Background: The present study was conducted to evaluate the effects of different bleaching methods on the shear bond strength of orthodontic Sapphire brackets bonded to human premolars teeth using light cured composite resin and to determine the predominant site of bond failure. Materials and Methods: Thirty freshly extracted human premolars were selected and randomly divided into three groups (10 per group). These groups are: control (unbleached) group, hydrogen peroxide group (HP) 37.5% ; which is the in- office bleaching method group, carbamide peroxide group (CP) 16%; which is the at- home bleaching method group. After bleaching process was performed, all the teeth stored in distilled water in a sealed container at room temperature for 24 hours before bonding was initiated, then orthodontic brackets were bonded with a light cure composite resin, stored in distilled water at room temperature for another 24 hours before debonding then the brackets de-bonded and tested for shear bond strength using an Instron universal testing machine. For adhesive remnant index (ARI) the enamel surface and bracket base of each tooth were inspected under magnifying lens (20X) of a stereomicroscope. Results and Conclusions: Non-statistically significant differences of shear bond strengths were found between the control group and the bleached groups, the dental bleaching in both methods did not affect the SBS of Sapphire brackets. The mode of failure was mostly between the adhesive and the enamel and the bond failure between the bracket base and the adhesive were also observed. Keywords: Shear bond strength, tooth bleaching agents, orthodontic brackets, dental bonding.
Inelastic longitudinal electron scattering form factors have been calculated for isoscaler transition
T = 0 of the (0+ ®2+ ) and (0+ ®4+ ) transitions for the 20Ne ,24Mg and 28Si nuclei. Model
space wave function defined by the orbits 1d5 2 ,2s1 2 and 1d3 2 can not give reasonable result for
the form factor. The core-polarization effects are evaluated by adopting the shape of the Tassie-
Model, together with the calculated ground Charge Density Distribution CDD for the low mass 2s-1d
shell nuclei using the occupation number of the states where the sub-shell 2s is included with an
occupation number of protons (a ) .
يصف هذا البحث الإنتاج الصديق للبيئة لجسيمات النحاس النانوية باستخدام مستخلص نبات الجرجير والحرق عند درجة حرارة 400 درجة مئوية لمدة 3 ساعات. تم استخدام SEM و TEM لتحليل حجم الجسيمات النانوية المحضرة. تم استخدام حيود الأشعة السينية لتحديد الهيكل البلوري. كشف التحليل الطيفي للأشعة السينية المشتتة للطاقة لهيكل المنتج الذي تم إنشاؤه عن مكونات النحاس والأكسجين فقط ، مما يدل على نقاء المادة المحضرة. استخدمت الماد
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... Show MoreThis study discusses risk management strategies caused by pandemic-related (Covid-19) suspensions in thirty-six engineering projects of different types and sizes selected from countries in the middle east and especially Iraq. The primary data collection method was a survey and questionnaire completed by selected project crew and laborers. Data were processed using Microsoft Excel to construct models to help decision-makers find solutions to the scheduling problems that may be expected to occur during a pandemic. A theoretical and practical concept for project risk management that addresses a range of global and local issues that affect schedule and cost is presented and results indicate that the most significant delays are due to a
... Show MoreLung cancer is one of the most serious and prevalent diseases, causing many deaths each year. Though CT scan images are mostly used in the diagnosis of cancer, the assessment of scans is an error-prone and time-consuming task. Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. The proposed model was trained on 1000 CT Scan Images of cancerous and non-c
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