days of in-shoe forces, takes into account recently developed theories on the importance of factors such as saturation, recovery, and standing and their effects on the osteogenic response of bone to daily physical activity. This algorithm can also quantify the tinting and type of activity (sit/unload, stand, walk, run or other loaded activity) performed throughout the day. The purpose of the current study was to use in-shoe force measurements from entire typical work days on Earth and on-orbit in order to quantify the type and amount of loading experienced by crew members. The specific aim was to use these measurements as inputs into the EDLS model to determine activity timing/type and the mechanical "dose" imparted on the musculoskeletal system of crew members and relate this dose to changes in bone homeostasis.

  5. Linking vegetation greenness and seasonal snow characteristics using field observations, SnowModel, and daily MODIS imagery in high-Arctic Greenland

    NASA Astrophysics Data System (ADS)

    Pedersen, S. H.; Liston, G. E.; Tamstorf, M. P.; Schmidt, N. M.; Abermann, J.

    2016-12-01

    In the terrestrial high-Arctic, the spatial distribution of vegetation is largely governed by the persistent pattern of snow cover through decades. Whereas the maximum level of vegetation greenness and its timing are likely controlled by the timing of snowmelt, the snow-free date, and the meltwater amounts released from the snowpack during spring snowmelt. To explore this second relationship, we applied the SnowModel snow modelling tool, with meteorological station observations and ERA-Interim reanalysis data, to reproduce the spatial and temporal snow distribution and snow-pack evolution in a high-Arctic region extending from the margin of the Greenland Ice Sheet to the east coast of Greenland. Within this region, and for the period 1979-2015, interannual variations in late-winter snow-water-equivalent, snowmelt timing, snow-free date, and meltwater quantities will be presented. Furthermore, spatially distributed vegetation greenness from daily Moderate Resolution Imaging Spectroradiometer (MODIS) imagery at 300-m spatial resolution are included to discuss the relationships between snowmelt measures (e.g., snow-free date) and vegetation phenological events (e.g., timing of annual maximum vegetation greenness) during the growing seasons 2000-2015.

  6. Analysis of the impacts of EC-Earth Global Circulation Model in the RCP45 climate change scenario on maximum daily streamflow quantiles at global scale

    NASA Astrophysics Data System (ADS)

    Silvestro, Francesco; Campo, Lorenzo; Rudari, Roberto; Herold, Christian; De Angeli, Silvia; Gabellani, Simone; D'Andrea, Mirko; Rodila, Denisa

    2016-04-01

    Climate changes can have an impact on various components of hydrological cycle. From a risk assessment point of view it is certainly interesting understanding how extreme streamflow values can change as a consequence of climate variability. In order to do this the outputs of a climate model (EC-EARTH) that accounts for a standard climate scenario were used to feed a hydrological model and to generate 140 years (1960-2100) of continuous streamflow simulations in a large number of stations that cover all the world. These time series were then post-processed in order to evaluate how annual daily maximum streamflow quantiles change because of climate scenarios. The analysis highlights that in many cases there is an increment or a decrease of the quantiles for fixed return periods, but only in a reduced number of situations these variation lay out of the confidence intervals of the quantiles estimated in current climate. The analysis was carried out on over 5000 stations distributed in all continents and spanned the period 1960-2100 according to the climate scenario RCP45.

  7. Using repeated daily assessments to uncover oscillating patterns and temporally-dynamic triggers in structures of psychopathology: Applications to the DSM-5 alternative model of personality disorders.

    PubMed

    Roche, Michael J; Jacobson, Nicholas C; Pincus, Aaron L

    2016-11-01

    Articulating an accurate and clinically useful structure of psychopathology is a crucial and difficult task. Dimensions identified through cross-sectional factor analyses are increasingly being linked with temporally dynamic processes of social cognition, emotion regulation, symptom expression, and functional impairment to demonstrate how between-person structures and within-person dynamics can be integrated. The present research considers how structure and processes are integrated in the DSM-5, specifically in the alternative model for personality disorders (AMPD). Participants (n = 248) completed a 14-day electronic diary, and results indicated that personality impairments oscillated across days and were triggered by daily negative emotions and cognitive distortions. Importantly, some aspects of the AMPD model that are identified as potentially redundant in cross-sectional research are shown here to increment each other in the prediction of dynamic oscillations and triggers. Thus, longitudinal designs and temporally dynamic analyses may provide new and novel evidence to fully inform structures of psychopathology. Such research is a needed step in the integration of the structure and process in classification and diagnosis of psychopathology. (PsycINFO Database Record

  8. Daily and Compulsive Internet Use and Well-Being in Adolescence: A Diathesis-Stress Model Based on Big Five Personality Traits

    ERIC Educational Resources Information Center

    van der Aa, Niels; Overbeek, Geertjan; Engels, Rutger C. M. E.; Scholte, Ron H. J.; Meerkerk, Gert-Jan; Van den Eijnden, Regina J. J. M.

    2009-01-01

    This study examined the associations between adolescents' daily Internet use and low well-being (i.e., loneliness, low self-esteem, and depressive moods). We hypothesized that (a) linkages between high levels of daily Internet use and low well-being would be mediated by compulsive Internet use (CIU), and (b) that adolescents with low levels of…

  9. Daily and Compulsive Internet Use and Well-Being in Adolescence: A Diathesis-Stress Model Based on Big Five Personality Traits

    ERIC Educational Resources Information Center

    van der Aa, Niels; Overbeek, Geertjan; Engels, Rutger C. M. E.; Scholte, Ron H. J.; Meerkerk, Gert-Jan; Van den Eijnden, Regina J. J. M.

    2009-01-01

    This study examined the associations between adolescents' daily Internet use and low well-being (i.e., loneliness, low self-esteem, and depressive moods). We hypothesized that (a) linkages between high levels of daily Internet use and low well-being would be mediated by compulsive Internet use (CIU), and (b) that adolescents with low levels of…

  10. Calibration and parameterization of a semi-distributed hydrological model to support sub-daily ensemble flood forecasting; a watershed in southeast Brazil

    NASA Astrophysics Data System (ADS)

    de Almeida Bressiani, D.; Srinivasan, R.; Mendiondo, E. M.

    2013-12-01

    The use of distributed or semi-distributed models to represent the processes and dynamics of a watershed in the last few years has increased. These models are important tools to predict and forecast the hydrological responses of the watersheds, and they can subside disaster risk management and planning. However they usually have a lot of parameters, of which, due to the spatial and temporal variability of the processes, are not known, specially in developing countries; therefore a robust and sensible calibration is very important. This study conduced a sub-daily calibration and parameterization of the Soil & Water Assessment Tool (SWAT) for a 12,600 km2 watershed in southeast Brazil, and uses ensemble forecasts to evaluate if the model can be used as a tool for flood forecasting. The Piracicaba Watershed, in São Paulo State, is mainly rural, but has about 4 million of population in highly relevant urban areas, and three cities in the list of critical cities of the National Center for Natural Disasters Monitoring and Alerts. For calibration: the watershed was divided in areas with similar hydrological characteristics, for each of these areas one gauge station was chosen for calibration; this procedure was performed to evaluate the effectiveness of calibrating in fewer places, since areas with the same group of groundwater, soil, land use and slope characteristics should have similar parameters; making calibration a less time-consuming task. The sensibility analysis and calibration were performed on the software SWAT-CUP with the optimization algorithm: Sequential Uncertainly Fitting Version 2 (SUFI-2), which uses Latin hypercube sampling scheme in an iterative process. The performance of the models to evaluate the calibration and validation was done with: Nash-Sutcliffe efficiency coefficient (NSE), determination coefficient (r2), root mean square error (RMSE), and percent bias (PBIAS), with monthly average values of NSE around 0.70, r2 of 0.9, normalized RMSE of 0

  11. Evaluation and adjustment of description of denitrification in the DailyDayCent and COUP models based on N2 and N2O laboratory incubation system measurements

    NASA Astrophysics Data System (ADS)

    Grosz, Balázs; Well, Reinhard; Dannenmann, Michael; Dechow, René; Kitzler, Barbara; Michel, Kerstin; Reent Köster, Jan

    2017-04-01

    data-sets are needed in view of the extreme spatio-temporal heterogeneity of denitrification. DASIM will provide such data based on laboratory incubations including measurement of N2O and N2 fluxes and determination of the relevant drivers. Here, we present how we will use these data to evaluate common biogeochemical process models (DailyDayCent, Coup) with respect to modeled NO, N2O and N2 fluxes from denitrification. The models are used with different settings. The first approximation is the basic "factory" setting of the models. The next step would show the precision in the results of the modeling after adjusting the appropriate parameters from the result of the measurement values and the "factory" results. The better adjustment and the well-controlled input and output measured parameters could provide a better understanding of the probable scantiness of the tested models which will be a basis for future model improvement.

  12. Daily rainfall and temperature estimation by kriging with external drift in an Alpine Catchment. Sensitivity analysis to the temporal scale adopted to define the variogram models. (southeast Spain)

    NASA Astrophysics Data System (ADS)

    Juan Collados-Lara, Antonio; Pardo-Iguzquiza, Eulogio; Pulido-Velazquez, David; Jimenez-Sanchez, Jorge

    2016-04-01

    The knowledge of the climatic historical variables in a River Basin is essential for an appropriate management of the water resources in the system. Temperature and precipitation are the most important variables from the point of view of the assessment of water availability and its spatially and temporal distribution. The aim of this work is to estimate temperature and precipitation using kriging with external drift (KED). A grid with a spatial resolution of 1 km and a daily temporal resolution has been adopted to estimate values for the period 1980 to 2014 in the "Alto Genil" basin (southeast Spain). The altitude in the catchment changes from 530 to 3100 m a.s.l. The climatic variables depend of the altitude and this variable has been used as external drift. Data from 119 precipitation station and 72 temperature station of the AEMET have been employed. The relationship between the altitude and the variables has been analyzed using the regression function of daily mean precipitation and temperature for annual and monthly scale. Normally the temperature and precipitation increase linearly with the altitude. The relationship between temperature and altitude is clearly linear. In the case of the precipitation there is a value of altitude (approximately 1500 m) from which the precipitation decreases with the altitude (inverse rainfall gradient) for every months with the exception of July that has a linear relationship. This inverse rainfall gradient has been observed in other cases as Andes Mountains, some African high mountains, tropical or subtropical high mountains. Therefore, in the case of the precipitation we have a quadratic external drift and for the temperature we have a linear external drift. The monthly and annual climatic variograms were calibrated in order to study if the climatic variables have a seasonal conduct. The KED allows to obtain an estimation with both models (annual and monthly) for the two variables and we can quantify the sensibility of the

  13. Accelerometer measured daily physical activity and sedentary pursuits–comparison between two models of the Actigraph and the importance of data reduction

    PubMed Central

    2013-01-01

    Background Very few validation studies have been performed between different generations of the commonly used Actigraph accelerometers. We compared daily physical activity data generated from the old generation Actigraph model 7164 with the new generation Actigraph GT1M accelerometer in 15 young females for eight consecutive days. We also investigated if different wear time thresholds had any impact on the findings. Minutes per day of moderate and vigorous physical activity (MVPA), vigorous physical activity (VPA) and very vigorous physical activity (VVPA) were calculated. Moreover, minutes of sedentary pursuits per day were calculated. Findings There were significant (P < 0.05) differences between the Actigraph 7164 and the GT1M concerning MVPA (61 ± 21vs. 56 ± 23 min/day), VPA (12 ± 8 vs. 9 ± 3 min/day) and VVPA (3.2 ± 3.0 vs. 0.3 ± 1.1 min/day). The different wear time thresholds had little impact on minutes per day in different intensities. Median minutes of sedentary pursuits per day ranged from 159 to 438 minutes depending on which wear time threshold was used (i.e. 10, 30 or 60 minutes), whereas very small differences were observed between the two different models. Conclusions Data from the old generation Actigraph 7164 and the new generation Actigraph GT1M accelerometers differ, where the Actigraph GT1M generates lower minutes spent in free living physical activity. Median minutes of sedentary pursuits per day are highly dependent on which wear time threshold that is used, and not by accelerometer model. PMID:24176143

  14. SU-E-J-257: A PCA Model to Predict Adaptive Changes for Head&neck Patients Based On Extraction of Geometric Features From Daily CBCT Datasets

    SciTech Connect

    Chetvertkov, M; Siddiqui, F; Chetty, I; Kim, J; Kumarasiri, A; Liu, C; Gordon, J

    2015-06-15

    Purpose: Using daily cone beam CTs (CBCTs) to develop principal component analysis (PCA) models of anatomical changes in head and neck (H&N) patients and to assess the possibility of using these prospectively in adaptive radiation therapy (ART). Methods: Planning CT (pCT) images of 4 H&N patients were deformed to model several different systematic changes in patient anatomy during the course of the radiation therapy (RT). A Pinnacle plugin was used to linearly interpolate the systematic change in patient for the 35 fraction RT course and to generate a set of 35 synthetic CBCTs. Each synthetic CBCT represents the systematic change in patient anatomy for each fraction. Deformation vector fields (DVFs) were acquired between the pCT and synthetic CBCTs with random fraction-to-fraction changes were superimposed on the DVFs. A patient-specific PCA model was built using these DVFs containing systematic plus random changes. It was hypothesized that resulting eigenDVFs (EDVFs) with largest eigenvalues represent the major anatomical deformations during the course of treatment. Results: For all 4 patients, the PCA model provided different results depending on the type and size of systematic change in patient’s body. PCA was more successful in capturing the systematic changes early in the treatment course when these were of a larger scale with respect to the random fraction-to-fraction changes in patient’s anatomy. For smaller scale systematic changes, random changes in patient could completely “hide” the systematic change. Conclusion: The leading EDVF from the patientspecific PCA models could tentatively be identified as a major systematic change during treatment if the systematic change is large enough with respect to random fraction-to-fraction changes. Otherwise, leading EDVF could not represent systematic changes reliably. This work is expected to facilitate development of population-based PCA models that can be used to prospectively identify significant

  15. An analysis of the daily precipitation variability in the Himalayan orogen using a statistical parameterisation and its potential in driving landscape evolution models with stochastic climatic forcing

    NASA Astrophysics Data System (ADS)

    Deal, Eric; Braun, Jean

    2015-04-01

    A current challenge in landscape evolution modelling is to integrate realistic precipitation patterns and behaviour into longterm fluvial erosion models. The effect of precipitation on fluvial erosion can be subtle as well as nonlinear, implying that changes in climate (e.g. precipitation magnitude or storminess) may have unexpected outcomes in terms of erosion rates. For example Tucker and Bras (2000) show theoretically that changes in the variability of precipitation (storminess) alone can influence erosion rate across a landscape. To complicate the situation further, topography, ultimately driven by tectonic uplift but shaped by erosion, has a major influence on the distribution and style of precipitation. Therefore, in order to untangle the coupling between climate, erosion and tectonics in an actively uplifting orogen where fluvial erosion is dominant it is important to understand how the 'rain dial' used in a landscape evolution model (LEM) corresponds to real precipitation patterns. One issue with the parameterisation of rainfall for use in an LEM is the difference between the timescales for precipitation (≤ 1 year) and landscape evolution (> 103 years). As a result, precipitation patterns must be upscaled before being integrated into a model. The relevant question then becomes: What is the most appropriate measure of precipitation on a millennial timescale? Previous work (Tucker and Bras, 2000; Lague, 2005) has shown that precipitation can be properly upscaled by taking into account its variable nature, along with its average magnitude. This captures the relative size and frequency of extreme events, ensuring a more accurate characterisation of the integrated effects of precipitation on erosion over long periods of time. In light of this work, we present a statistical parameterisation that accurately models the mean and daily variability of ground based (APHRODITE) and remotely sensed (TRMM) precipitation data in the Himalayan orogen with only a few

  16. Effect of icariin in combination with daily sildenafil on penile atrophy and erectile dysfunction in a rat model of bilateral cavernous nerves injury.

    PubMed

    Xu, Y; Xin, H; Wu, Y; Guan, R; Lei, H; Fu, X; Xin, Z; Yang, Y

    2017-05-01

    The commonly utilized phosphodiesterase type 5 inhibitors do not lead to satisfactory penile erection after radical prostatectomy mainly because of insufficient nitric oxide drive from the damaged cavernous nerves. The aim of this study was to assess the efficacy and mechanisms of icariin in combination with daily sildenafil on neurogenic erectile dysfunction and penile atrophy in a rat model of bilateral cavernous nerves injury. Sixty male Sprague-Dawley rats injected with 5-ethynyl-2-deoxyuridine (50 mg/kg) at postnatal day 1 for the purpose of tracking endogenous stem cells in penis. Forty-eight rats of bilateral cavernous nerves injury were randomized equally into gavage feeding of vehicle, sildenafil (10 mg/kg), icariin (1.5 mg/kg) and sildenafil + icariin, respectively. Twelve sham-operated rats served as control. The intracavernous pressure and mean arterial pressure was measured and mid-penile cross sections were histologically examined 5 weeks after surgery. Western blotting of cavernous tissue protein was also performed. Animals treated with sildenafil + icariin had significantly higher mean intracavernous pressure/mean arterial pressure ratio relative to other rats with bilateral cavernous nerves injury (p < 0.05). The circumference and mean cross-sectional area of the paired corpus cavernosum were effectively preserved in the sildenafil + icariin. Treatment with sildenafil + icariin significantly increased the cavernous cyclic guanosine monophosphate concentration compared with the icariin group (p < 0.05). In addition, the numbers of neuronal nitric oxide synthase-positive nerves and 5-ethynyl-2-deoxyuridine-positive cells co-expressing S100 in the icariin-treated groups were greater compared with the bilateral cavernous nerves injury control group (p < 0.05). These data suggest that the combined use of icariin and daily sildenafil holds promise as a potential therapy for neurogenic erectile dysfunction in the future. The underlying

  17. Toothbrushing: Do It Daily.

    ERIC Educational Resources Information Center

    Texas Child Care, 1993

    1993-01-01

    Offers a practical guide for promoting daily toothbrushing in young children. Discusses the importance of proper dental care, explains the causes of tooth decay, describes proper dental care for infants and young children, recommends materials and teaching methods, and discusses visits to the dentist and the benefits of fluoride for dental health.…

  18. Toothbrushing: Do It Daily.

    ERIC Educational Resources Information Center

    Texas Child Care, 1993

    1993-01-01

    Offers a practical guide for promoting daily toothbrushing in young children. Discusses the importance of proper dental care, explains the causes of tooth decay, describes proper dental care for infants and young children, recommends materials and teaching methods, and discusses visits to the dentist and the benefits of fluoride for dental health.…

  19. Estimation of daily risk of neonatal death, including the day of birth, in 186 countries in 2013: a vital-registration and modelling-based study.

    PubMed

    Oza, Shefali; Cousens, Simon N; Lawn, Joy E

    2014-11-01

    The days immediately after birth are the most risky for human survival, yet neonatal mortality risks are generally not reported by day. Early neonatal deaths are sometimes under-reported or might be misclassified by day of death or as stillbirths. We modelled daily neonatal mortality risk and estimated the proportion of deaths on the day of birth and in week 1 for 186 countries in 2013. We reviewed data from vital registration (VR) and demographic and health surveys for information on the timing of neonatal deaths. For countries with high-quality VR we used the data as reported. For countries without high-quality VR data, we applied an exponential model to data from 206 surveys in 79 countries (n=50,396 deaths) to estimate the proportions of neonatal deaths per day and used bootstrap sampling to develop uncertainty estimates. 57 countries (n=122,757 deaths) had high-quality VR, and modelled data were used for 129 countries. The proportion of deaths on the day of birth (day 0) and within week 1 varied little by neonatal mortality rate, income, or region. 1·00 million (36.3%) of all neonatal deaths occurred on day 0 (uncertainty range 0·94 million to 1·05 million), and 2·02 million (73.2%) in the first week (uncertainty range 1·99 million to 2·05 million). Sub-Saharan Africa had the highest risk of neonatal death and, therefore, had the highest risk of death on day 0 (11·2 per 1000 livebirths); the highest number of deaths on day 0 was seen in southern Asia (n=392,300). The risk of early neonatal death is very high across a range of countries and contexts. Cost-effective and feasible interventions to improve neonatal and maternity care could save many lives. Save the Children's Saving Newborn Lives programme. Copyright © 2014 Oza et al. Open Access article distributed under the terms of CC BY. Published by .. All rights reserved.

  20. Genistein administered as a once-daily oral supplement had no beneficial effect on the tibia in rat models for postmenopausal bone loss.

    PubMed

    Turner, Russell T; Iwaniec, Urszula T; Andrade, Juan E; Branscum, Adam J; Neese, Steven L; Olson, Dawn A; Wagner, Lindsay; Wang, Victor C; Schantz, Susan L; Helferich, William G

    2013-06-01

    Estrogen deficiency after menopause results in rapid bone loss, predisposing women to osteoporotic fractures. Genistein, a phytoestrogen present in high concentrations in soy, is an ingredient in dietary supplements aggressively marketed for bone health. However, in a recent long-duration clinical trial in postmenopausal women, the efficacy of soy extracts in reducing bone loss was disappointing. To better understand the failure of soy extracts to consistently induce a robust skeletal response in women, we investigated the long-term (5 mo) efficacy of genistein, administered as a daily oral supplement, (1) in preventing cancellous bone loss in skeletally mature virgin Long-Evans rats ovariectomized at 7 months of age and (2) in improving cancellous bone mass and architecture in aged retired-breeder rats ovariectomized at 16 or 22 months of age. Rats within each age group were randomly assigned into one of three treatment groups (n = 7-12 rats/group): (1) vehicle control, (2) genistein 485 μg/day, or (3) genistein 970 μg/day, resulting in mean (SE) serum genistein levels of 0.18 (0.10), 0.76 (0.15), and 1.48 (0.31) μM, respectively. Total tibia bone mass and density were evaluated using dual-energy x-ray absorptiometry, whereas cancellous bone mass and architecture in the tibial metaphysis, as well as cortical bone mass and architecture in the tibial diaphysis, were evaluated by micro-CT. Oral genistein administered as a dietary supplement did not influence the cumulative effects of ovariectomy, aging, and/or reproductive history on cancellous and cortical bone mass and architecture. Serum levels of genistein similar to those in women consuming a high-soy diet are ineffective in preventing or treating bone loss in rat models for postmenopausal osteoporosis.

  1. Genistein Delivered as a Once Daily Oral Supplement Had No Beneficial Effect on the Tibia in Rat Models for Postmenopausal Bone Loss

    PubMed Central

    Turner, Russell T.; Iwaniec, Urszula T.; Andrade, Juan E.; Branscum, Adam J.; Neese, Steven L.; Olson, Dawn A.; Wagner, Lindsay; Wang, Victor C.; Schantz, Susan L.; Helferich, William G.

    2014-01-01

    Objective Estrogen deficiency following menopause results in rapid bone loss, predisposing women to osteoporotic fractures. Genistein, a phytoestrogen present in high concentrations in soy, is an ingredient in dietary supplements aggressively marketed for bone health. However, the efficacy of soy extracts in reducing bone loss in a recent, long-duration clinical trial in postmenopausal women was disappointing. To better understand the failure of soy extracts to consistently induce a robust skeletal response in women, we investigated the long-term (5 months) effects of genistein, administered as a daily oral supplement, on 1) its efficacy to prevent cancellous bone loss in skeletally mature virgin Long-Evans rats ovariectomized (ovx) at 7 months of age, and 2) its efficacy to improve cancellous bone mass and architecture in aged retired breeder rats ovx at 16 or 22 months of age. Methods Rats within each age group were randomly assigned into one of 3 treatment groups (n=7–12 rats/group); 1) vehicle control, 2) 485 µg/day genistein, or 3) 970 µg/day genistein, resulting in serum genistein levels of 0.18 ± 0.10, 0.76 ± 0.15, and 1.48 ± 0.31 µM, respectively. Total tibia bone mass and density were evaluated using dual energy absorptiometry whereas cancellous bone mass and architecture in the tibial metaphysis and cortical bone mass and architecture in the tibial diaphysis were evaluated by micro-computed tomography. Results Oral genistein administered as a dietary supplement did not influence the cumulative effects of ovx, aging and/or reproductive history on cancellous and cortical bone mass and architecture. Conclusions Serum levels of genistein similar to those in women consuming a high soy diet are ineffective in prevention or treatment of bone loss in rat models for postmenopausal osteoporosis. PMID:23385720

  2. Inter-daily variability of a strong thermally-driven wind system over the Atacama Desert of South America: synoptic forcing and short-term predictability using the GFS global model

    NASA Astrophysics Data System (ADS)

    Jacques-Coper, Martín; Falvey, Mark; Muñoz, Ricardo C.

    2015-07-01

    Crucial aspects of a strong thermally-driven wind system in the Atacama Desert in northern Chile during the extended austral winter season (May-September) are studied using 2 years of measurement data from the Sierra Gorda 80-m meteorological mast (SGO, 22° 56' 24″ S; 69° 7' 58″ W, 2,069 m above sea level (a.s.l.)). Daily cycles of atmospheric variables reveal a diurnal (nocturnal) regime, with northwesterly (easterly) flow and maximum mean wind speed of 8 m/s (13 m/s) on average. These distinct regimes are caused by pronounced topographic conditions and the diurnal cycle of the local radiative balance. Wind speed extreme events of each regime are negatively correlated at the inter-daily time scale: High diurnal wind speed values are usually observed together with low nocturnal wind speed values and vice versa. The associated synoptic conditions indicate that upper-level troughs at the coastline of southwestern South America reinforce the diurnal northwesterly wind, whereas mean undisturbed upper-level conditions favor the development of the nocturnal easterly flow. We analyze the skill of the numerical weather model Global Forecast System (GFS) in predicting wind speed at SGO. Although forecasted wind speeds at 800 hPa do show the diurnal and nocturnal phases, observations at 80 m are strongly underestimated by the model. This causes a pronounced daily cycle of root-mean-squared error (RMSE) and bias in the forecasts. After applying a simple Model Output Statistics (MOS) post-processing, we achieve a good representation of the wind speed intra-daily and inter-daily variability, a first step toward reducing the uncertainties related to potential wind energy projects in the region.

  3. New daily persistent headache.

    PubMed

    Tyagi, Alok

    2012-08-01

    New daily persistent headache (NDPH) is a chronic headache developing in a person who does not have a past history of headaches. The headache begins acutely and reaches its peak within 3 days. It is important to exclude secondary causes, particularly headaches due to alterations in cerebrospinal fluid (CSF) pressure and volume. A significant proportion of NDPH sufferers may have intractable headaches that are refractory to treatment. The condition is best viewed as a syndrome rather than a diagnosis. The headache can mimic chronic migraine and chronic tension-type headache, and it is also important to exclude secondary causes, particularly headaches due to alterations in CSF pressure and volume. A large proportion of NDPH sufferers have migrainous features to their headache and should be managed with treatments used for treating migraine. A small group of NDPH sufferers may have intractable headaches that are refractory to treatment.

  4. Quarrelsomeness in daily life.

    PubMed

    Moskowitz, D S

    2010-02-01

    It is common in studies of interpersonal characteristics to examine personality variables as static predictors. Yet in recent years it has also become possible to examine personality and related interpersonal processes as they unfold over time in association with event specific cues. The present article reviews research that (1) identifies behaviors that reflect the occurrence of hostile-irritable-quarrelsome traits in daily life, (2) demonstrates both the stability and within-person variability of these behaviors over time, (3) documents event-level interpersonal cues that are systematically associated with within-person variation in quarrelsome behavior, and (4) describes how dispositional level agreeableness and irritability moderate the associations of event-level cues with quarrelsome behavior. The influence of the neurotransmitter serotonin on quarrelsome behavior is also considered. The studies indicate that quarrelsome individuals have reduced affective reactivity to engaging in quarrelsome behavior, increased behavioral reactivity to perceptions of quarrelsomeness in others, and greater responsiveness to change in serotonin levels.

  5. The relationships among Muslim Uyghur and Kazakh disabled elders' life satisfaction, activity of daily living, and informal family caregiver's burden, depression, and life satisfaction in far western China: A structural equation model.

    PubMed

    Wang, Wen Ting; He, Bin; Wang, Yu Huan; Wang, Mei Yan; Chen, Xue Feng; Wu, Fu Chen; Yang, Xue

    2017-04-01

    1 Hypothesis Disabled elders' activities of daily living, caregiver burden, caregiver depression, and caregivers' life satisfaction are significantly related to the life satisfaction of elderly people with disability. 2 Hypothesis There are direct and indirect effects between the life satisfaction of elders, disabled elders' activities of daily living, and family caregivers' factors. This study explored the interrelationships of disabled elders' life satisfaction and activities of daily living, caregivers' factors (burden, depression, and life satisfaction) through a structural equation model. In total, 621 dyads of disabled elders and informal family caregivers completed questionnaires during face-to-face interviews in Xinjiang Uyghur Autonomous Region from September 2013 to January 2014. Activity of daily living exerted a direct effect on life satisfaction of disabled elders and 30.4% indirect effect through caregivers' factors. Caregiver burden had a 60.0% direct effect on life satisfaction of disabled elders and a 40.0% indirect effect through the caregiver depression. Caregiver depression showed 76% direct effect on life satisfaction of disabled elders and 24% indirect effect through caregivers' life satisfaction. Direct relationships between activity of daily living and caregiver burden, caregiver burden and caregiver depression, and caregiver depression and caregivers' life satisfaction were observed. Activity of daily living had a 91.3% indirect effect on caregiver depression mediated by caregiver burden; caregiver burden had a 40.0% indirect effect on caregivers' life satisfaction mediated by caregiver depression. Results provide useful information for nurses and policymakers and shed light on the need to consider caregivers' factors in improving care recipients' life satisfaction. © 2017 John Wiley & Sons Australia, Ltd.

  6. Analysis of daily latitude variations

    NASA Technical Reports Server (NTRS)

    Graber, M. A.

    1979-01-01

    The daily latitude measurements of the International Polar Motion Service are analyzed. The annual oscillation in the data was modeled by separate oscillations in each observatory's latitude data. The separate oscillations varied in amplitude from 0.05 sec to 0.15 sec with standard deviations of about 0.007 sec. Within the resolution of the latitude residuals (150 cm), there is no indication of the sharp changes which might be associated with earthquake effects. Then, applying Schuster's test to a periodogram of the residuals indicates that there are probably several processes occurring at amplitudes between 0.007 sec and 0.03 sec whose solution awaits a more precise measurement technique.

  7. Vestibular loss disrupts daily rhythm in rats.

    PubMed

    Martin, T; Mauvieux, B; Bulla, J; Quarck, G; Davenne, D; Denise, P; Philoxène, B; Besnard, S

    2015-02-01

    Hypergravity disrupts the circadian regulation of temperature (Temp) and locomotor activity (Act) mediated through the vestibular otolithic system in mice. In contrast, we do not know whether the anatomical structures associated with vestibular input are crucial for circadian rhythm regulation at 1 G on Earth. In the present study we observed the effects of bilateral vestibular loss (BVL) on the daily rhythms of Temp and Act in semipigmented rats. Our model of vestibular lesion allowed for selective peripheral hair cell degeneration without any other damage. Rats with BVL exhibited a disruption in their daily rhythms (Temp and Act), which were replaced by a main ultradian period (τ <20 h) for 115.8 ± 68.6 h after vestibular lesion compared with rats in the control group. Daily rhythms of Temp and Act in rats with BVL recovered within 1 wk, probably counterbalanced by photic and other nonphotic time cues. No correlation was found between Temp and Act daily rhythms after vestibular lesion in rats with BVL, suggesting a direct influence of vestibular input on the suprachiasmatic nucleus. Our findings support the hypothesis that the vestibular system has an influence on daily rhythm homeostasis in semipigmented rats on Earth, and raise the question of whether daily rhythms might be altered due to vestibular pathology in humans. Copyright © 2015 the American Physiological Society.

  8. Quantification of Daily Physical Activity

    NASA Technical Reports Server (NTRS)

    Whalen, Robert; Breit, Greg; Quintana, Jason

    1994-01-01

    The influence of physical activity on the maintenance and adaptation of musculoskeletal tissue is difficult to assess. Cumulative musculoskeletal loading is hard to quantify and the attributes of the daily tissue loading history affecting bone metabolism have not been completely identified. By monitoring the vertical component of the daily ground reaction force (GRFz), we have an indirect measure of cumulative daily lower limb musculoskeletal loading to correlate with bone density and structure. The objective of this research is to develop instrumentation and methods of analysis to quantify activity level in terms of the daily history of ground reaction forces.

  9. Allosteric Learning Model in English Lesson: Teachers' Views, the Instructions of Curriculum and Course Book, a Sample of Daily Lesson Plan

    ERIC Educational Resources Information Center

    Berkant, Hasan Güner; Baysal, Seda

    2017-01-01

    The changes which occur during the learning process have been explained by many teaching-learning models and theories. One of these models is allosteric learning model (ALM) which was developed by André Giordan in 1989. This model was derived from a biological metaphor related to proteins. The interaction between individual and environment in a…

  10. Temporal disaggregation of daily meteorological grid data

    NASA Astrophysics Data System (ADS)

    Vormoor, K.; Skaugen, T.

    2012-04-01

    For operational flood forecasting, the Norwegian Water Resources and Energy Administration (NVE) applies the conceptual HBV rainfall-runoff model for 117 catchments. The hydrological models are calibrated and run using an extensive meteorological grid data set providing daily temperature and precipitation data back to 1957 for entire Norway at 1x1 km grid resolution (seNorge grids). The daily temporal resolution is dictated by the resolution of historical meteorological data. However, since meteorological forecasts and runoff observations are also available at a much finer than a daily time-resolution (e.g. 6 hourly), and many hydrological extreme events happens at a temporal scale of less than daily, it is important to try to establish a historical dataset of meteorological input at a finer corresponding temporal resolution. We present a simple approach for the temporal disaggregation of the daily meteorological seNorge grids into 6-hour values by consulting a HIRLAM hindcast grid data series with an hourly time resolution and a 10x10 km grid resolution. The temporal patterns of the hindcast series are used to disaggregate the daily interpolated observations from the seNorge grids. In this way, we produce a historical grid dataset from 1958-2010 with 6-hourly temperature and precipitation for entire Norway on a 1x1 km grid resolution. For validation and to see if additional information is gained, the disaggregated data is compared with observed values from selected meteorological stations. In addition, the disaggregated data is evaluated against daily data, simply split into four fractions. The validation results indicate that additional information is indeed gained and point out the benefit of disaggregated data compared to daily data split into four. With regard to temperature, the disaggregated values show very low deviations (MAE, RMSE), and are highly correlated with observed values. Regarding precipitation, the disaggregated data shows cumulative

  11. Nowcasting daily minimum air and grass temperature

    NASA Astrophysics Data System (ADS)

    Savage, M. J.

    2016-02-01

    Site-specific and accurate prediction of daily minimum air and grass temperatures, made available online several hours before their occurrence, would be of significant benefit to several economic sectors and for planning human activities. Site-specific and reasonably accurate nowcasts of daily minimum temperature several hours before its occurrence, using measured sub-hourly temperatures hours earlier in the morning as model inputs, was investigated. Various temperature models were tested for their ability to accurately nowcast daily minimum temperatures 2 or 4 h before sunrise. Temperature datasets used for the model nowcasts included sub-hourly grass and grass-surface (infrared) temperatures from one location in South Africa and air temperature from four subtropical sites varying in altitude (USA and South Africa) and from one site in central sub-Saharan Africa. Nowcast models used employed either exponential or square root functions to describe the rate of nighttime temperature decrease but inverted so as to determine the minimum temperature. The models were also applied in near real-time using an open web-based system to display the nowcasts. Extrapolation algorithms for the site-specific nowcasts were also implemented in a datalogger in an innovative and mathematically consistent manner. Comparison of model 1 (exponential) nowcasts vs measured daily minima air temperatures yielded root mean square errors (RMSEs) <1 °C for the 2-h ahead nowcasts. Model 2 (also exponential), for which a constant model coefficient ( b = 2.2) was used, was usually slightly less accurate but still with RMSEs <1 °C. Use of model 3 (square root) yielded increased RMSEs for the 2-h ahead comparisons between nowcasted and measured daily minima air temperature, increasing to 1.4 °C for some sites. For all sites for all models, the comparisons for the 4-h ahead air temperature nowcasts generally yielded increased RMSEs, <2.1 °C. Comparisons for all model nowcasts of the daily grass

  12. Nowcasting daily minimum air and grass temperature.

    PubMed

    Savage, M J

    2016-02-01

    Site-specific and accurate prediction of daily minimum air and grass temperatures, made available online several hours before their occurrence, would be of significant benefit to several economic sectors and for planning human activities. Site-specific and reasonably accurate nowcasts of daily minimum temperature several hours before its occurrence, using measured sub-hourly temperatures hours earlier in the morning as model inputs, was investigated. Various temperature models were tested for their ability to accurately nowcast daily minimum temperatures 2 or 4 h before sunrise. Temperature datasets used for the model nowcasts included sub-hourly grass and grass-surface (infrared) temperatures from one location in South Africa and air temperature from four subtropical sites varying in altitude (USA and South Africa) and from one site in central sub-Saharan Africa. Nowcast models used employed either exponential or square root functions to describe the rate of nighttime temperature decrease but inverted so as to determine the minimum temperature. The models were also applied in near real-time using an open web-based system to display the nowcasts. Extrapolation algorithms for the site-specific nowcasts were also implemented in a datalogger in an innovative and mathematically consistent manner. Comparison of model 1 (exponential) nowcasts vs measured daily minima air temperatures yielded root mean square errors (RMSEs) <1 °C for the 2-h ahead nowcasts. Model 2 (also exponential), for which a constant model coefficient (b = 2.2) was used, was usually slightly less accurate but still with RMSEs <1 °C. Use of model 3 (square root) yielded increased RMSEs for the 2-h ahead comparisons between nowcasted and measured daily minima air temperature, increasing to 1.4 °C for some sites. For all sites for all models, the comparisons for the 4-h ahead air temperature nowcasts generally yielded increased RMSEs, <2.1 °C. Comparisons for all model nowcasts of the daily grass

  13. REL3.0 LPLA DAILY NC

    Atmospheric Science Data Center

    2016-10-05

    ... 3.0 Langley Parameterized Longwave Model daily Data in 1x1 Degree NetCDF Format News:  LPLA Project ... Temporal Resolution:  3-hourly averaged by day File Format:  NETCDF Tools:  Search and ...

  14. The effect of personality on daily life emotional processes.

    PubMed

    Komulainen, Emma; Meskanen, Katarina; Lipsanen, Jari; Lahti, Jari Marko; Jylhä, Pekka; Melartin, Tarja; Wichers, Marieke; Isometsä, Erkki; Ekelund, Jesper

    2014-01-01

    Personality features are associated with individual differences in daily emotional life, such as negative and positive affectivity, affect variability and affect reactivity. The existing literature is somewhat mixed and inconclusive about the nature of these associations. The aim of this study was to shed light on what personality features represent in daily life by investigating the effect of the Five Factor traits on different daily emotional processes using an ecologically valid method. The Experience Sampling Method was used to collect repeated reports of daily affect and experiences from 104 healthy university students during one week of their normal lives. Personality traits of the Five Factor model were assessed using NEO Five Factor Inventory. Hierarchical linear modeling was used to analyze the effect of the personality traits on daily emotional processes. Neuroticism predicted higher negative and lower positive affect, higher affect variability, more negative subjective evaluations of daily incidents, and higher reactivity to stressors. Conscientiousness, by contrast, predicted lower average level, variability, and reactivity of negative affect. Agreeableness was associated with higher positive and lower negative affect, lower variability of sadness, and more positive subjective evaluations of daily incidents. Extraversion predicted higher positive affect and more positive subjective evaluations of daily activities. Openness had no effect on average level of affect, but predicted higher reactivity to daily stressors. The results show that the personality features independently predict different aspects of daily emotional processes. Neuroticism was associated with all of the processes. Identifying these processes can help us to better understand individual differences in daily emotional life.

  15. Statistical Analysis of daily mean temperatures

    NASA Technical Reports Server (NTRS)

    Ross, D. C.

    1980-01-01

    Data of daily mean temperatures recorded at the Kennedy Center during the period of 1957-1977 were analyzed to forecast daily mean temperatures and their thirty-day moving averages for a period of ten to fifteen days in a given month. Since it is found that the standard deviation is linear in the mean, a logarithmic transformation of the data is used for finding an integrated moving average process IMA by the Box-Jenkins aproach. The first differences of the transformed data seem to fit a moving average model with parameter value 2, MA(2). The consideration of seasonality factor makes the fit worse.

  16. Daily Inter-Annual Simulations of SST and MLD using Atmospherically Forced OGCMs: Model Evaluation in Comparison to Buoy Time Series

    DTIC Science & Technology

    2008-01-30

    verification procedure for analyzing embedded in the dynamical model (Wallcraft et al., the model output is specifically designed for application 2003; Kara...8217E to navy.mil/nmld/nmld.html. 77.21°W. All models have 6 dynamical layers plus the The net surface heat flux that has been absorbed (or mixed layer...unlimited. 13. SUPPLEMENTARY NOTES 14. ABSTRACT A systematic methodology for model-data comparisons of sea surface temperature (SST) and mixed layer

  17. Warmer daily temperatures since 1951

    NASA Astrophysics Data System (ADS)

    Bhattacharya, Atreyee

    2012-09-01

    Days and nights have indeed become warmer over the past 60 years, a new study finds. Although several observation-based studies have shown that daily average temperatures as well as daily maximum and minimum temperatures have increased over the past few decades, controversy has remained as to how the observed trends in extreme and average temperatures are related to each other: Are the warming trends in extreme temperatures a result of a shifting mean climate, or have temperatures become more variable? Using a global observational data set of daily temperatures, Donat and Alexander compared the probability distributions of daily maximum and minimum temperatures over two 30-year periods, 1951-1980 and 1981-2010. The authors show that the maximum and minimum daily temperatures all over the globe have significantly shifted toward higher values during the latter period. They further show that the distributions have become skewed toward the hotter part of the distribution; changes are greater for daily minimum (nighttime) temperatures than for the daily maximum (daytime) temperatures. The authors conclude that the distribution of global daily temperatures has indeed become “more extreme” compared to the middle of the twentieth century. (Geophysical Research Letters, doi:10.1029/2012GL052459, 2012)

  18. A model to approximate lake temperature from gridded daily air temperature records and its application in risk assessment for the establishment of fish diseases in the UK.

    PubMed

    Thrush, M A; Peeler, E J

    2013-10-01

    Ambient water temperature is a key factor controlling the distribution and impact of disease in fish populations, and optimum temperature ranges have been characterised for the establishment of a number important aquatic diseases exotic to the UK. This study presents a simple regression method to approximate daily average surface water temperature in lakes of 0.5-15 ha in size across the UK using 5 km(2) gridded daily average air temperatures provided by the UK Meteorological Office. A Geographic information system (GIS) is used to present thematic maps of relative risk scores established for each grid cell based on the mean number of days per year that water temperature satisfied optimal criteria for the establishment of two economically important pathogens of cyprinid fish (koi herpesvirus (KHV) and spring viraemia of carp virus (SVCV)) and the distribution and density of fish populations susceptible to these viruses. High-density susceptible populations broadly overlap the areas where the temperature profiles are optimal for KHV (central and south-east England); however, few fish populations occur in areas where temperature profiles are most likely to result in the establishment of spring viremia of carp (SVC) (namely northern England and Scotland). The highest grid-cell risk scores for KHV and SVC were 7 and 6, respectively, out of a maximum score of 14. The proportion of grid cells containing susceptible populations with risk scores of 5 or more was 37% and 5% for KHV and SVC, respectively. This work demonstrates a risk-based approach to inform surveillance for exotic pathogens in aquatic animal health management, allowing efficient use of resources directed towards higher risk animals and geographic areas for early disease detection. The methodology could be used to examine the change in distribution of high-risk areas for both exotic and endemic fish diseases under different climate change scenarios. © 2012 Crown copyright Reproduced with the permission

  19. Daily exposure to summer temperatures affects the motile subpopulation structure of epididymal sperm cells but not male fertility in an in vivo rabbit model.

    PubMed

    Maya-Soriano, M J; Taberner, E; Sabés-Alsina, M; Ramon, J; Rafel, O; Tusell, L; Piles, M; López-Béjar, M

    2015-08-01

    High temperatures have negative effects on sperm quality leading to temporary or permanent sterility. The aim of the study was to assess the effect of long exposure to summer circadian heat stress cycles on sperm parameters and the motile subpopulation structure of epididymal sperm cells from rabbit bucks. Twelve White New Zealand rabbit bucks were exposed to a daily constant temperature of the thermoneutral zone (from 18 °C to 22 °C; control group) or exposed to a summer circadian heat stress cycles (30 °C, 3 h/day; heat stress group). Spermatozoa were flushed from the epididymis and assessed for sperm quality parameters at recovery. Sperm total motility and progressivity were negatively affected by high temperatures (P < 0.05), as were also specific motility parameters (curvilinear velocity, linear velocity, mean velocity, straightness coefficient, linearity coefficient, wobble coefficient, and frequency of head displacement; P < 0.05, but not the mean amplitude of lateral head displacement). Heat stress significantly increased the percentage of less-motile sperm subpopulations, although the percentage of the high-motile subpopulation was maintained, which is consistent with the fact that no effect was detected on fertility rates. However, prolificacy was reduced in females submitted to heat stress when inseminated by control bucks. In conclusion, our results suggest that environmental high temperatures are linked to changes in the proportion of motile sperm subpopulations of the epididymis, although fertility is still preserved despite the detrimental effects of heat stress. On the other hand, prolificacy seems to be affected by the negative effects of high temperatures, especially by altering female reproduction. Copyright © 2015 Elsevier Inc. All rights reserved.

  20. Modelling in-stream temperature and dissolved oxygen at sub-daily time steps: an application to the River Kennet, UK.

    PubMed

    Williams, Richard J; Boorman, David B

    2012-04-15

    The River Kennet in southern England shows a clear diurnal signal in both water temperature and dissolved oxygen concentrations through the summer months. The water quality model QUESTOR was applied in a stepwise manner (adding modelled processes or additional data) to simulate the flow, water temperature and dissolved oxygen concentrations along a 14 km reach. The aim of the stepwise model building was to find the simplest process-based model which simulated the observed behaviour accurately. The upstream boundary used was a diurnal signal of hourly measurements of water temperature and dissolved oxygen. In the initial simulations, the amplitude of the signal quickly reduced to zero as it was routed through the model; a behaviour not seen in the observed data. In order to keep the correct timing and amplitude of water temperature a heating term had to be introduced into the model. For dissolved oxygen, primary production from macrophytes was introduced to better simulate the oxygen pattern. Following the modifications an excellent simulation of both water temperature and dissolved oxygen was possible at an hourly resolution. It is interesting to note that it was not necessary to include nutrient limitation to the primary production model. The resulting model is not sufficiently proven to support river management but suggests that the approach has some validity and merits further development. Crown Copyright © 2012. Published by Elsevier B.V. All rights reserved.

  1. Daily rhythms of serotonin metabolism and the expression of clock genes in suprachiasmatic nucleus of rotenone-induced Parkinson's disease male Wistar rat model and effect of melatonin administration.

    PubMed

    Mattam, Ushodaya; Jagota, Anita

    2015-02-01

    The circadian system in suprachiasmatic nucleus (SCN) involves regulated serotonin levels and coordinated expression of various clock genes. To understand circadian disfunction in the age-related neurodegenerative disorder Parkinson's disease (PD), the rotenone-induced PD (RIPD) male Wistar rat model was used. The alterations in the rhythmic dynamic equilibrium of interactions between the various components of serotonin metabolism and the molecular clock were measured. There was significant decrease in the mean 24 h levels of tryptophan, 5-hydroxytryptophan (5-HTP), serotonin (5-HT), N-acetyl serotonin (NAS) and melatonin (MEL) by approximately 63, 51, 76 and 96% respectively ( p ≤ 0.05). However significant increase in 5-methoxy indole acetic acid (5-MIAA), 5-methoxy tryptophol (5-MTOH), 5-hydroxy tryptophol (5-HTOH) indicated increased serotonin catabolism with the abolition of daily rhythms of MEL, 5-HTP and 5-MIAA in RIPD. 24 h mean levels of rPer1, rCry1, rBmal1 reduced by about 0.5, 0.74 and 0.39-fold and increased for rPer2 by about 1.7-fold. The daily pulse of rPer2, rCry1, rCry2 and rBmal1 significantly decreased by 0.36, 0.6, 0.14, 0.1 and 0.2-fold. As melatonin, an antioxidant and an endogenous synchronizer of rhythm declined in RIPD male Wistar rat model, the effects of melatonin-administration on the rhythmic expression of various clock genes were studied. Interestingly, melatonin-administration resulted in restoration of the phase of rPer1 daily rhythm in RIPD indicating differential sensitivity of various clock components towards melatonin. The animals which were administered both rotenone and MEL for 48 days interestingly showed neuroprotective effects in dark phase on correlations between expression of various genes.

  2. Coping Motives, Negative Moods, and Time-to-Drink: Exploring Alternative Analytic Models of Coping Motives as a Moderator of Daily Mood-Drinking Covariation

    PubMed Central

    Littlefield, Andrew K.; Jackson, Kristina M.; Talley, Amelia E.

    2012-01-01

    Affect regulation models of alcohol use posit individuals use alcohol to modify mood states. Importantly, these models hypothesize that individual difference in coping motives for drinking moderate the relation between drinking and negative moods. Despite consistently significant correlations among negative moods, coping motives, and alcohol involvement in numerous between-level studies, within-person analyses have yielded results inconsistent with theoretical models. Analytic techniques modeling time-to-drink have provided results more consistent with theory, though there remains a paucity of research using these methods. The purpose of the current study was to explore whether coping motives moderate the relation between negative moods and the immediacy of drinking using methodology outlined by Hussong (2007) and Armeli, Todd, Conner, and Tennen (2008). Overall, our study showed little evidence for hypothesized mood-motive-alcohol use relations, thus demonstrating that time-to-drink approaches may not provide more consistent support for these hypotheses. PMID:22867813

  3. Modeling daily reference ET in the karst area of northwest Guangxi (China) using gene expression programming (GEP) and artificial neural network (ANN)

    NASA Astrophysics Data System (ADS)

    Wang, Sheng; Fu, Zhi-yong; Chen, Hong-song; Nie, Yun-peng; Wang, Ke-lin

    2016-11-01

    Nonlinear complexity is a characteristic of hydrologic processes. Using fewer model parameters is recommended to reduce error. This study investigates, and compares, the ability of gene expression programming (GEP) and artificial neural network (ANN) techniques in modeling ET0 by using fewer meteorological parameters in the karst area of northwest Guangxi province, China. Over a 5-year period (2008-2012), meteorological data consisting of maximum and minimum air temperature, relative humidity, wind speed, and sunshine duration were collected from four weather stations: BaiSe, DuAn, HeChi, and RongAn. The ET0 calculated by the FAO-56 PM equation was used as a reference to evaluate results for GEP, ANN, and Hargreaves models. The coefficient of determination ( R 2) and the root mean square error (RMSE) were used as statistical indicators. Evaluations revealed that GEP, and ANN, can be used to successfully model ET0. In most cases, when using the same input variables, ANN models were superior to GEP. We then established ET0 equations with fewer parameters under various conditions. GEP can produce simple explicit mathematical formulations which are easier to use than the ANN models.

  4. Daily exercise prevents diastolic dysfunction and oxidative stress in a female mouse model of western diet induced obesity by maintaining cardiac heme oxygenase-1 levels.

    PubMed

    Bostick, Brian; Aroor, Annayya R; Habibi, Javad; Durante, William; Ma, Lixin; DeMarco, Vincent G; Garro, Mona; Hayden, Melvin R; Booth, Frank W; Sowers, James R

    2017-01-01

    Obesity is a global epidemic with profound cardiovascular disease (CVD) complications. Obese women are particularly vulnerable to CVD, suffering higher rates of CVD compared to non-obese females. Diastolic dysfunction is the earliest manifestation of CVD in obese women but remains poorly understood with no evidence-based therapies. We have shown early diastolic dysfunction in obesity is associated with oxidative stress and myocardial fibrosis. Recent evidence suggests exercise may increase levels of the antioxidant heme oxygenase-1 (HO-1). Accordingly, we hypothesized that diastolic dysfunction in female mice consuming a western diet (WD) could be prevented by daily volitional exercise with reductions in oxidative stress, myocardial fibrosis and maintenance of myocardial HO-1 levels. Four-week-old female C57BL/6J mice were fed a high-fat/high-fructose WD for 16weeks (N=8) alongside control diet fed mice (N=8). A separate cohort of WD fed females was allowed a running wheel for the entire study (N=7). Cardiac function was assessed at 20weeks by high-resolution cardiac magnetic resonance imaging (MRI). Functional assessment was followed by immunohistochemistry, transmission electron microscopy (TEM) and Western blotting to identify pathologic mechanisms and assess HO-1 protein levels. There was no significant body weight decrease in exercising mice, normalized body weight 14.3g/mm, compared to sedentary mice, normalized body weight 13.6g/mm (p=0.38). Total body fat was also unchanged in exercising, fat mass of 6.6g, compared to sedentary mice, fat mass 7.4g (p=0.55). Exercise prevented diastolic dysfunction with a significant reduction in left ventricular relaxation time to 23.8ms for exercising group compared to 33.0ms in sedentary group (p<0.01). Exercise markedly reduced oxidative stress and myocardial fibrosis with improved mitochondrial architecture. HO-1 protein levels were increased in the hearts of exercising mice compared to sedentary WD fed females. This

  5. The patient with daily headaches.

    PubMed

    Maizels, Morris

    2004-12-15

    The term "chronic daily headache" (CDH) describes a variety of headache types, of which chronic migraine is the most common. Daily headaches often are disabling and may be challenging to diagnose and treat. Medication overuse, or drug rebound headache, is the most treatable cause of refractory daily headache. A pathologic underlying cause should be considered in patients with recent-onset daily headache, a change from a previous headache pattern, or associated neurologic or systemic symptoms. Treatment of CDH focuses on reduction of headache triggers and use of preventive medication, most commonly anti-depressants, antiepileptic drugs, and beta blockers. Medication overuse must be treated with discontinuation of symptomatic medicines, a transitional therapy, and long-term prophylaxis. Anxiety and depression are common in patients with CDH and should be identified and treated. Although the condition is challenging, appropriate treatment of patients with CDH can bring about significant improvement in the patient's quality-of-life.

  6. An empirical model to estimate daily forest fire smoke exposure over a large geographic area using air quality, meteorological, and remote sensing data

    PubMed Central

    Yao, Jiayun; Henderson, Sarah B

    2014-01-01

    Exposure to forest fire smoke (FFS) is associated with a range of adverse health effects. The British Columbia Asthma Medication Surveillance (BCAMS) product was developed to detect potential impacts from FFS in British Columbia (BC), Canada. However, it has been a challenge to estimate FFS exposure with sufficient spatial coverage for the provincial population. We constructed an empirical model to estimate FFS-related fine particulate matter (PM2.5) for all populated areas of BC using data from the most extreme FFS days in 2003 through 2012. The input data included PM2.5 measurements on the previous day, remotely sensed aerosols, remotely sensed fires, hand-drawn tracings of smoke plumes from satellite images, fire danger ratings, and the atmospheric venting index. The final model explained 71% of the variance in PM2.5 observations. Model performance was tested in days with high, moderate, and low levels of FFS, resulting in correlations from 0.57 to 0.83. We also developed a method to assign the model estimates to geographical local health areas for use in BCAMS. The simplicity of the model allows easy application in time-constrained public health surveillance, and its sufficient spatial coverage suggests utility as an exposure assessment tool for epidemiologic studies on FFS exposure. PMID:24301352

  7. An empirical model to estimate daily forest fire smoke exposure over a large geographic area using air quality, meteorological, and remote sensing data.

    PubMed

    Yao, Jiayun; Henderson, Sarah B

    2014-01-01

    Exposure to forest fire smoke (FFS) is associated with a range of adverse health effects. The British Columbia Asthma Medication Surveillance (BCAMS) product was developed to detect potential impacts from FFS in British Columbia (BC), Canada. However, it has been a challenge to estimate FFS exposure with sufficient spatial coverage for the provincial population. We constructed an empirical model to estimate FFS-related fine particulate matter (PM2.5) for all populated areas of BC using data from the most extreme FFS days in 2003 through 2012. The input data included PM2.5 measurements on the previous day, remotely sensed aerosols, remotely sensed fires, hand-drawn tracings of smoke plumes from satellite images, fire danger ratings, and the atmospheric venting index. The final model explained 71% of the variance in PM2.5 observations. Model performance was tested in days with high, moderate, and low levels of FFS, resulting in correlations from 0.57 to 0.83. We also developed a method to assign the model estimates to geographical local health areas for use in BCAMS. The simplicity of the model allows easy application in time-constrained public health surveillance, and its sufficient spatial coverage suggests utility as an exposure assessment tool for epidemiologic studies on FFS exposure.

  8. Calibration and validation of the SWAT model for predicting daily ET over irrigated crops in the Texas High Plains using lysimetric data

    USDA-ARS?s Scientific Manuscript database

    The Soil Water Assessment Tool (SWAT) is a widely used watershed model for simulating stream flow, overland flow, sediment, pesticide, and bacterial loading in response to management practices. All SWAT processes are directly dependent upon the accurate representation of hydrology. Evapotranspiratio...

  9. Calibration and validation of the SWAT model for predicting daily ET for irrigated crops in the Texas High Plains using lysimetric data

    USDA-ARS?s Scientific Manuscript database

    The Soil and Water Assessment Tool (SWAT) model has been used to assess the impacts of alternative agricultural management practices on non-point source pollution in watersheds of various topography and scale throughout the world. Water balance is the driving force behind all processes of SWAT, as i...

  10. Improving ozone modeling in complex terrain at a fine grid resolution - Part II: Influence of schemes in MM5 on daily maximum 8-h ozone concentrations and RRFs (Relative Reduction Factors) for SIPs in the non-attainment areas

    NASA Astrophysics Data System (ADS)

    Kim, Yunhee; Fu, Joshua S.; Miller, Terry L.

    2010-06-01

    Part II presents a comprehensive evaluation of CMAQ for August of 2002 on twenty-one sensitivity simulations (detailed in Part I) in MM5 to investigate the model performance for O 3 SIPs (State Implementation Plans) in the complex terrain. CMAQ performance was quite consistent with the results of MM5, meaning that accurate meteorological fields predicted in MM5 as an input resulted in good model performance of CMAQ. In this study, PBL scheme plays a more important role than its land surface models (LSMs) for the model performance of CMAQ. Our results have shown that the outputs of CMAQ on eighteen sensitivity simulations using two different nudging coefficients for winds (2.5 and 4.5 × 10 -4 s -1, respectively) tend to under predict daily maximum 8-h ozone concentrations at valley areas except the TKE PBL sensitivity simulations (ETA M-Y PBL scheme with Noah LSMs and 5-layer soil model and Gayno-Seaman PBL) using 6.0 × 10 -4 s -1 with positive MB (Mean Bias). At mountain areas, none of the sensitivity simulations has presented over predictions for 8-h O 3, due to relatively poor meteorological model performance. When comparing 12-km and 4-km grid resolutions for the PX simulation in CMAQ statistics analysis, the CMAQ results at 12-km grid resolution consistently show under predictions of 8-h O 3 at both of valley and mountain areas and particularly, it shows relatively poor model performance with a 15.1% of NMB (Normalized Mean Bias). Based on our sensitivity simulations, the TKE PBL sensitivity simulations using a maximum value (6 × 10 -4) among other sensitivity simulations yielded better model performance of CMAQ at all areas in the complex terrain. As a result, the sensitivity of RRFs to the PBL scheme may be considerably significant with about 1-3 ppb in difference in determining whether the attainment test is passed or failed. Furthermore, we found that the result of CMAQ model performance depending on meteorological variations is affected on estimating

  11. Influence of climate model biases and daily-scale temperature and precipitation events on hydrological impacts assessment: A case study of the United States

    SciTech Connect

    Ashfaq, Moetasim; Bowling, Laura C.; Cherkauer, Keith; Pal, Jeremy; Diffenbaugh, Noah

    2010-01-01

    The Intergovernmental Panel on Climate Change's Fourth Assessment Report concludes that climate change is now unequivocal, and associated increases in evaporation and atmospheric water content could intensify the hydrological cycle. However, the biases and coarse spatial resolution of global climate models limit their usefulness in hydrological impact assessment. In order to reduce these limitations, we use a high-resolution regional climate model (RegCM3) to drive a hydrological model (variable infiltration capacity) for the full contiguous United States. The simulations cover 1961-1990 in the historic period and 2071-2100 in the future (A2) period. A quantile-based bias correction technique is applied to the times series of RegCM3-simulated precipitation and temperature. Our results show that biases in the RegCM3 fields not only affect the magnitude of hydrometeorological variables in the baseline hydrological simulation, but they also affect the response of hydrological variables to projected future anthropogenic increases in greenhouse forcing. Further, we find that changes in the intensity and occurrence of severe wet and hot events are critical in determining the sign of hydrologic change. These results have important implications for the assessment of potential future hydrologic changes, as well as for developing approaches for quantitative impacts assessment.

  12. Daily Interpersonal and Affective Dynamics in Personality Disorder

    PubMed Central

    Wright, Aidan G.C.; Hopwood, Christopher J.; Simms, Leonard J.

    2015-01-01

    In this naturalistic study we adopt the lens of interpersonal theory to examine between-and within-person differences in dynamic processes of daily affect and interpersonal behaviors among individuals (N = 101) previously diagnosed with personality disorders who completed daily diaries over the course of 100 days. Dispositional ratings of interpersonal problems and measures of daily stress were used as predictors of daily shifts in interpersonal behavior and affect in multilevel models. Results indicate that ~40%–50% of the variance in interpersonal behavior and affect is due to daily fluctuations, which are modestly related to dispositional measures of interpersonal problems but strongly related to daily stress. The findings support conceptions of personality disorders as a dynamic form of psychopathology involving the individuals interacting with and regulating in response to the contextual features of their environment. PMID:26200849

  13. The Daily Curriculum Guide, Year II, Weeks 1-10.

    ERIC Educational Resources Information Center

    Dissemination and Assessment Center for Bilingual Education, Austin, TX.

    Spanning two years, the program set forth in the Daily Curriculum Guide for preschool Spanish-speaking children is essentially a language maintenance model in which Spanish is used as a means to develop basic concepts, skills and attitudes. This guide gives daily lesson plans for the first ten weeks of the second year. Each lesson, written in…

  14. The Daily Curriculum Guide, Year II, Weeks 1-10.

    ERIC Educati