The efficiency evaluation of the railway lines performance is done through a set of indicators and criteria, the most important are transport density, the productivity of enrollee, passenger vehicle production, the productivity of freight wagon, and the productivity of locomotives. This study includes an attempt to calculate the most important of these indicators which transport density index from productivity during the four indicators, using artificial neural network technology. Two neural networks software are used in this study, (Simulnet) and (Neuframe), the results of second program has been adopted. Training results and test to the neural network data used in the study, which are obtained from the international in

A total of 28 birds were examined to investigate about the distribution of the nematode Hadjelia truncata among some members of the avian family Columbidae in Al-Diwaniya Province, Central Iraq. The percentages of the infection rate with this nematode were 27.27, 37.5, 14.28 and 0 in Columba livia, C. palumbis, Streptopelia decaocto, and S. turtur respectively. Reporting Hadjelia truncata from Streptopelia decaocto constitutes a new host record.

Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, F

Microbiological contamination by fungi impacts the quality and safety of wheat grain storage. This study aimed to evaluate the efficacy of cold plasma in restricting the growth of the fungus, Aspergillus niger, which was isolated from wheat grains. A dielectric barrier discharge (DBD) operating at atmospheric pressure generated cold plasma that was used to treat the fungus, and the impact of this treatment was investigated at various periods  1, 2, 4, 6, and 15 minutes. The results revealed a highly significant decrease in the growth and number of spores of Aspergillus niger compared to the controls. This study revealed an efficient technique for enhancing wheat grain storage that could be a foundation for further large-scale studies.

In this research, the focus was on estimating the parameters on (min- Gumbel distribution), using the maximum likelihood method and the Bayes method. The genetic algorithmmethod was employed in estimating the parameters of the maximum likelihood method as well as  the Bayes method. The comparison was made using the mean error squares (MSE), where the best  estimator  is the one who has the least mean squared error. It was noted that the best estimator was (BLG_GE).

This paper aims to decide the best parameter estimation methods for the parameters of the Gumbel type-I distribution under the type-II censorship scheme. For this purpose, classical and Bayesian parameter estimation procedures are considered. The maximum likelihood estimators are used for the classical parameter estimation procedure. The asymptotic distributions of these estimators are also derived. It is not possible to obtain explicit solutions of Bayesian estimators. Therefore, Markov Chain Monte Carlo, and Lindley techniques are taken into account to estimate the unknown parameters. In Bayesian analysis, it is very important to determine an appropriate combination of a prior distribution and a loss function. Therefore, two different