Objective: The aim of this study was to assess the knowledge and attitude of Iraqi dentists towards cone beam computed tomography (CBCT) applications in endodontics by using an online survey. Materials and Methods: A questionnaire, consisting of 31 questions, targeted general dental practitioners and specialists in different dental specialities. A total of 306 participants were included. Data were assessed according to the frequency of distribution and the chi-square test was applied to analyse the difference in responses between two independent groups. Results: Among the participants 63.4% were using digital radiography in their daily practice, and 84% had awareness about CBCT's uses, with higher statistically significant responses among endodontists. About 51.4% of participants gained their CBCT knowledge from lectures, while 79.4% believed that continuous education courses enhance knowledge of CBCT. Nearly 75% of participants agreed on the accuracy of CBCT rather than periapical radiography in detecting endodontic conditions. However, most participants responded that CBCT would be used selectively in their future routine practice. Conclusions: This questionnaire showed that Iraqi dentists have a very good knowledge regarding indications and limitations of CBCT in endodontics. However, there is a lack of structured teaching and practical training on the use of CBCT within dental schools. The development of CBCT training programmes and increasing the availability of CBCT machines within dental schools are strongly supported by the results of this survey
Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med
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The objective of this study is to evaluate the efficacy and safety of rowatinex and tamsulosin in the treatment of patients with ureteric stone.
Forty patients with ureteric stone ranged (4- 12) mm, were included in this study. They were randomized into two groups where the first group includes twenty patients treated with Rowatinex three times daily (Group 1), and the second group includes twenty patients treated with tamsulosin 0.4mg/day (Group 2). All patients were randomly assigned to receive the designed standard medical therapy for a maximum of 3 weeks.
Each group was given an antibiotic as prophylaxis and an injectable non-steroidal anti-inflammatory drug used on demand. At the outpatient clinic all subjects were a
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Rotational Piezoelectric Energy Harvesting (RPZTEH) is widely used due to mechanical rotational input power availability in industrial and natural environments. This paper reviews the recent studies and research in RPZTEH based on its excitation elements and design and their influence on performance. It presents different groups for comparison according to their mechanical inputs and applications, such as fluid (air or water) movement, human motion, rotational vehicle tires, and other rotational operational principal including gears. The work emphasises the discussion of different types of excitations elements, such as mass weight, magnetic force, gravity force, centrifugal force, gears teeth, and impact force, to show their effect
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Cybersecurity refers to the actions that are used by people and companies to protect themselves and their information from cyber threats. Different security methods have been proposed for detecting network abnormal behavior, but some effective attacks are still a major concern in the computer community. Many security gaps, like Denial of Service, spam, phishing, and other types of attacks, are reported daily, and the attack numbers are growing. Intrusion detection is a security protection method that is used to detect and report any abnormal traffic automatically that may affect network security, such as internal attacks, external attacks, and maloperations. This paper proposed an anomaly intrusion detection system method based on a
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Cybersecurity refers to the actions that are used by people and companies to protect themselves and their information from cyber threats. Different security methods have been proposed for detecting network abnormal behavior, but some effective attacks are still a major concern in the computer community. Many security gaps, like Denial of Service, spam, phishing, and other types of attacks, are reported daily, and the attack numbers are growing. Intrusion detection is a security protection method that is used to detect and report any abnormal traffic automatically that may affect network security, such as internal attacks, external attacks, and maloperations. This paper proposed an anomaly intrusion detection system method based on a
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In this paper, an algorithm is suggested to train a single layer feedforward neural network to function as a heteroassociative memory. This algorithm enhances the ability of the memory to recall the stored patterns when partially described noisy inputs patterns are presented. The algorithm relies on adapting the standard delta rule by introducing new terms, first order term and second order term to it. Results show that the heteroassociative neural network trained with this algorithm perfectly recalls the desired stored pattern when 1.6% and 3.2% special partially described noisy inputs patterns are presented.