Background: One common undesirable side effect of orthodontic treatment with fixed appliances is the development of incipient caries lesions around brackets, particularly in patients with poor oral hygiene. Different methods have been used to prevent demineralization; the recent effort to improve the resistance against the demineralization is by the application of lasers. Materials and method: Thirty human premolars extracted for orthodontic purposes were used to test the effect of two energy level of ER-YAG laser on enamel resistance to demineralization. The brackets were bonded on the teeth and all the labial surface excluding 2 mm area gingival to the brackets were painted with acid resistance varnish. Three groups were generated. The first group was the control group (A), with no treatment was performed. In group II (B)and groups III (C); teeth were irradiated by ER-YAG laser of 200, 60 mj energy respectively. All the teeth were individually subjected to acid challenge cycle for 30 days. After debonding longitudinal sections were taken and examined under stereomicroscope. The enamel demineralization evaluation was done by taking the average of three depths at the centre of the artificial lesion. Also the enamel surface was classified by an experienced investigator according to acid etch pattern. Comparisons of the average depth values of the groups were performed with ANOVA and LSD tests. The statistical significance level was set at p ≤ 0.05. Results: The results revealed that average lesion depth was significantly deeper at the control group than the laser groups, and its significantly deeper in group (B) 200 mj than in group (C) 60 mj, enamel surfaces showed deeper pits and craters than in control group. Conclusions: the decrease in artificial caries lesion depth associated with use of the two laser energy level support the ER-YAG laser as a tool to increase enamel resistance to demineralization and white spot lesion prevention. Key words: Demineralization, ER-YAG, laser.
Industrial development has recently increased, including that of plastic industries. Since plastic has a very long analytical life, it will cause environmental pollution, so studies have resorted to reusing recycled waste plastic (sustainable plastic) to produce environmentally friendly concrete (green concrete). In this research, producing environmentally friendly load-bearing concrete masonry units (blocks) was considered where five concrete mixtures were compressed at the blocks producing machine. The cement content reduced from 400 kg/m3 (B-400) to 300 kg/m3 (B-300) then to 200 kg/m3 (B-200). While (B-380) was produced using 380 kg/m3 cement and 20 kg/m3 nano-sil
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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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Azo derivative ligand[H3L] have been synthesized by the reaction of diazonium salt of p-amino benzoic acid with orcinol in(1:1)mole ratio. The bidente ligand was reacted with the metal ions MnII,FeIIandCrIIIin(2:1)mole ratio via reflux in ethanol using Et3N as a base to give complexes of the general formula: [ M(H2L)2(H2O)x]Cly The synthesized compounds were characterized by spectroscopic methods[ I.R , UV-Vis, A.A and H1 NMR]along with melting point, chloride content and conductivity measurements. The complexes were screend for their in vitro antibacterial activity against one strain of staphylococcus as Gram(+) positive and one strain of pseudomonas as Gram(-) Negative, using the agar diffusion technique.