Persistent organic pollutants distribution in plasma lipoprotein fractions

This study determines the associations among serum lipid profiles, risk of cardiovascular disease, and persistent organic pollutants. Using Gas chromatography technique, the intensity of toxic pollutant residues in serum samples of Hypertensive patients were measured. Based on statistical analysis, the effects of different covariates namely pesticides, age, systolic blood pressure, diastolic blood pressure, and lipid profile duration was checked using the logistic regression model. Statistical computation was performed on SPSS 22.0. The P-values of F-Statistic for each lipid profile class are greater than 0.01 (1%), therefore we cannot reject the null hypothesis for all cases. The estimated coefficients, their standard errors, Wald Statistic, and odds ratio of the binary logistic regression model for different lipid profile parameters indicate if pesticides increase then the logit value of different lipid profile parameters changes from -0.46 to -0.246 except LDL which increases by 0.135. The study reports a significantly increased threat of cardiovascular disease with increased concentrations of toxic pollutants.

Effect of optimal filtering parameters for autoregressive model AR(p) on motor unit action potential signal

Error is one element of the autoregressive (AR) model, which is supposed to be white noise. Correspondingly assumption that white noise error is a normal distribution in electromyography (EMG) estimation is one of the common causes for error maximization. This paper presents the effect of a suitable choice of filtering function based on the non-invasive analysis properties of motor unit action potential signal, extracted from a non-invasive method-the high spatial resolution (HSR) electromyography (EMG), recorded during low-level isometric muscle contractions. The final prediction error procedure is used to find the number of parameters in the model. The error signal parameter, the simulated deviation from the actual signals, is suitably filtered to obtain optimally appropriate estimates of the parameters of the automatic regression model. It is filtered to acquire optimally appropriate estimates of the parameters of the automatic regression model. Then appropriate estimates of spectral power shapes are obtained with a high degree of efficiency compared with the robust method under investigation. Extensive experiment results for the proposed technique have shown that it provides a robust and reliable calculation of model parameters. Moreover, estimates of power spectral profiles were evaluated efficiently.

Comparison of Different Count Models for Investigation of Some Environmental Factors Affecting Stillbirth in Holsteins

Background: The objective of this study is comparing different count data models for stillbirth data. In modeling this type of data, Poisson regression or alternative models can be preferred. Methods: The poisson, negative binomial, zero-inflated poisson, zero-inflated negative binomial, poisson-logit hurdle and negative binomial-logit hurdle regressions were compared and used to examine the effects of the gender, parity and herd-year-season independent variables on stillbirth. Furthermore, the Log-Likelihood statistics, Akaike Information Criteria, Bayesian Information Criteria and rootogram graphs were used as comparison criteria for performance of the models. According to these criteria, Negative Binomial-Logit Hurdle Regression model was chosen as the best model. Result: The parameter estimates obtained by Negative Binomial-Logit Hurdle Regression model in relation to the effects of the gender, parity and herd-year-season independent variables on stillbirth were found to be significant (p less than 0.01). It was found that while stillbirth incidence was higher in males than females, it was found to decrease as the parity increased. As a result, the Negative Binomial Logit Hurdle model was found the best model for stillbirth count data with overdispersion.

A Linear Regression Model for THD Sensitivity in PV Based Microgrid with Varying Insolation Levels

Solar PV systems can be used for powering small microgrids in rural area of developing countries. Generally, a solar power microgrid consists of a PV array, an MPPT, a dc-dc converter and an inverter, particularly as the general loads are A.C in nature. In a PV system, reactive current, unbalancing in currents, and harmonics are generated due to the power electronics-based converters as well as nonlinear loads (computers induction motors etc). Thus, estimation of the harmonics levels measured by the Total Harmonic Distortion (THD) is an essential aspect of performance assessment of a solar powered microgrid. A major issue that needs to be examined is the impact of PV system control parameters on the THD. In this paper, we take up this assessment for a small PV based rural microgrid with varying levels of solar irradiance. A Simulink model has been developed for the study from which the THD at equilibrium conditions is estimated. This data is in turn used to design a generalized Linear Regression Model, which can be used to observe the sensitivity of three control variables on the magnitude of the THD. These variables are: Solar Irradiance levels, Power Factor (PF) of connected load magnitude of the connected load (in kVA) The results obtained show that the greatest sensitivity is obtained for load kVA variation.

A Statistical Intra-Seasonal Prediction Model of Extended Boreal Summer Western North Pacific Tropical Cyclone Genesis

A L2 regularized logistic regression model is developed in this study to predict weekly tropical cyclone (TC) genesis over the western North Pacific (WNP) and sub-regions of the WNP including the South China Sea (SCS), the western WNP (WWNP), and the eastern WNP (EWNP). The potential predictors for the TC genesis model include a time-varying TC genesis climatology, the Madden-Julian oscillation (MJO), the quasi-biweekly oscillation (QBWO), and ENSO. The relative importance of the predictors in a constructed L2 regression model is justified by a forward stepwise selection procedure for each region from a 0-week to a 7-week lead. Cross-validated hindcasts are then generated for the corresponding prediction schemes out to a 7-week lead. The TC genesis climatology generally improves the regional model skill, while the importance of intra-seasonal oscillations and ENSO are regionally dependent. Over the WNP, there is increased model skill over the time-varying climatology in predicting weekly TC genesis out to a 4-week lead by including the MJO and QBWO, while ENSO has a limited impact. On a regional scale, ENSO and then the MJO and QBWO respectively, are the two most important predictors over the EWNP and WWNP after the TC genesis climatology. The MJO is found to be the most important predictor over the SCS. The logistic regression model is shown to have comparable reliability and forecast skill scores to the ECMWF dynamical model on intra-seasonal time scales.