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Diabetes mellitus, known as diabetes, is a chronic disease that affects the control of blood glucose concentration levels, it is a disease that mostly affects adults (type 2 diabetes), but it can also occur in children (type 1 or childhood diabetes), as well as in pregnant women (gestational diabetes). Diabetes is one of the diseases with the highest prevalence and high mortality worldwide. Diabetes has no cure, but continuous monitoring to maintain blood glucose levels in normal ranges reduces the possibility of suffering from gastrointestinal problems, vision loss, limb amputations (such as diabetic foot) and damage to vital organs such as the heart and kidneys, among other associated complications. This article compares the results in glucose estimation by using a linear, quadratic and cubic regression considering the electrical characteristics generated in the cardiac conduction (HR, HRV, T-wave peak, and QT interval) recorded on a single-lead electrocardiogram (VII), used as a non-invasive blood glucose estimation model. The best estimate was obtained using a cubic regression. The validation was performed using the Clarke grid having 77.78 % of data in the A zone and 22.22 % in the B zone and a Pearson correlation value of 0.94103 in the cubic regression.

In this paper, synthesized organic compound of the family of ketene dithioacetal was studied as corrosion inhibitors for mild steel in 1.0 M hydrochloric acid by gravimetric measurement. The aim of this work is to study the effect of inhibitor concentration and the temperature on the resistance, corrosion and to compare the experimental results with mathematical models. The structural properties were performed using scanning electron microscopy technique. It has been found that the inhibition efficiency, increases with increasing inhibitor concentration. The adsorption of studied compounds on mild steel surface follows Langmuir’s isotherm. Obtained data were analyzed by suggesting two mathematical models based on linear and quadratic regression, taking into account the influence of concentration and temperature on the inhibition efficiency. Obtained experimental data were in good agreement with those predicated by the quadratic model

The analysis of frequencies of extreme hydrological data such as floods, droughts, winds and maximum daily precipitation, is based on accepting that the maximum annual data of the available sample are independent and come from a random process that is stationary. This means that their statistical properties do not vary with time. Due to changes in land use and impacts of global warming, the hydrological data series show trends, indicating that they are non-stationary. The objective of this study was to expose the generalization of the L moments method, to estimate the fit parameters of the probability distribution functions: General Extreme Values (GVE), Generalized Logistics (LOG) and Generalized Pareto (PAG) of non-stationary type, by varying with time (t) its location parameter (u) in a linear and quadratic way. The probabilistic models GVE1, LOG1 and PAG1 have four fitting parameters (δ1, δ2, a, k), since u = δ1 + δ2·t and their scale (a) and form (k) parameters are constant. The models GVE2, LOG2 and PAG2 have five fitting parameters (δ1, δ2, δ3, a, k), due to the fact that u = δ1 + δ2·t + δ3·t². In series with trend it is used as a covariate t in years, but indicators of regional or global climate variability can also be used, such as the southern oscillation index. By means of the four numerical applications that are described, the simplicity of the operating procedure was demonstrated and the utility of the use of non-stationary GVE, LOG and PAG models is highlighted through their predictions in series with trend, or using a climatic covariate. [ABSTRACT FROM AUTHOR]

Testing how well Taylor's law (TL) describes spatial variation of the population density of a species requires grouping sampling areas (patches of habitat) into blocks so that a mean and a variance of the population density can be calculated over the patches in each block. The relationship between specific groupings and TL remains largely unknown. Here, using tree counts from a deciduous forest, we studied the effect of four biological methods of grouping sampling areas into blocks on the form and parameters of TL. Regardless of the method of grouping, the species-specific basal area densities obeyed TL, and the estimated slopes were not significantly different from one grouping method to another. Surprisingly, TL remained valid when four kinds of randomizations were performed to the biological groupings and tree census. These randomizations randomly assigned sampling areas to blocks, and/or randomized the species composition within or across sampling areas. We found that the form of TL was robust to different grouping methods and species randomizations, but its parameter values depended significantly on species compositions at sampling areas. [ABSTRACT FROM AUTHOR]

A pedigree file consisting of 5860 individuals, 167 sires and 1582 dams collected at Makooei sheep breeding station (MSBS) during a period of 24 years (1990 to 2013) was used to calculate the inbreeding coefficients to reveal any probable effects of inbreeding (F) on the studied traits. The studied traits were classified to the five main groups including body weight, Kleiber ratio, body measurements, greasy fleece weight and reproductive traits. The inbreeding coefficient among the individuals ranged from 0 to 25% with an average of 0.33%, and ranged from 0 to 25% among the ewes with an average of 0.21%. Fluctuations in individual and maternal inbreeding were observed in the period under study. The average generation interval was calculated as 3.6 years. The effective population size of the flock was 51.8 animals. The rate of inbreeding was 0.08% per year and 0.53% per generation. Six different models were applied and likelihood ratio test (LRT) was used to select the appropriate model. Based on the LRT, model II was selected as appropriate model for growth, greasy fleece weight at 6 months of age and kleiber ratio traits. The traits related to the live body weight were affected negatively (P<0.001) by the quadratic individual F. The linear individual regression coefficient of the Kleiber ratio was estimated significantly (P<0.01) as 0.001, as well the linear maternal inbreeding regression was estimated significantly (P<0.05) as 0.0002. In general, body measurements were affected negatively by the quadratic effects of inbreeding (both individual and maternal) and positively by the linear effects of inbreeding. The reproductive traits were studied based on ewe inbreeding coefficient. Among the reproductive traits, the quadratic regression coefficient of conception rate was estimated significantly (P<0.05) as -0.22. Quadratic regression coefficient of litter mean weight per lamb born were estimated significantly (P<0.01) as -0.63 per 0.01 change in the ewe inbreeding coefficient. Therefore, inbreeding should be avoided, except for purposes of genetic breeding whose main objective is the fixation of certain alleles in the population. [ABSTRACT FROM AUTHOR]

This critical review paper will discuss the main analytical calibration models as well as the guidelines for their practical use. The main models used to fit a multiple-point calibration dataset are: 1) linear unweighted or ordinary least squares regression (OLSR); 2) quadratic unweighted least squares regression (QLSR); 3) linear weighted least squares regression (WLSR). Unfortunately, there is no standard procedure in analytical chemistry for objectively testing the goodness-of-fit of calibration models. Different proposals were reported in the literature. However, none is more commonly used, and probably not more controversial than R 2 . In this document, a three step simple calibration diagnosis has been proposed. It is based on a combination of different procedures such as graphical plots, statistical significance tests and numerical parameters. Experimental conditions and design of calibration procedures are very relevant for appropriate selection. Finally, some information on the choice of the different models will be reported in four case studies. [Display omitted] • OLSR, QLSR and WLSR are the main analytical calibration models. • Graphical plots, statistical tests and numerical parameters are used to evaluate GoF of calibration models. • %RE values are useful to evaluate bias and therefore to appropriately select the calibration model. • Model selection is dependent on experimental conditions and design of calibration procedures. [ABSTRACT FROM AUTHOR]