This paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback

The quality of groundwater should be improved by keeping safe water sources from contaminants in protective way by doing regular measuring and checkup before it supplied for usage. Private Wells do not receive the same services that wells supplying the public do. Well owners are responsible for protecting their drinking water. This work was carried out in Badra city, Iraq from December 2017 to May 2018, six wells water were investigated to determine the general characteristics of wells as well as studying the effect of environmental factors on the quality of water. The average of six wells were eleven parameters that is out of permissible limits were EC, Sal., Alk., TH, TDS, Na, Ca, Cl, SO₄, Fe, Zn (4402-5183 /cm, 2.76-3.9 ppt

The density-based spatial clustering for applications with noise (DBSCAN) is one of the most popular applications of clustering in data mining, and it is used to identify useful patterns and interesting distributions in the underlying data. Aggregation methods for classifying nonlinear aggregated data. In particular, DNA methylations, gene expression. That show the differentially skewed by distance sites and grouped nonlinearly by cancer daisies and the change Situations for gene excretion on it. Under these conditions, DBSCAN is expected to have a desirable clustering feature i that can be used to show the results of the changes. This research reviews the DBSCAN and compares its performance with other algorithms, such as the tradit

This study aimed to evaluate oral health (OH) and periodontal diseases (PD) awareness in the Iraqi population.

Modifying of HY/Zeolite is by loading nickel for applying catalyst in thermal catalytic cracking of furfural extract-40 from the lubricating base oil unit. The study involved the characterizing of HY-zeolite and promoted catalyst with nickel by X-ray diffraction analysis, Scanning electron microscopy (SEM), BET (Brunauer, Emmett, and Teller), and infrared ray analyses FTIR. The catalytic thermal cracking tubular reactor with a fixed bed with two type catalysts; HY/zeolite and Ni HY/zeolite, individually at a temperature of 580^oC with LHSV 5h^-1 was investigated. The results indicated that increase the conversion of catalytic cracking of furfural extract-40 also increases the yield of useful petroleum

Background: Investigating dental health knowledge among children is important. Knowing what behaviors are right in relation to dental health does not guarantee that children will practice those behaviors. However, lack of knowledge and misconceptions about dental health may lead to behaviors that are harmful to teeth and gum. Baseline data on knowledge levels are required to determine which particular areas of dental health education are in need of improvement for high-risk children living in different geographical areas. This research was conducted to study the oral health status, dental knowledge and behavior in relation to two different cities, among children in Baghdad and Thamar (republic of Yemen) governorate. Materia

Chemical Methodologies (CHEMM)