<p>Currently, breast cancer is one of the most common cancers and a main reason of women death worldwide particularly in<strong> </strong>developing countries such as Iraq. our work aims to predict the type of tumor whether benign or malignant through models that were built using logistic regression and neural networks and we hope it will help doctors in detecting the type of breast tumor. Four models were set using binary logistic regression and two different types of artificial neural networks namely multilayer perceptron MLP and radial basis function RBF. Evaluation of validated and trained models was done using several performance metrics like accuracy, sensitivity, specificity, and AUC (area under receiver ope

The work includes synthesis and characterization of some new heterocyclic compounds, as flow: The compound (3) (5-(4-chlorophenyl) -2-hydrazinyl-1,3,4-oxadiazole was synthesized by using two methods; the first method includes the direct reaction between hydrazine hydrate 80% and 5-(4-chlorophenyl)-2- (ethylthio) 1,3,4-oxadiazole (1), the second method involves converting 5-(4-chlorophenyl)-1,3,4-oxadiazol-2-amine (2) to diazonium salt then reducing this salt to compound (3) by stannous chloride. Compound (3) was used as starting material for synthesizing several fused heterocyclic compounds. The compound 6-(4- chlorophenyl)[1,2.4] triazolo [3,4,b][1,3,4] oxadiazole-3-(2H) thione (compound 4) was synthesized from the reaction of compo

In this research, some robust non-parametric methods were used to estimate the semi-parametric regression model, and then  these methods were compared using the MSE comparison criterion, different sample sizes, levels of variance, pollution rates, and three different models were used. These methods are S-LLS S-Estimation -local smoothing, (M-LLS)M- Estimation -local smoothing, (S-NW) S-Estimation-NadaryaWatson Smoothing, and (M-NW) M-Estimation-Nadarya-Watson Smoothing.

The results in the first model proved that the (S-LLS) method was the best in the case of large sample sizes, and small sample sizes showed that the

This study involves the design of 24 mixtures of fiber reinforced magnetic reactive powder concrete containing nano Silica. Tap water has been used in mixing 12 of these mixtures, while the other 12 have been mixed using magnetic water. Nano Silica (NS) with ratios (1, 1.5, 2, 2.5 and 3) % were used. The results showed 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 showed that the fiber reinforced magnetic reactive powder concrete containing 2.5% NS (FRMRPCCNS)  has the higher bulk density, dynamic modulus of elasticity, ultrasonic pulse velocity  electrical resistivity and lesser absorption than fiber reinforced

In this study, the effect of glass fiber reinforced polymer (GFRP) section and compressive strength of concrete in composite beams under static and low velocity impact loads was examined. Modeling was performed and the obtained results were compared with the test results and their compatibility was evaluated.‎ Experimental tests of four composite beams were carried out, where two of them are control specimen with 20 MPa compressive strength of concrete deck slab and 50 MPa for other. Bending characteristics were affected by the strength of concrete under impact loading case, as it increased maximum impact force and damping time at a ratio of 59% and reduced the damping ratio by 47% compared to the reference hybrid beam. Under stat

The present study was conducted to estimate the antimicrobial activity and the potential biological control of the killer toxin produced byD. hanseniiDSMZ70238 against several pathogenic microorganisms. In this study, the effects of NaCl, pH, and temperature, killer toxin production, and antimicrobial activity were studied. The results showed that the optimum inhibitory effect of killer toxin was at 8% NaCl, and the diameters of clear zones were 20, 22, 22, 21, 14, and 13 mm forStaphylococcus aureus, Escherichia coli, Klebsiella pneumoniae, Streptococcus pyogenes, Candida albicans,andCandida neoformans, respectively. The largest inhibition zones were