Objective(s): current study aims to find the effect of electronic games on children's behavior, and find the
relationship between child demographic data and the effect of electronic games on the child's behavior.
Methodology: A descriptive-analytic study was conducted for assessing the Effect of electronic games on
children's behaviors that attending to teaching hospitals in Baghdad city during the period of (October/ 20th
/2017 to March/1st /2018). A (50) purposive (non- probabilistic) sample from: Child Protection teaching
Hospital, Child Central Hospital in AL-Iskan. The sample is selected according to the criteria: Children who
visited the consulting unit of children in the hospitals in the first visit, children in the age between 7 and 12
years, boys and girls and their parents must be founded with them to help in the fill of the questionnaire. The
data are collected for the present study through the use of the questionnaires and semi-structured interview
technique. The data have been analyzed by using the descriptive statistics (Frequencies, Percentages, Means
and Standard Deviations (SD) and the inferential statistics (Chi-square to find out the relationships between the
socio-demographic and the effect of electronic games on the child's behavior).
Results: study results show that 70% of study sample was boys and 45% of them was between 9-10 years and
100% from the study sample have electronic game devices and 72% of them was playing every day, 24% play
for one hour or three hours. And that 52% of study sample have problems with the study, and their significant
statistical association in (p-value=0.026) between gender and behavioral problems of children.
Recommendations: study recommended for the importance of awareness of parents and educators to the right
way for using electronic games and supervise and guide theme to reduce the its negative effect on children
In this paper, split-plate airlift electrochemical reactor as an apparatus with new configuration for wastewater treatment was provided. Two aluminum plates were fixed inside the reactor and present two functions; first it works as split plates for internal loop generation of the airlift system (the zone between the two plates acts as riser while the other two zones act as downcomer) and second it works as two electrodes for electrocoagulation process. Simulated wastewater contaminated with zinc ions was used to test the performance of this apparatus for zinc removal by studying the effect of different experimental variables such as initial concentration of zinc (50-800 ppm), electrical current density (2.67-21.4 mA/cm2), init
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Three mesoporous silica with different functional group were prepared by one-step synthesis based on the simultaneous hydrolysis and condensation of sodium silicate with organo - silane in the presence of template surfactant polydimethylsiloxane - polyethyleneoxide (PDMS - PEO). The prepared materials were characterized by Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), atomic force microscopy (AFM) and nitrogen adsorption/desorption experiments. The results indicate that the preparation of methyl and phenyl functionalized silica were successful and the mass of methyl and phenyl groups bonded to the silica structure are 15, 38 mmol per gram silica. The average diameter of the silica particles are 103.51,
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The purpose of my thesis is to synthesis two new bidentate ligands which were used to prepare series of metal complexes by reacting the ligands with (M+2 = Mn, Co, Ni, Cu, Cd and Hg) Succinyl chloride was used as starting material to synthesis two bidentate ligands (L1) and (L2) by reaction it with 4-chloroaniline (L1) and (4-aminoacetophenone) (L2) in dichloromethane as a solvent, that are: (L1) = N1,N4-bis (4-chloro phenyl ) succinamide (L2) =N1,N4-bis(4-acetylphenyl)succinamide The new ligands were characterize by using spectroscopic study (Fourier-transform infrared spectroscopy (FT-IR), electronic spectra ( UV-Vis) ,nuclear magnetic resonance(1H,13C-NMR), Mass spectra
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Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne
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