This work deals with separation of the aromatic hydrocarbons benzene, toluene, and xylene (BTX) from reformate. The separation was examined using adsorption by molecular sieve zeolite 13X in a fixed bed process. The concentration of aromatic hydrocarbons in the influent and effluent streams was measured using gas chromatography. The effect of flow rate and bed length of adsorbent on the adsorption of multicomponent hydrocarbons and adsorption capacity of molecular sieve was studied. The tendency of aromatic hydrocarbons adsorption from reformate is in the order: benzene >toluene>xylenes.

A total of 37 Staphylococcus epidermidis isolates, isolated from corneal scraping of patients with bacterial keratitis and 20 isolates from healthy eyes (as control) (all isolates, isolated from, Ibn Al- Haietham eye hospital / Baghdad), were tested for slime production, 52.63% of all isolates were positive-slime production (23 isolates from patients and 7 isolates from controls). It was found that positive-slime producing S. epidermidis were exhibited a high resistance to antibiotics as compared to negative-slime producing isolates.

This work studies the performance of zeolite permeable reactive barrier in removing cadmium from a contaminated shallow aquifer. Batch tests have been performed to characterize the equilibrium sorption properties of the zeolite in cadmium-containing aqueous solutions. A 1D numerical finite difference model has been developed to describe pollutant transport within groundwater taking pollutant sorption on the permeable reactive barrier (PRB), which is performed by Langmuir equation, into account. Numerical results show that the PRB starts to saturate after a period of time (~120 h) due to reduction of the retardation factor, indicating a decrease in the percentage of zeolite functionality. However, a reasonable agreement between model predict

This research includes the study of dual data models with mixed random parameters, which contain two types of parameters, the first is random and the other is fixed. For the random parameter, it is obtained as a result of differences in the marginal tendencies of the cross sections, and for the fixed parameter, it is obtained as a result of differences in fixed limits, and random errors for each section. Accidental bearing the characteristic of heterogeneity of variance in addition to the presence of serial correlation of the first degree, and the main objective in this research is the use of efficient methods commensurate with the paired data in the case of small samples, and to achieve this goal, the feasible general least squa

Iraq suffers the continuing lack of water resources in generdwether it is surface or underearth water or rain. The study of rain has got the utmost importance in order to the rain direction in Iraq and in Mosul in particular and what it will be in future. It also shows the wet as well as the dry seasons and the possibility of expecting them and expecting their quantities in order to invest them and to keep this vital resource The research deals with predict the wet and dry rainy seasons in Mosul using (SPI) Standardized precipitation index extracted from conversion of Gamma distribution to standardized normal distribution , depending on data of monthly rain amounts for 1940-2013 . Results showed existence of 31 w

The consensus algorithm is the core mechanism of blockchain and is used to ensure data consistency among blockchain nodes. The PBFT consensus algorithm is widely used in alliance chains because it is resistant to Byzantine errors. However, the present PBFT (Practical Byzantine Fault Tolerance) still has issues with master node selection that is random and complicated communication. The IBFT consensus technique, which is enhanced, is proposed in this study and is based on node trust value and BLS (Boneh-Lynn-Shacham) aggregate signature. In IBFT, multi-level indicators are used to calculate the trust value of each node, and some nodes are selected to take part in network consensus as a result of this calculation. The master node is chosen

Support vector machines (SVMs) are supervised learning models that analyze data for classification or regression. For classification, SVM is widely used by selecting an optimal hyperplane that separates two classes. SVM has very good accuracy and extremally robust comparing with some other classification methods such as logistics linear regression, random forest, k-nearest neighbor and naïve model. However, working with large datasets can cause many problems such as time-consuming and inefficient results. In this paper, the SVM has been modified by using a stochastic Gradient descent process. The modified method, stochastic gradient descent SVM (SGD-SVM), checked by using two simulation datasets. Since the classification of different ca

The simulation of passively Q-switching is four non – linear first order differential equations. The optimization of passively Q-switching simulation was carried out using the constrained Rosenbrock technique. The maximization option in this technique was utilized to the fourth equation as an objective function; the parameters, γa, γc and β as were dealt with as decision variables. A FORTRAN program was written to determine the optimum values of the decision variables through the simulation of the four coupled equations, for ruby laser Q–switched by Dy +2: CaF2.For different Dy +2:CaF2 molecules number, the values of decision variables was predicted using our written program. The relaxation time of Dy +2: CaF2, used with ruby was