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The current study aimed to establish and describe the basic pharmacokinetics of a single dose of gliclazide 60 mg modified release formulation manufactured by Ranbaxy Laboratories Limited, now Sun Pharmaceutical Industries Limited, India. Ten mathematical models were created for analyzing the experimental data resulted from two bioequivalence studies conducted at Clinical Pharmacology and Pharmacokinetics Department of Terapia S.A. Model discrimination was done using the Akaike index value and it was observed that model number 10 (M10) is the model that best describes the gliclazide disposition. This model has two compartments (peripheral and central), mixed order absorption process (1st and zero order kinetics) with a lag time of around 3 hrs and 1st order kinetics of elimination. Applying this model in Phoenix software version 6.3 (Pharsight Corp., Mountain view, CA, USA) the main pharmacokinetic parameters of gliclazide were also computed. [ABSTRACT FROM AUTHOR]

The widespread increase in global temperature is driving more frequent and more severe local heatwaves within the contiguous United States (CONUS). General circulation models (GCMs) show increasing, but spatially uneven trends in heatwave properties. However, the wide range of model outputs raises the question of the suitability of this method for indicating the future impacts of heatwaves on human health and well-being. This work examines the fitness of 32 models from CMIP5 and their ensemble median to predict a set of heatwave descriptors across the CONUS, by analyzing their capabilities in the simulation of historical heatwaves during 1950–2005. Then, we use a multi-criteria decision-making tool and rank the overall performance of each model for 10 locations with different climates. We found GCMs have different capabilities in the simulation of historical heatwave characteristics. In addition, we observed similar performances for GCMs over the areas with a partially similar climate. The ensemble model showed better performance in simulation of historical heatwave intensity in some locations, while other individual GCMs represented heatwave time-related components more similar to observations. These results are a step towards the use of contemporary weather models to guide heatwave impact predictions.

Knowledge about soil moisture dynamics and their relation with rainfall, evapotranspiration, and soil physical properties is fundamental for understanding the hydrological processes in a region. Given the difficulties of measurement and the scarcity of surface soil moisture data in some places such as Northeast Brazil, modelling has become a robust tool to overcome such limitations. This study investigated the dynamics of soil water content in two plots in the Gameleira Experimental River Basin, Northeast Brazil. For this, Time Domain Reflectometry (TDR) probes and Hydrus-1D for modelling one-dimensional flow were used in two stages: with hydraulic parameters estimated with the Beerkan Estimation of Soil Transfer Parameters (BEST) method and optimized by inverse modelling. The results showed that the soil water content in the plots is strongly influenced by rainfall, with the greatest variability in the dry–wet–dry transition periods. The modelling results were considered satisfactory with the data estimated by the BEST method (Root Mean Square Errors, RMSE = 0.023 and 0.022 and coefficients of determination, R2 = 0.72 and 0.81) and after the optimization (RMSE = 0.012 and 0.020 and R2 = 0.83 and 0.72). The performance analysis of the simulations provided strong indications of the efficiency of parameters estimated by BEST to predict the soil moisture variability in the studied river basin without the need for calibration or complex numerical approaches.

This paper explains the current costs characterization in broiler meat production under the economic conditions of Malaysia using mathematical and statistical techniques. State of the art of econometrical modelling currently used for regulatory decision-develop in the cost productivity model are described. This study examined the effects of the interaction between the main factors. Five variables considered in this study in which it has been divided into two types of variables which are dependent and independent variables. Four independent variables considered are the price of chick, starter fertilizer prices, chicken grower fertilizer prices and prices of chicken medicines. The multiple regression models involve the interaction up to the third level and considering 32 potential models. Each model was tested with multicollinearity test and test coefficient with the objective of removing variables that were not significant. Concluded in this study that mathematical modelling using Eight Selection Criteria (8SC) would improve economic profitability of broiler productivity. [ABSTRACT FROM AUTHOR]

There are a lot of investigations to select the best model to estimate potential evapotranspiration (ET o ) in a certain climate or region. In this paper, the types of climate include arid, semiarid, Mediterranean, and very humid. A spatial and temporal study of the ET o is the aim of this paper, according to the peak and low events (extreme events) and climate change alarms. For this purpose, 50 years (1961–2010) monthly meteorological data of 18 regions in Iran, with various climates, were collected. For estimating the ET o , 5 temperature−based, 5 radiation−based, and 5 mass transfer−based models, were selected with respect to better performance of them in different climates on the basis of past investigations. The results will especially be useful in the regions where the monthly (rather than daily) meteorological data are available. The results appear that the Blaney−Criddle (BC) (root mean square error (RMSE) = 1.32 mm day −1 ) and Abtew (Ab) (RMSE = 0.83 mm day −1 ) are the best models for estimating the ET o in the arid and semiarid regions, respectively. While, modified Hargreaves−Samani 2 (MHS2) represents the best performance in the Mediterranean and very humid regions (RMSE = 0.30 mm day −1 & 0.68 mm day −1 , respectively). In addition, radiation—and mass transfer−based models are proper tools to estimate the ET o in warm and cold seasons on the basis of improving values of evaluation indices in 40% and 70% of the study area, respectively. Increasing air temperature and decreasing minimum relative humidity for best performance of most models alarms a climate change in most regions of Iran. As a result, the radiation−based models were adapted with climate change better than the temperature−based and particularly mass transfer−based models. Finally, a step by step flowchart was presented for selecting the best model to estimate the ET o in each climate. [ABSTRACT FROM AUTHOR]

Poisson regression was used to analyze the count data which Poisson distributed. Poisson regression analysis requires state equidispersion, in which the mean value of the response variable is equal to the value of the variance. However, there are deviations in which the value of the response variable variance is greater than the mean. This is called overdispersion. If overdispersion happens and Poisson Regression analysis is being used, then underestimated standard errors will be obtained. Negative Binomial Regression can handle overdispersion because it contains a dispersion parameter. From the simulation data which experienced overdispersion in the Poisson Regression model it was found that the Negative Binomial Regression was better than the Poisson Regression model.

Small scale hydraulic experiments are widely used to model the stability of a scour protection around an offshore monopile foundation. Knowing the associated scale effects is important for evaluating the validity of the obtained experimental and design results. This paper provides a quantification analysis of scale effects that exist in monopile scour protection experiments, with a focus on the shear damage of a dynamically stable scour protection. Large scale models (scale ratio 1:8.33 and 1:16.67) and similar small scale models (scale ratio 1:50) have been adopted in the experiments with waves against current hydrodynamic conditions applied. For the waves and current conditions, the Froude scaling rule is used; for the armour stones, the so-called Best Model scaling rule suggested by Hughes (1993) is used. The scaling scheme achieves similarities between large and small scale models with regard to Shields number, relative density, geometry and settling velocity of particle. The scour protection damage patterns are measured and the three dimensional damage numbers are analysed. For better comparing the small and large scale test results, the small scale tests are performed repeatedly to obtain reliable damage results with associated range of deviation. Visual assessment of the damage patterns shows some agreements between small and large scale tests with regard to the damage and accretion locations. However, detailed analysis shows that the small scale tests introduce higher global and subarea damage numbers compared to large scale tests. The damage areas in small scale tests are larger than that in large scale tests. Significant lee-side damage due to the presence of lee-wake vortices is found in small scale tests. The dissimilarities of pile Reynolds number (R e , D P ), ratio between Shields numbers ( θ m a x / θ c r ) and vortex shedding frequency in the different scaled models are believed to be the primary reasons of obtained scale effects. • Scale effects in monopile scour protection experiments are investigated quantitatively. • Large scale (1:8.33 and 1:16.67) and small scale (1:50) experiments are performed under wave opposing current conditions. • Scour protection damages are analysed and compared between small and large scale experiments. • Time scale of scour protection progressive damage development is investigated. • Different Reynolds numbers (R e , D P ) and ratios between Shields numbers ( θ m a x / θ c r ) are the main reasons for the scale effects. [ABSTRACT FROM AUTHOR]

The purpose of this study was to estimate the genetic parameters for birth weight of Karayaka lambs by separating direct genetic, maternal genetic, and maternal permanent environmental effects. Records of 1013 Karayaka lambs born between 2005 and 2010 were analyzed. Six different animal models were examined, all including direct additive genetic variance and various combinations of genetic and environmental maternal effects. The most appropriate model was chosen based on log-likelihood ratio tests. Since model 1 had the smallest likelihood value, it was chosen as the best model in this study. Depending on the model, direct heritability varied from 0.37 to 0.55 and maternal heritability ranged from 0.08 to 0.20 for birth weight. [ABSTRACT FROM AUTHOR]

Estimating the relative importance of habitat loss and fragmentation is necessary to estimate the potential benefits of specific management actions and to ensure that limited conservation resources are used efficiently. However, estimating relative effects is complicated because the two processes are highly correlated. Previous studies have used a wide variety of statistical methods to separate their effects and we speculated that the published results may have been influenced by the methods used. We used simulations to determine whether, under identical conditions, the following 7 methods generate different estimates of relative importance for realistically correlated landscape predictors: residual regression, model or variable selection, averaged coefficients from all supported models, summed Akaike weights, classical variance partitioning, hierarchical variance partitioning, and a multiple regression model with no adjustments for collinearity. We found that different methods generated different rankings of the predictors and that some metrics were strongly biased. Residual regression and variance partitioning were highly biased by correlations among predictors and the bias depended on the direction of a predictor's effect (positive vs. negative). Our results suggest that many efforts to deal with the correlation between amount and fragmentation may have done more harm than good. If confounding effects are controlled and adequate thought is given to the ecological mechanisms behind modeled predictors, then standardized partial regression coefficients are unbiased estimates of the relative importance of amount and fragmentation, even when predictors are highly correlated. [ABSTRACT FROM AUTHOR]

Independence of observations is one of the basic assumptions of the analysis of variance. Performed randomizations prevent results from being biased in cases when independence is violated. The objective of the present paper is to find out the predominant shape of spatial relationships in Polish wheat variety testing trials. One of the possibilities is to apply some geo-statistical method ( Cressie Noel 1993 , Grondona and Cressie 1991 ). Such an approach is used in this paper. Using the results of nearly 200 trials on wheat varieties, conducted either in generalized lattice (GL) designs ( Patterson and Hunter 1983 ) or in incomplete split-block designs, the empirical values of semivariance have been calculated. Residuals were computed from a model with fixed effects for varieties and replicates, ignoring incomplete blocks, then 10 different geo-statistical models have been fitted to empirical semivariances. With spatial models convergence problems occurred in some cases. The linear and bounded-linear models were the ones most often successfully fitted. Inclusion of revealed relationships into classical models for incomplete blocks did not improve substantially the effectiveness of analyses. [ABSTRACT FROM AUTHOR]

The widespread increase in global temperature is driving more frequent and more severe local heatwaves within the contiguous United States (CONUS). General circulation models (GCMs) show increasing, but spatially uneven trends in heatwave properties. However, the wide range of model outputs raises the question of the suitability of this method for indicating the future impacts of heatwaves on human health and well-being. This work examines the fitness of 32 models from CMIP5 and their ensemble median to predict a set of heatwave descriptors across the CONUS, by analyzing their capabilities in the simulation of historical heatwaves during 1950–2005. Then, we use a multi-criteria decision-making tool and rank the overall performance of each model for 10 locations with different climates. We found GCMs have different capabilities in the simulation of historical heatwave characteristics. In addition, we observed similar performances for GCMs over the areas with a partially similar climate. The ensemble model showed better performance in simulation of historical heatwave intensity in some locations, while other individual GCMs represented heatwave time-related components more similar to observations. These results are a step towards the use of contemporary weather models to guide heatwave impact predictions. [ABSTRACT FROM AUTHOR]

Knowledge about soil moisture dynamics and their relation with rainfall, evapotranspiration, and soil physical properties is fundamental for understanding the hydrological processes in a region. Given the difficulties of measurement and the scarcity of surface soil moisture data in some places such as Northeast Brazil, modelling has become a robust tool to overcome such limitations. This study investigated the dynamics of soil water content in two plots in the Gameleira Experimental River Basin, Northeast Brazil. For this, Time Domain Reflectometry (TDR) probes and Hydrus-1D for modelling one-dimensional flow were used in two stages: with hydraulic parameters estimated with the Beerkan Estimation of Soil Transfer Parameters (BEST) method and optimized by inverse modelling. The results showed that the soil water content in the plots is strongly influenced by rainfall, with the greatest variability in the dry–wet–dry transition periods. The modelling results were considered satisfactory with the data estimated by the BEST method (Root Mean Square Errors, RMSE = 0.023 and 0.022 and coefficients of determination, R2 = 0.72 and 0.81) and after the optimization (RMSE = 0.012 and 0.020 and R2 = 0.83 and 0.72). The performance analysis of the simulations provided strong indications of the efficiency of parameters estimated by BEST to predict the soil moisture variability in the studied river basin without the need for calibration or complex numerical approaches. [ABSTRACT FROM AUTHOR]