The quality of groundwater in the Al-Hawija area was assessed using a water quality index. Data of nine physico-chemical parameters of 28 groundwater wells were used to calculate the water quality index (WQI). A heterogeneous water quality was reported, where in close proximity to the Lesser Zab River (LZR), it has low WQI values and permissible for human consumptions due to the dilution processes by fresh water; whereas, it becomes deteriorated in areas located far away the river. The values of WQI ranges from 22 to 336, indicating a good to very poor groundwater quality.

Wisconsin Breast Cancer Dataset (WBCD) was employed to show the performance of the Adaptive Resonance Theory (ART), specifically the supervised ART-I Artificial Neural Network (ANN), to build a breast cancer diagnosis smart system. It was fed with different learning parameters and sets. The best result was achieved when the model was trained with 50% of the data and tested with the remaining 50%. Classification accuracy was compared to other artificial intelligence algorithms, which included fuzzy classifier, MLP-ANN, and SVM. We achieved the highest accuracy with such low learning/testing ratio.

   The natural radioactivity levels in water samples along the Tigris river (one of the major rivers of the world) within Baghdad city were investigated to determine and evaluate the radioactivity risks in the water of the river. The specific activity of the radionuclides (²³⁸U, ²³²Th, ⁴⁰K, and ¹³⁷Cs) for thirty different water samples from Tigris river within Baghdad city were measured using gamma-ray spectrometer, employing a NaI(Tl) scintillation detector. The results showed that the average value of the specific activity for ²³⁸U, ²³²Th, ⁴⁰K, and ¹³⁷Cs were (24.20, 16.70, 329.22, and 19.40) Bq/l, respectively. The calculated average annual effec

This aim of this study is to assess the Tigris River sediments and utilize them as a new abrasive for the preparation of polished surface of magnetite ore to be studied under reflected light ore microscope. Such polishing process was tested using 250, 125, 71, 45, 25 and 18μm grain sizes of the river sediments. For the completion of the polishing and to obtain a glossy perfect polished surface, the 7 and 2.5 μm sized standard diamond pastes were used. After each polishing stage, the reflectance and roughness of these surfaces were measured as an evaluation step for the polishing efficiency. The reflectance values (R%) of the magnetite surface were found to be reversely proportioned to the abrasive grain size; while the surface roughnes

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 matter of handwritten text recognition is as yet a major challenge to mainstream researchers. A few ways deal with this challenge have been endeavored in the most recent years, for the most part concentrating on the English pre-printed or handwritten characters space. Consequently, the need to effort a research concerning to Arabic texts handwritten recognition. The Arabic handwriting presents unique technical difficulties because it is cursive, right to left in writing and the letters convert its shapes and structures when it is putted at initial, middle, isolation or at the end of words. In this study, the Arabic text recognition is developed and designed to recognize image of Arabic text/characters. The proposed model gets a single l

The turning process has various factors, which affecting machinability and should be investigated. These are surface roughness, tool life, power consumption, cutting temperature, machining force components, tool wear, and chip thickness ratio. These factors made the process nonlinear and complicated. This work aims to build neural network models to correlate the cutting parameters, namely cutting speed, depth of cut and feed rate, to the machining force and chip thickness ratio. The turning process was performed on high strength aluminum alloy 7075-T6. Three radial basis neural networks are constructed for cutting force, passive force, and feed force. In addition, a radial basis network is constructed to model the chip thickness ratio. T

This study focusing on river water quality as a source of raw water, efficiencies of water treatment plants, the goodness of transporting water pipes in the water networks and drinking water quality as consumed within certain city residential sectors, in addition to seasonal variation. The following water quality parameters were studied: Electrical Conductivity, Turbidity, Dissolved oxygen, Biochemical oxygen demand, the concentration of nutrients like nitrogen and phosphate, the residual chlorine in addition to total bacterial count and total colon bacteria of raw water, and treated water of four selected water treatment plants in Mosul city. These plants are : the right united scheme, the new left scheme ,the old left scheme and the Gh