Background: Periodontitis is an inflammatory disease that affects the supporting tissues of the teeth; Smoking is an important risk factor for periodontitis induces alveolar bone loss and cause an imbalance between bone resorption and bone deposition. The purpose of this study is to detect and compare the presence of incipient periodontitis among young smokers and non-smokers by measuring the distance between cement-enamel junction and alveolar crest (CEJ-Ac) using Cone Beam Computed Tomography (CBCT). Material and methods: The total sample composed of fifty two participants, thirty one smokers and twenty one non-smokers (age range 14-22 years). Periodontal parameters: plaque index (PLI), gingival index (GI) were recorded for all teeth except the third molar while the radiographic analysis using CBCT was recorded on the Ramfjord teeth, the unit of measurement was from cement-enamel junction to alveolar crest distance (CEJ-Ac distance) per site in millimeters. Results: The results obtained were a non significant difference for PLI, a significant difference of mean of GI between young smokers and non smokers. There was a highly significant difference in the general mean of CEJ-Ac distance between both groups. There was a significant difference between maxillary and mandibular teeth, a non significant difference between right and left sides among young smokers and non smokers. Conclusion: The CBCT device plays an important role in detection the incipient form of periodontitis among young smokers and non-smokers, so we concluded that there is a highly significant difference in the general mean CEJ-Ac distance between young smokers and non smokers with increase distance in the maxillary teeth than that in the mandibular teeth.
With the high usage of computers and networks in the current time, the amount of security threats is increased. The study of intrusion detection systems (IDS) has received much attention throughout the computer science field. The main objective of this study is to examine the existing literature on various approaches for Intrusion Detection. This paper presents an overview of different intrusion detection systems and a detailed analysis of multiple techniques for these systems, including their advantages and disadvantages. These techniques include artificial neural networks, bio-inspired computing, evolutionary techniques, machine learning, and pattern recognition.
This study was conducted to determine the relationship between test anxiety and cognitive representation among university students. To this end, 152 student (male, female) were chosen randomly from scientific and social departments to fill out the questionnaires of test anxiety and cognitive representation. The researcher utilized Independent Samples T-Test, Pearson product-moment correlation coefficient, Cronbach's alpha and T-Test in his study. The result revealed that there were negative and a weak correlation between test anxiety and cognitive representation among university students.
Background: Undergraduate dental students are more susceptible to situations of stress that affected quality of sleep, such profiles of stress may result in sleep bruxism and/or awake bruxism, parafunctions that can affect oral and general health. The aim of the study was to evaluate the association of sleep bruxism, awake bruxism and sleep quality among dental students. Materials and methods: A cross-sectional study was performed including 260 Iraqi dental students from university of Baghdad aged from 20to25years old. Students enrolled in the third and fifth class participated in the study. The Pittsburgh Sleep Questionnaire Index (PSQI) was used for data collection. The PSQI was distributed during lecture classes. Sleep bruxism and awake
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Generalized multivariate transmuted Bessel distribution belongs to the family of probability distributions with a symmetric heavy tail. It is considered a mixed continuous probability distribution. It is the result of mixing the multivariate Gaussian mixture distribution with the generalized inverse normal distribution. On this basis, the paper will study a multiple compact regression model when the random error follows a generalized multivariate transmuted Bessel distribution. Assuming that the shape parameters are known, the parameters of the multiple compact regression model will be estimated using the maximum likelihood method and Bayesian approach depending on non-informative prior information. In addition, the Bayes factor was used
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Often there is no well drilling without problems. The solution lies in managing and evaluating these problems and developing strategies to manage and scale them. Non-productive time (NPT) is one of the main causes of delayed drilling operations. Many events or possibilities can lead to a halt in drilling operations or a marginal decrease in the advancement of drilling, this is called (NPT). Reducing NPT has an important impact on the total expenditure, time and cost are considered one of the most important success factors in the oil industry. In other words, steps must be taken to investigate and eliminate loss of time, that is, unproductive time in the drilling rig in order to save time and cost and reduce wasted time. The data of
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KE Sharquie, AA Noaimi, SY Mohsin, 2011 - Cited by 4
<p><span>A Botnet is one of many attacks that can execute malicious tasks and develop continuously. Therefore, current research introduces a comparison framework, called BotDetectorFW, with classification and complexity improvements for the detection of Botnet attack using CICIDS2017 dataset. It is a free online dataset consist of several attacks with high-dimensions features. The process of feature selection is a significant step to obtain the least features by eliminating irrelated features and consequently reduces the detection time. This process implemented inside BotDetectorFW using two steps; data clustering and five distance measure formulas (cosine, dice, driver & kroeber, overlap, and pearson correlation
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