Background: Crown preparation of vital teeth involve the removal of a sound tooth structure, and when enamel removed this lead to exposed dentin with an increase in the number of open dentinal tubules also the diameter of dentinal tubules will increase, furthermore lead to increase movement of fluids inside the tubules all that causes post preparation sensitivity. The aim of this study is to evaluate the effect of desensitizing by Er:Cr:YSGG laser on shear bond strength of prepared tooth and resin cement. Materials and methods: Thirty sound maxillary premolars, grouped into three groups(n=10). Group A is the control group, group B irradiated by Er:Cr:YSGG laser with (0.25 W, 20 Hz, 10%water and air), group C irradiated by Er:Cr:YSGG laser with (0.5 W, 20 Hz, 10% water and air). Results: SEM examination showed complete occlusion of opened dentinal tubules after laser irradiation of both groups that irradiated with laser. Statistical analysis showed significant increase in surface roughness in group B and C. SBS was significantly increase in group B with no significant increase in group C. Conclusion: Er:Cr:YSGG laser can occlude open dentinal tubules without and adverse effect on the retention of the restoration, on the contrary it increase bonding strength. And this increasing was significant in group B with (0.25W, 20 Hz, 10 % water and air). suggestion: so the parameters used in group B (0.25 W, 20Hz, 10 % water and air) is recommended for desensitizing prepared tooth and induce enhancement to the bonding strength of resin to tooth surface.
Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for
Seawater might serve as a fresh‐water supply for future generations to help meet the growing need for clean drinking water. Desalination and waste management using newer and more energy intensive processes are not viable options in the long term. Thus, an integrated and sustainable strategy is required to accomplish cost‐effective desalination via wastewater treatment. A microbial desalination cell (MDC) is a new technology that can treat wastewater, desalinate saltwater, and produce green energy simultaneously. Bio‐electrochemical oxidation of wastewater organics creates power using this method. Desalination and the creation of value‐added by‐products are expected because of this ionic mov
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