Introduction: Carrier-based gutta-percha is an effective method of root canal obturation creating a 3-dimensional filling; however, retrieval of the plastic carrier is relatively difficult, particularly with smaller sizes. The purpose of this study was to develop composite carriers consisting of polyethylene (PE), hydroxyapatite (HA), and strontium oxide (SrO) for carrier-based root canal obturation. Methods: Composite fibers of HA, PE, and SrO were fabricated in the shape of a carrier for delivering gutta-percha (GP) using a melt-extrusion process. The fibers were characterized using infrared spectroscopy and the thermal properties determined using differential scanning calorimetry. The elastic modulus and tensile strength tests were determined using a universal testing machine. The radiographic appearance was established using digital periapical radiographs. Results: The composite core carrier exhibited a melting point of 111°C to 112°C, which would facilitate removal by heat application. The elastic modulus and the tensile strength were found to be lower than those of Thermafil carriers (Dentsply Tulsa Dental, Tulsa, OK). The preliminary radiographic evaluation showed that the novel composite core carrier is sufficiently radiopaque and can be distinguished from gutta-percha. Conclusions: The PE-HA-SrO composites were successfully melt processed into composite core carriers for delivering gutta-percha into the root canal space.
Image databases are increasing exponentially because of rapid developments in social networking and digital technologies. To search these databases, an efficient search technique is required. CBIR is considered one of these techniques. This paper presents a multistage CBIR to address the computational cost issues while reasonably preserving accuracy. In the presented work, the first stage acts as a filter that passes images to the next stage based on SKTP, which is the first time used in the CBIR domain. While in the second stage, LBP and Canny edge detectors are employed for extracting texture and shape features from the query image and images in the newly constructed database. The p
<p>Generally, The sending process of secret information via the transmission channel or any carrier medium is not secured. For this reason, the techniques of information hiding are needed. Therefore, steganography must take place before transmission. To embed a secret message at optimal positions of the cover image under spatial domain, using the developed particle swarm optimization algorithm (Dev.-PSO) to do that purpose in this paper based on Least Significant Bits (LSB) using LSB substitution. The main aim of (Dev. -PSO) algorithm is determining an optimal paths to reach a required goals in the specified search space based on disposal of them, using (Dev.-PSO) algorithm produces the paths of a required goals with most effi
... Show MoreA submerged weir is a hydraulic structure utilized to control flow in canals and rivers. Water scarcity is a persistent issue in Iraq, especially during the dry season when irrigation withdrawals reduce downstream water levels in canals (Water is lost from irrigation canals due to seepage, evaporation, and vegetation growth). The final section of the Bani Hassan Canal experiences significant drops in water surface (WS) levels, negatively impacting irrigation efficiency. This study addresses that gap by investigating the use of submerged weirs to enhance water distribution and raise WS in the final 6 km segment of the canal. A one-dimensional (1D) hydraulic mode
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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