irrigation use at many stations along the Euphrates River inside the Iraqi lands and to try to correlate the results with the satellite image analyses for the purpose of making a colored model for the Euphrates that can be used to predict the quality classifications of the river for irrigation use at any point along the river. The Bhargava method was used to calculate the water quality index for irrigation use at sixteen stations along the river from its entrance to the Iraqi land at Al-Qaim in Anbar governorate to its union with the Tigris River at Qurna in Basrah governorate. Coordinates of the sixteen stations of the Euphrates River were projected at the mosaic of Iraq satellite image which was taken from LANDSAT satellite for bands 1, 2

    This research study the effect of surface modification and copper (Cu) plating carbon fiber (CF) surface on the thermal stability and wettability of carbon fiber (CF)/epoxy (EP) composites. The TGA result indicates that the thermal-stability of carbon fiber may be enhanced after Cu coating CF. TGA curve showed that the treatment temperature was enhanced thermal stability of Ep/CF, this is due to the oxidation during heating. The Cu plating increased the thermal conductivity, this increase might be due to reduce in contact resistance at the interface due to chemical modification and copper plating and tunneling resistance.

   The increase of surface polarity after coating cause decreas

         This study involves the design of 24 mixtures of fiber reinforced magnetic reactive powder concrete containing nano silica. Tap water was used for 12 of these mixtures, while magnetic water was used for the others. The nano silica (NS) with ratios (1, 1.5, 2, 2.5 and 3) % by weight of cement, were used for all the mixtures. The results have shown that the mixture containing 2.5% NS gives the highest compressive strength at age 7 days. Many different other tests were carried out, the results have shown that the carbon fiber reinforced magnetic reactive powder concrete containing 2.5% NS (CFRMRPCCNS) had higher compressive strength, modulus of rupture, splitting tension, str

In this research an Artificial Neural Network (ANN) technique was applied for the prediction of Ryznar Index (RI) of the flowing water from WTPs in Al-Karakh side (left side) in Baghdad city for year 2013. Three models (ANN1, ANN2 and ANN3) have been developed and tested using data from Baghdad Mayoralty (Amanat Baghdad) including drinking water quality for the period 2004 to 2013. The results indicate that it is quite possible to use an artificial neural networks in predicting the stability index (RI) with a good degree of accuracy. Where ANN 2 model could be used to predict RI for the effluents from Al-Karakh, Al-Qadisiya and Al-Karama WTPs as the highest correlation coefficient were obtained 92.4, 82.9 and 79.1% respe

Compounds were prepared from In2O3 doped SnO2 with different doping ratio by mixing and sintering at 1000oC. Pulsed Laser Deposition PLD was used to deposit thin films of different doping ratio In2O3: SnO2 (0, 1, 3, 5, 7 and 9 % wt.) on glass and p-type wafer Si(111) substrates at ambient temperature under vacuum of 10-3 bar thickness of ~100nm. X-ray diffraction and atomic force microscopy were used to examine the structural type, grain size and morphology of the prepared thin films. The results show the structures of thin films was also polycrystalline, and the predominate peaks are identical with standard cards ITO. On the other side the prepared thin films declared a reduction of degree of crystallinity with the increase of doping ra

Functionalized-multi wall carbon nanotubes (F-MWCNTs) and functionalized-single wall carbon nanotubes (F-SWCNTs) were well enhanced using CoO Nanoparticles. The sensor device consisted of a film of sensitive material (F-MWCNTs/CoONPs) and (F-SWCNTs/CoO NPs) deposited by drop- casting on an n-type porous silicon substrate. The two sensors perform high sensitivity to NO₂ gas at room temperatures. The analysis indicated that the (F-MWCNTs/CoONPs) have a better performance than (F-SWCNTs/CoONPs). The F-SWCNTs/CoONPs gas sensor shows high sensitivity (19.1 %) at RT with response time 17 sec, while F-MWCNTs/CoONPs gas sensor show better sensitivity (39 %) at RT with response time 13 sec. The device shows a very reproducible sensor p