Background: The main aim of the present study is to qualify and quantify voids formation of root canals obturated with GuttaCore (GC) and experimental Hydroxyapatite polyethylene (HA/PE) as new carrier-based root canal fillings by using micro computed tomography scan. Materials and methods: In the present study, eight straight single-rooted human permanent premolar teeth are selected and disinfected, then stored in distilled water. The teeth decoronated leaving a root length of 12mm each. The root canals instrumented by using crown down technique and the apical diameter of the root canal prepared to a size # 30/0.04 for achieving standardized measurements. A 5mL of 17% EDTA used to remove the smear layer followed by 5mL of 2.5% NaOCl and rinsing with normal saline. Then the shaped root canals were randomly subdivided into two groups of 4 teeth each according to the carrier-based obturation system use, GuttaCore or experimental HA/PE. Afterwards, the obturated roots stored at 37°C with 100% humidity for 72 hours to allow for complete setting of the sealer. Micro-CT was then scanned to quantify the voids within the root canal space. The data were statistically analyzed by one-way ANOVA and post hoc comparison tests (α=0.05). Results: The root canals obturated with both obturation systems, GuttaCore andexperimental HA/PE showed voids formation, particularly at the apical third of the root canal. GC obturation showed a lower percentage of voids volume (1.54%) than the experimental HA/PE obturation (2.3%). The void volume percentage in the GuttaCore system, however, was non-significantly different (P> 0.05) in comparison with the experimental PE/HA system. Conclusions: GuttaCore and experimental HA/PE obturators exhibited voids formation within the entire root canal space. The experimental HA/PE obturator is comparable to the GuttaCore obturator in terms of voids qualification
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The continuous advancement in the use of the IoT has greatly transformed industries, though at the same time it has made the IoT network vulnerable to highly advanced cybercrimes. There are several limitations with traditional security measures for IoT; the protection of distributed and adaptive IoT systems requires new approaches. This research presents novel threat intelligence for IoT networks based on deep learning, which maintains compliance with IEEE standards. Interweaving artificial intelligence with standardization frameworks is the goal of the study and, thus, improves the identification, protection, and reduction of cyber threats impacting IoT environments. The study is systematic and begins by examining IoT-specific thre
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Nowadays, internet security is a critical concern; the One of the most difficult study issues in network security is "intrusion detection". Fight against external threats. Intrusion detection is a novel method of securing computers and data networks that are already in use. To boost the efficacy of intrusion detection systems, machine learning and deep learning are widely deployed. While work on intrusion detection systems is already underway, based on data mining and machine learning is effective, it requires to detect intrusions by training static batch classifiers regardless considering the time-varying features of a regular data stream. Real-world problems, on the other hand, rarely fit into models that have such constraints. Furthermor
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This research depends on the relationship between the reflected spectrum, the nature of each target, area and the percentage of its presence with other targets in the unity of the target area. The changes occur in Land cover have been detected for different years using satellite images based on the Modified Spectral Angle Mapper (MSAM) processing, where Landsat satellite images are utilized using two software programming (MATLAB 7.11 and ERDAS imagine 2014). The proposed supervised classification method (MSAM) using a MATLAB program with supervised classification method (Maximum likelihood Classifier) by ERDAS imagine have been used to get farthest precise results and detect environmental changes for periods. Despite using two classificatio
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