Background: Tooth extraction is one of the most commonly performed procedures in dentistry. It is usually a traumatic process often resulting in immediate destruction and loss of alveolar bone and surrounding soft tissues. Various instruments have been described to perform atraumatic extractions which can prevent damage to the paradental structures. The physics forceps is one of those innovations in dental extraction technologies that claim to provide an efficient means for atraumatic dental extractions. Materials and method: A randomized clinical trial was conducted to compare the physics forceps with the conventional forceps for the removal of 28 mandibular single rooted teeth under the following parameters: incidence of crown, root, buccal alveolar bone fracture, the incidence of gingival tear and time needed for extraction. The samples were assigned randomly into two groups according to the computer based randomization software, into a control group (A) and study group (B). The control group was subjected to the surgical extraction procedure using the conventional forceps while the study group was subjected to the surgical extraction procedure using the physics forceps. Results: results showed that the time required for extraction using the physics forceps was (mean 0.385 min.), which was significantly lesser as compared with that of conventional forceps (mean 3.971 min.) (P=0.011), buccal bone fracture occurred in 4 out of 14 cases (28.57%) using the conventional forceps while it did not occur with the use of the physics forceps (0.00%), crown fracture occurred in 3 cases using the conventional forceps (21.43%), while it did not occur with the use of the physics forceps (0.00%), root fracture occurred in 1 case using the physics forceps (3.57%), while it did not occur with the use of the conventional forceps (0.00%). As for the gingival tear, it occurred in 7 cases using the conventional forceps (50.00%), while it did not occur with the use of the physics forceps (0.00%) which was highly significant (P=0.006). Conclusions: the use of physics forceps maintains the integrity of gingiva and surrounding periodontium. So extractions using physics forceps are less invasive over conventional forceps and can be considered as a reliable method for extraction requiring significantly less comparative intraoperative time.
DAIRMD Professor Hayder R. Al-Hamamy, **Professor Adil A. Noaimi, **Dr. Ihsan A. Al-Turfy, IOSR Journal of Dental and Medical Sciences (IOSR-JDMS), 2015
The study aims to demonstrate the importance of instructional methods in teaching Arabic language as a second language or teaching the Arabic language to non-native speakers. The study is in line with the tremendous development in the field of knowledge, especially in the field of technology and communication, and the emergence of many electronic media in education in general and language teaching in particular. It employs an image in teaching vocabulary and presenting the experience of the Arabic Language Institute for Non-Speakers-King Abdul-Aziz University. The study follows the descriptive approach to solve the problem represented by the lack of interest in the educational methods when teaching Arabic as a second language. Accordingl
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... Show MoreThe problem of the study and its significance:
Due to the increasing pressures of life continually, and constant quest behind materialism necessary and frustrations that confront us daily in general, the greater the emergence of a number of cases of disease organic roots psychological causing them because of severity of a lack of response to conventional treatments (drugs), and this is creating in patients a number of emotional disorders resulting from concern the risk of disease
That is interested psychologists and doctors searchin
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In this study, the most important and most widespread estimations of the estimation of the nonlinear regression function were investigated using Nadaraya-Watson and Regression Local Ploynomial, which are one of the types of non-linear
... Show MoreSoftware-defined networking (SDN) presents novel security and privacy risks, including distributed denial-of-service (DDoS) attacks. In response to these threats, machine learning (ML) and deep learning (DL) have emerged as effective approaches for quickly identifying and mitigating anomalies. To this end, this research employs various classification methods, including support vector machines (SVMs), K-nearest neighbors (KNNs), decision trees (DTs), multiple layer perceptron (MLP), and convolutional neural networks (CNNs), and compares their performance. CNN exhibits the highest train accuracy at 97.808%, yet the lowest prediction accuracy at 90.08%. In contrast, SVM demonstrates the highest prediction accuracy of 95.5%. As such, an
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