Background: The purpose of the current study was to evaluate the efficacy of a new orthodontic bonding system (Beauty Ortho Bond) involving the shear bond strength in dry and wet environments, and adhesion remnant index (ARI) scores evaluation in regard to other bonding systems (Heliosit and Resilience Orthodontic Adhesives). Materials and methods: Sixty defect free extracted premolars were randomly divided into six groups of 10 teeth each, mounted in acrylic resin, three groups for a dry environment and three for a wet one. Shear bond strength test was performed with a cross head speed of 0.5 mm/min, while surfaces of enamel and bracket-adhesive-enamel surfaces were examined with stereomicroscope For ARI scores evaluation. Data were analyzed by one way analysis of variance, least significant difference, student's t-test, and Fisher exact test. Results: The mean shear bond strength showed highest values for Resilience adhesive followed by Beauty Ortho Bond and Heliosit adhesives respectively both in dry and wet environments. Interestingly, there was a non-significant difference (P<0.05) between Resilience and Beauty Ortho Bond adhesives using least significant difference at dry environment. In wet environment the Beauty Ortho Bond showed an acceptable mean shear bond strength value (6.39 Mpa) which is considered as a clinically acceptable value. Adhesive remnant index scores demonstrated a tendency towards score 1 in dry environment, and towards score 3 in wet environment, the scores also showed a non-significant difference (P<0.05) between Resilience and Beauty Ortho Bond adhesives using Fischer exact test. Conclusion: Beauty Ortho Bond is less sensitive to wet environment than Resilience and Heliosit adhesives, therefore it has an advantage during clean up, as it reduces the risk of enamel damage during debonding procedure. Keywords: Beauty Ortho Bond, Shear bond strength, light cured composite.
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In this paper the queuing system (M/Er/1/N) has been considered in equilibrium. The method of stages introduced by Erlang has been used. The system of equations which governs the equilibrium probabilities of various stages has been given. For general N the probability of j stages of service are left in the system, has been introduced. And the probability for the empty system has been calculated in the explicit form.
Abstract
For sparse system identification,recent suggested algorithms are 
-norm Least Mean Square ( -LMS), Zero-Attracting LMS (ZA-LMS), Reweighted Zero-Attracting LMS (RZA-LMS), and p-norm LMS (p-LMS) algorithms, that have modified the cost function of the conventional LMS algorithm by adding a constraint of coefficients sparsity. And so, the proposed algorithms are named -ZA-LMS,
HM Al-Dabbas, RA Azeez, AE Ali, Iraqi Journal of Science, 2023
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Portable devices such as smartphones, tablet PCs, and PDAs are a useful combination of hardware and software turned toward the mobile workers. While they present the ability to review documents, communicate via electronic mail, appointments management, meetings, etc. They usually lack a variety of essential security features. To address the security concerns of sensitive data, many individuals and organizations, knowing the associated threats mitigate them through improving authentication of users, encryption of content, protection from malware, firewalls, intrusion prevention, etc. However, no standards have been developed yet to determine whether such mobile data management systems adequately provide the fu
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The Costing Accounting is one the analytic tools which plays important role by support the management in planning& control and decisions-making ,as it became attendant necessity to establish any project whether industrial ,commercial ,service or agriculture ..etc.
The consolidated accounting system has committed the companies to have their active costing system in which the management can obtain their own data, but we found most of the economic units face problems of applying the costing system because of reasons related to the system design itself or might be related to the requirements of the application success.
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After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings
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