Smart water flooding (low salinity water flooding) was mainly invested in a sandstone reservoir. The main reasons for using low salinity water flooding are; to improve oil recovery and to give a support for the reservoir pressure.

In this study, two core plugs of sandstone were used with different permeability from south of Iraq to explain the effect of water injection with different ions concentration on the oil recovery. Water types that have been used are formation water, seawater, modified low salinity water, and deionized water.

The effects of water salinity, the flow rate of water injected, and the permeability of core plugs have been studied in order to summarize the best conditions of low salinity

Worldwide, shipping documents are still primarily created and handled in the traditional paper manner. Processes taking place in shipping ports as a result are time-consuming and heavily dependent on paper. Shipping documents are particularly susceptible to paperwork fraud because they involve numerous parties with competing interests. With the aid of smart contracts, a distributed, shared, and append-only ledger provided by blockchain technology allows for the addition of new records. In order to increase maritime transport and port efficiency and promote economic development, this paper examines current maritime sector developments in Iraq and offers a paradigm to secure the management system based on a hyper-ledger fabric blockchain p

Natural settings make it challenging to identify facial expressions since head position, illumination level, and ‎‎occlusion vary. Thus, developing a more generic model without front-facing images alone is quite crucial. This ‎research proposes a facial expression ‎recognition model based on pre-trained deep convolutional neural networks ‎with transfer learning. The model was trained ‎on several cases to classify face expressions into seven ‎classifications efficiently. The proposed system used the EfficientNetB0 model ‎that has one dense dropout layer. The model first rescales and norms the input dataset in the input ‎layer that takes images of a larger resolution to get better results. After entering 7 blocks sequential

Electrospinning is a novel technique that can be used to produce highly porous fibers with highly tunable properties. In this research, this technique is adopted to prepare the electrospun nanofiber membrane for membrane distillation application. A custom-built electrospinning setup was made to prepare the nanofibers membrane. Polyvinylidene fluoride (PVDF) polymer was used in the electrospinning process due to its high hydrophobicity. Electrospun (PVDF) nanofibers were tested in direct contact membrane distillation (DCMD) process using 0.6 M sodium chloride as a feed solution. The resulting nanofiber membrane exhibited high performance in DCMD (i.e. relatively high water flux and high salt rejection). It has been found

In this research, we did this qualitative and quantitative study in order to improve the assay of aspirin colorimetrically using visible spectrophotometer. This method depends on aqueous hydrolysis of aspirin and then treating it with the ferric chloride acidic solution to give violet colored complex with salicylic acid, as a result of aspirin hydrolysis, which has a maximum absorption at 530nm. This procedure was applied to determine the purity of aspirin powder and tablet. The results were approximately comparative so that the linearity was observed in the high value of both correlation coefficient (R= 0.998) and Determination Coefficient or Linearity (R2= 0.996) while the molar absorpitivity was 1.3× 103 mole

In this research Artificial Neural Network (ANN) technique was applied to study the filtration process in water treatment. Eight models have been developed and tested using data from a pilot filtration plant, working under different process design criteria; influent turbidity, bed depth, grain size, filtration rate and running time (length of the filtration run), recording effluent turbidity and head losses. The ANN models were constructed for the prediction of different performance criteria in the filtration process: effluent turbidity, head losses and running time. The results indicate that it is quite possible to use artificial neural networks in predicting effluent turbidity, head losses and running time in the filtration process, wi