In this study, the photodegradation of Congo red dye (CR) in aqueous solution was investigated using Au-Pd/TiO₂ as photocatalyst. The concentration of dye, dosage of photocatalyst, amount of H₂O₂, pH of the medium and temperature were examined to find the optimum values of these parameters. It has been found that 28 ppm was the best dye concentration. The optimum amount of photocatalyst was 0.09 g/75 mL of dye solution when the degradation percent was ~ 96 % after irradiation time of 12 hours, while the best amount of hydrogen peroxide was 7μl/75 mL of dye solution at degradation percent ~97 % after irradiation time of 10 hours, whereas pH 5 was the best value to carry out the reaction at the highest deg

تعد أساليب وطرائق تدريس المواد الدراسية العلمية من المجالات المهمة التي تساعد في تحويل المجرد إلى ثوابت في الذهن وترفع مستوى خبرات كل من المعلم والمتعلم على حد سواء إضافة إلى إنها جزء لا يتجزأ من العملية التربوية ومن الركائز المهمة التي تقوم عليها المناهج الدراسية لأنها تساعد الطلبة على اكتساب مهارات ومعلومات وتكوين اتجاهات وميول تخدم أهداف تدريس المادة وبما أن مادة الكيمياء الكمية هي إحدى المواد الدراسية

   يهدف هذا البحث إلى تأصيل الإستدلال بالقاعدة العقائدية، وبيان ضوابطه، وإبراز نماذج من أهم تطبيقاته المعاصرة، وقد أعتمد مناهج من أهمها المنهج الإستقرائي والمنهج المقارن ، وتناول البحث التعريف لقصص النبوة فى الأديان السماوية ، وعرض بعض الدراسات السابقة التى تناولت نفس الموضوع ، ثم تطرق البحث لذكر نماذج من قصص الأنبياء من الديانات السماوية الثلاث ، وتوصل البحث الى أن الأديان السماوية رغم بعض الإختلاف

The present study included the microscopic and molecular identification of Entamoeba histolytica by using specific primers to detect four virulence factors possessed by Entamoeba histolytica. Virulence factors included Active Cysteine proteinase, Galactose/N-acetyl-D-galactose-lectin, Amoeba pore C and Phospholipase. Titanium dioxide nanoparticles (TiO₂NPs) were synthesized from Pseudomonas aeruginosa which producing Pyocyanin pigment as a reducing agent to form it. After that we studied the ability ofTiO₂NPs to inhibit virulence factors production and curing the genes responsible for encoding them by using four different dose 2 ,3, 4, 6 mg/Kg and administered by intraperitoneal injection

This study is dedicated to solving multicollinearity problem for the general linear model by using Ridge regression method. The basic formulation of this method and suggested forms for Ridge parameter is applied to the Gross Domestic Product data in Iraq. This data has normal distribution. The best linear regression model is obtained after solving multicollinearity problem with the suggesting of 10 k value.

            Nowadays, it is convenient for us to use a search engine to get our needed information. But sometimes it will misunderstand the information because of the different media reports. The Recommender System (RS) is popular to use for every business since it can provide information for users that will attract more revenues for companies. But also, sometimes the system will recommend unneeded information for users. Because of this, this paper provided an architecture of a recommender system that could base on user-oriented preference. This system is called UOP-RS. To make the UOP-RS significantly, this paper focused on movie theatre information and collect the movie database from the IMDb website that provides informatio

In this paper, we made comparison among different parametric ,nonparametric and semiparametric estimators for partial linear regression model users parametric represented by ols and nonparametric methods represented by cubic smoothing spline estimator and Nadaraya-Watson estimator, we study three nonparametric regression models and samples sizes  n=40,60,100,variances used σ²=0.5,1,1.5 the results  for the first model show that N.W estimator for partial linear regression model(PLM) is the best followed the cubic smoothing spline estimator for (PLM),and the results of the second and the third model show that the best estimator is C.S.S.followed by N.W estimator for (PLM) ,the

         The issue of penalized regression model has received considerable critical attention to variable selection. It plays an essential role in dealing with high dimensional data. Arctangent denoted by the Atan penalty has been used in both estimation and variable selection as an efficient method recently. However, the Atan penalty is very sensitive to outliers in response to variables or heavy-tailed error distribution. While the least absolute deviation is a good method to get robustness in regression estimation. The specific objective of this research is to propose a robust Atan estimator from combining these two ideas at once. Simulation experiments and real data applications show that the proposed LAD-Atan estimator

         The issue of penalized regression model has received considerable critical attention to variable selection. It plays an essential role in dealing with high dimensional data. Arctangent denoted by the Atan penalty has been used in both estimation and variable selection as an efficient method recently. However, the Atan penalty is very sensitive to outliers in response to variables or heavy-tailed error distribution. While the least absolute deviation is a good method to get robustness in regression estimation. The specific objective of this research is to propose a robust Atan estimator from combining these two ideas at once. Simulation experiments and real data applications show that the p

This research aims to analyze and simulate biochemical real test data for uncovering the relationships among the tests, and how each of them impacts others. The data were acquired from Iraqi private biochemical laboratory. However, these data have many dimensions with a high rate of null values, and big patient numbers. Then, several experiments have been applied on these data beginning with unsupervised techniques such as hierarchical clustering, and k-means, but the results were not clear. Then the preprocessing step performed, to make the dataset analyzable by supervised techniques such as Linear Discriminant Analysis (LDA), Classification And Regression Tree (CART), Logistic Regression (LR), K-Nearest Neighbor (K-NN), Naïve Bays (NB