The biochar prepared from sawdust raw material was applied in this study for the treatment of wastewater polluted with methyl orange dye. The effect of pH (2-11), initial concertation (50-250 mg/L) and time were studied. The isotherm of Langmuir, Frendluch and temkin models studied. The Langmuir model was the best to explain the adsorption process, maximum uptake was 136.67 mg/g at 25C^o of methyl orange dye. Equilibrium reached after four hours of contact for most adsorbents.The values of thermodynamic parameters ∆G were negative at various temperatures, so the process spontaneous, while ∆H values were 16683 j/mol and ∆S values was 60.82 j/mol.k.

Copper, and its, alloys and composites (being the matrix), are broadly used in the electronic as well as bearing materials due to the excellent thermal and electrical conductivities it has.

In this study, powder metallurgy technique was used for the production of copper graphite composite with three volume perc ent of graphite.  Processing parameters selected is (900) °C sintering temperature and (90) minutes holding time for samples that were heated in an inert atmosphere (argon gas). Wear test results showed a pronounced improvement in wear resistance as the percent of graphite increased which acts as solid lubricant (where wear rate was decreased by about 88% as compared with pure Cu). Microhardness and

 The purpose of this paper is to recognize the impact of database levels on fields of banking service (provision of remittance services and transfer of funds, save financial deposits, provision of personal loans services) in some of Iraqi banks using one-way multivariate analysis of variance. The paper population consisted of (120) employees, then a random stratified sample of (104) employees was taken. A questionnaire paper consists of (24) items were designed in order to analyze by one-Way multivariate analysis of variance (MANOVA) using SPSS.One of the main findings of the current paper is that there is an impact of database on fields of banking service in Iraqi banks (Al Rafidain and Al Rasheed).

Neural cryptography deals with the problem of “key exchange” between two neural networks by using the mutual learning concept. The two networks exchange their outputs (in bits) and the key between two communicating parties ar eventually represented in the final learned weights, when the two networks are said to be synchronized. Security of neural synchronization is put at risk if an attacker is capable of synchronizing with any of the two parties during the training process.

Shadow detection and removal is an important task when dealing with color outdoor images. Shadows are generated by a local and relative absence of light. Shadows are, first of all, a local decrease in the amount of light that reaches a surface. Secondly, they are a local change in the amount of light rejected by a surface toward the observer. Most shadow detection and segmentation methods are based on image analysis. However, some factors will affect the detection result due to the complexity of the circumstances. In this paper a method of segmentation test present to detect shadows from an image and a function concept is used to remove the shadow from an image.

Compressing the speech reduces the data storage requirements, leading to reducing the time of transmitting the digitized speech over long-haul links like internet. To obtain best performance in speech compression, wavelet transforms require filters that combine a number of desirable properties, such as orthogonality and symmetry.The MCT bases functions are derived from GHM bases function using 2D linear convolution .The fast computation algorithm methods introduced here added desirable features to the current transform. We further assess the performance of the MCT in speech compression application. This paper discusses the effect of using DWT and MCT (one and two dimension) on speech compression. DWT and MCT performances in terms of comp

Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att