Polyaniline organic Semiconductor polymer thin films have been prepared by oxidative polymerization at room temperature, this polymer was deposited on glass substrate with thickness 900nm, FTIR spectra was tested , the structural,optical and electrical properties were studied through XRD ,UV-Vis ,IR measurements ,the results was appeared that polymer thin film sensing to NH3 gas.

Polyacetal was synthesized from the reaction of PVA with para-methyoxy benzaldehyde. Polymer metal complexwas prepared by reaction with Cu, polymer blend with Chitosan was prepared through the technique of solution casting method.All prepared compounds have been characterized through FT-IR, DSC, SEM as well as the Biological activity. The FT-IR results indicated the formation of polyacetal. The DSC results indicated the thermal stability regarding prepared polymer, polymermetal complex and Chitosan polymer blends. Antibacterial potential related to synthesized polyacetal, its metal complex andChitosan blend against four types of bacteria namely, Staphylococcus aureas, Psedomonas aeruginosa, Bacillus subtilis, Escherichia coli was examined a

Carbon Nanopowder was fabricated by arc discharge technique at deposition pressure of 10-5 mbar Argon gas on glass substrates. The prepared carbon nano- powder was collected from chamber and purified with nitric acid at 323K .The morphology and crystalline structure of the prepared powder was examined by X-Ray Diffraction (XRD), Atomic Force Microscope (AFM), and Scanning Electron Microscope (SEM). XRD spectrums showed that the powder exhibits amorphous structure and after purification, the powder showed hexagonal structure with a preferential orientation along(002) direction ,where AFM and SEM gave very compatible estimation on the grain size and shape of the nanopowder.

The possibility of using activated carbon developed from date palm seeds wastes as a permeable reactive barrier (PRB) to remove copper from polluted shallow groundwater was investigated. The activated carbon has been developed from date palm seeds by dehydrating methods using concentrated sulfuric acid. Batch tests were performed to characterize the equilibrium sorption properties of new activated carbon in copper-containing aqueous solutions, while the sandy soil (aquifer) was assumed to be inert. Under the studied conditions, the Langmuir isotherm model gives a better fit for the sorption data of copper by activated carbon than other models. At a pilot scale, One-dimensional column experiments were performed, and an integrated model ba

Hydrate dissociation equilibrium conditions for carbon dioxide + methane with water, nitrogen + methane with water and carbon dioxide + nitrogen with water were measured using cryogenic sapphire cell. Measurements were performed in the temperature range of 275.75 K–293.95 K and for pressures ranging from 5 MPa to 25 MPa. The resulting data indicate that as the carbon dioxide concentration is increased in the gas mixture, the gas hydrate equilibrium temperature increases. In contrast, by increasing the nitrogen concentration in the gas mixtures containing methane or carbon dioxide decreased the gas hydrate equilibrium temperatures. Furthermore, the cage occupancies for the carbon dioxide + methane system were evaluated using the Van der Wa

Background/Objectives: The purpose of this study was to classify Alzheimer’s disease (AD) patients from Normal Control (NC) patients using Magnetic Resonance Imaging (MRI). Methods/Statistical analysis: The performance evolution is carried out for 346 MR images from Alzheimer's Neuroimaging Initiative (ADNI) dataset. The classifier Deep Belief Network (DBN) is used for the function of classification. The network is trained using a sample training set, and the weights produced are then used to check the system's recognition capability. Findings: As a result, this paper presented a novel method of automated classification system for AD determination. The suggested method offers good performance of the experiments carried out show that the

Analyzing sentiment and emotions in Arabic texts on social networking sites has gained wide interest from researchers. It has been an active research topic in recent years due to its importance in analyzing reviewers' opinions. The Iraqi dialect is one of the Arabic dialects used in social networking sites, characterized by its complexity and, therefore, the difficulty of analyzing sentiment. This work presents a hybrid deep learning model consisting of a Convolution Neural Network (CNN) and the Gated Recurrent Units (GRU) to analyze sentiment and emotions in Iraqi texts. Three Iraqi datasets (Iraqi Arab Emotions Data Set (IAEDS), Annotated Corpus of Mesopotamian-Iraqi Dialect (ACMID), and Iraqi Arabic Dataset (IAD)) col