Rheumatoid arthritis (AR) is one of the chronic diseases resulting in many complications such as cardiovascular disease (CVD). Any change in the lipid profiles and myocardial markers indicates cardiovascular disease risk, so this study is designed to monitor the pattern of lipid profiles and myocardial markers in newly diagnosed RA patients. Blood samples were collected from 70 Iraqi patients newly diagnosed with rheumatoid arthritis (male and female) and 30 healthy served as control. These individuals were aged 35-65 years. The serum samples were obtained to determine myocardial markers; included troponin, creatinine kinase (CK), lactate dehydrogenase (LDH), and glutamic oxaloacetic transaminase GOT; and lipid profiles; such as choleste

Heavy metals are currently of much environmental concern. The contamination by heavy metals in plants and water is one of the major issues to be faced throughout the world. This research is tried to estimate levels of heavy metals in vegetative crops and soil irrigated with well water (as alternative source for irrigation). Samples of well water, soil and vegetative crops were collected from agriculture fields at Al-Dora district in Baghdad. Physico-chemical parameters (pH, EC, TDS and Salinity) were measured for water and (pH, EC and salinity) for soil samples. Estimation of Lead (Pb), Nickle (Ni), Zinc (Zn) and Iron (Fe) concentrations in water, vegetable crops (Raphanus sativus, Apium graveoleus, Beta vulgaris, Allium ampeloprasum, Le

Missing data is one of the problems that may occur in regression models. This problem is usually handled by deletion mechanism available in statistical software. This method reduces statistical inference values because deletion affects sample size. In this paper, Expectation Maximization algorithm (EM), Multicycle-Expectation-Conditional Maximization algorithm (MC-ECM), Expectation-Conditional Maximization Either (ECME), and Recurrent Neural Networks (RNN) are used to estimate multiple regression models when explanatory variables have some missing values. Experimental dataset were generated using Visual Basic programming language with missing values of explanatory variables according to a missing mechanism at random general pattern and s

in this work the polymides were prepared as rthemally stable polymers by diffrent ways

In this paper, Bayes estimators of Poisson distribution have been derived by using two loss functions: the squared error loss function and the proposed exponential loss function in this study, based on different priors classified as the two different informative prior distributions represented by erlang and inverse levy prior distributions and non-informative prior for the shape parameter of Poisson distribution. The maximum likelihood estimator (MLE) of the Poisson distribution has also been derived. A simulation study has been fulfilled to compare the accuracy of the Bayes estimates with the corresponding maximum likelihood estimate (MLE) of the Poisson distribution based on the root mean squared error (RMSE) for different cases of the

As one type of resistance furnace, the electrical tube furnace (ETF) typically experiences input noise, measurement noise, system uncertainties, unmodeled dynamics and external disturbances, which significantly degrade its temperature control performance. To provide precise, and robust temperature tracking performance for the ETF, a robust composite control (RCC) method is proposed in this paper. The overall RCC method consists of four elements: First, the mathematical model of the ETF system is deduced, then a state feedback control (SFC) is constructed. Third, a novel disturbance observer (DO) is designed to estimate the lumped disturbance with one observer parameter. Moreover, the stability of the closed loop system including controller

In this study, we investigate the behavior of the estimated spectral density function of stationary time series in the case of missing values, which are generated by the second order Autoregressive (AR (2)) model, when the error term for the AR(2) model has many of continuous distributions. The Classical and Lomb periodograms used to study the behavior of the estimated spectral density function by using  the simulation.

In general, the importance of cluster analysis is that one can evaluate elements by clustering multiple homogeneous data; the main objective of this analysis is to collect the elements of a single, homogeneous group into different divisions, depending on many variables. This method of analysis is used to reduce data, generate hypotheses and test them, as well as predict and match models. The research aims to evaluate the fuzzy cluster analysis, which is a special case of cluster analysis, as well as to compare the two methods—classical and fuzzy cluster analysis. The research topic has been allocated to the government and private hospitals. The sampling for this research was comprised of 288 patients being treated in 10 hospitals. As t

larization modulation plays an important role in polarization encoding in quantum key distribution. By using polarization modulation, quantum key distribution systems become more compact and more vulnerable as one laser source is used instead of using multiple laser sources that may cause side-channel attacks. Metasurfaces with their exceptional optical properties have led to the development of versatile ultrathin optical devices. They are made up of planar arrays of resonant or nearly resonant subwavelength pieces and provide complete control over reflected and transmitted electromagnetic waves opening several possibilities for the development of innovative optical components. In this work, the Si nanowire metasurface