Liquefied petroleum gas (LPG), Natural gas (NG) and hydrogen were all used to operate spark ignition internal combustion engine Ricardo E6. A comparison of CO emissions emitted from each case, with emissions emitted from engine fueled with gasoline as a fuel is conducted.

The study was accomplished when engine operated at HUCR for gasoline n(8:1), was compared with its operation at HUCR for each fuel. Compression ratio, equivalence ratio and spark timing were studied at constant speed 1500 rpm.

CO concentrations were little at lean ratios; it appeared to be effected a little with equivalence ratio in this side, at rich side its values became higher, and it appeared to be effected by equivalence ratio highly, the results s

This study includes the application of non-parametric methods in estimating the conditional survival function of the Beran method using both the Nadaraya-Waston and the Priestley-chao weights and using data for Interval censored and Right censored of breast cancer and two types of treatment, Chemotherapy and radiation therapy Considering age is continuous variable, through using (MATLAB)  use of the (MSE) To compare weights The results showed a superior weight (Nadaraya-Waston) in estimating the survival function and condition of Both for chemotherapy and radiation therapy.

                     Crises affect the economic growth of the country in general and the banking sector in particular. Given the importance of this sector, it is necessary to evaluate the performance of banks from time to time and monitor the liquidity of each bank and how to organize its management to overcome any crisis or emergency situation that may befall it. The research seeks to diagnose the level of impact of crisis management. In the liquidity of Iraqi private banks, using liquidity indicators, as the research community represents the banks listed in the Iraq Stock Exchange As for the research sample, it is represented by (10) pri

Estimation of the unknown parameters in 2-D sinusoidal signal model can be considered as important and difficult problem. Due to the difficulty to find estimate of all the parameters of this type of models at the same time, we propose sequential non-liner least squares method and sequential robust  M method after their development through the use of sequential  approach in the estimate suggested by Prasad et al to estimate unknown frequencies and amplitudes for the 2-D sinusoidal compounds but depending on Downhill Simplex Algorithm in solving non-linear equations for the purpose of obtaining non-linear parameters estimation which represents frequencies and then use of least squares formula to estimate

Abstract:

 This research aims to compare Bayesian Method and Full Maximum Likelihood to estimate hierarchical Poisson regression model.

The comparison was done by  simulation  using different sample sizes (n = 30, 60, 120) and different Frequencies (r = 1000, 5000) for the experiments as was the adoption of the  Mean Square Error to compare the preference estimation methods and then choose the best way to appreciate model and concluded that hierarchical Poisson regression model that has been appreciated Full Maximum Likelihood Full Maximum Likelihood  with sample size  (n = 30) is the best to represent the maternal mortality data after it has been reliance value param

As we know the transportation studies regarded as one of a very
important and difficult studies and one of its difficulties created from the
process of data updating therefore the researcher well facing many difficulties
to balancing between the old data on collecting new data.
The research present an opinion which is summarized by: can we use
the old data after we updated and used it as alternatives? Or the researcher
must collect new data to complete their research which indicate to the present
situation and some times they cant complete their studies because of the
security, economic, temporally difficulties.
The research used two kinds of data, the old data which belong to the
period (1998) and new data

This research a study model of linear regression problem of autocorrelation of random error is spread when a normal distribution as used in linear regression analysis for relationship between variables and through this relationship can predict the value of a variable with the values of other variables, and was comparing methods (method of least squares, method of the average un-weighted, Thiel method and Laplace method) using the mean square error (MSE) boxes and simulation and the study included fore sizes of samples (15, 30, 60, 100). The results showed that the least-squares method is best, applying the fore methods of buckwheat production data and the cultivated area of the provinces of Iraq for years (2010), (2011), (2012),

Abstract:

The models of time series often suffer from the problem of the existence of outliers ​​that accompany the data collection process for many reasons, their existence may have a significant impact on the estimation of the parameters of the studied model. Access to highly efficient estimators  is one of the most important stages of statistical analysis, And it is therefore important to choose the appropriate methods to obtain good  estimators. The aim of this research is to compare the ordinary estimators and the robust estimators of the estimation of the parameters of

Neuroendocrine differentiation has been mentioned in many cancers of non-neuroendocrinal organs, involving the gastrointestinal tract. In contrast, the correlation of focally diffused neuroendocrine differentiation in colorectal adenocarcinoma with neuroendocrine cell hyperplasia has not been somewhat reported. The objective of this research is to study the relationship between neuroendocrine cell hyperplasia and neuroendocrine differentiation in colorectal adenocarcinoma and to find the correlation of neuroendocrine differentiation and VEGF expression with clinicopathological parameters of colorectal adenocarcinoma. Methods employed in the current study were including eighty-one patients with colorectal cancer. Formalin fixed paraffin e

Linear regression is one of the most important statistical tools through which it is possible to know the relationship between the response variable and one variable (or more) of the independent variable(s), which is often used in various fields of science. Heteroscedastic is one of the linear regression problems, the effect of which leads to inaccurate conclusions. The problem of heteroscedastic may be accompanied by the presence of extreme outliers in the independent variables (High leverage points) (HLPs), the presence of (HLPs) in the data set result unrealistic estimates and misleading inferences. In this paper, we review some of the robust