Background: Menopause can bring oral health problems and also associated with significant adverse changes in the orofacial complex. After menopause, women become more susceptible to periodontal disease due to deficiency of estrogen hormone. Current study aimed to evaluate the periodontal health status in relation to salivary constituent including pH, flow rate and some elements (Magnesium, Calcium and inorganic phosphorus) of pre and post-menopause women. Materials and Methods: Periodontal health status of 52 women aged 48-50 years old (26 pre-menopause and 26 post-menopause) were examined including (gingival index, plaque index, calculus index, probing pocket depth and clinical attachment level). Salivary sample was collected for two women

Background: Pain and the usage of local anesthetic agents are still real problem in pediatric dentistry, for these reasons, the use of minimal invasive dentistry (MID) in regard to the patient comfort is important especially for children, anxious and uncooperative patients. Chemomechanical caries removal (CMCR) methods involve the selective removal of the carious dentine hence it avoided the painful removal of the sound dentine and the anxiety resulted due to the vibration of the hand piece which is also decreased thus it appears to be more acceptable and comfortable to the patient. Aims of this study: This study was conducted among group of children to assess and compare the anxiety rating scale (during and after treatment) between the

The interest in the issue of capital movement as an economic phenomenon has increased because of its effects and effects and its ability to influence the economic balance and the effectiveness of monetary policy. All countries seek to attract capital and benefit from it because of its effects and results such as supporting economic development process and optimal allocation of economic resources. The problem of the financing gap that most countries suffer from, and others, but sometimes the movement of capital creates challenges for monetary policy makers in achieving their goals.

   After 2003, the Iraqi economy witnessed an openness and economic liberalization unlike previous years, which

Prediction of daily rainfall is important for flood forecasting, reservoir operation, and many other hydrological applications. The artificial intelligence (AI) algorithm is generally used for stochastic forecasting rainfall which is not capable to simulate unseen extreme rainfall events which become common due to climate change. A new model is developed in this study for prediction of daily rainfall for different lead times based on sea level pressure (SLP) which is physically related to rainfall on land and thus able to predict unseen rainfall events. Daily rainfall of east coast of Peninsular Malaysia (PM) was predicted using SLP data over the climate domain. Five advanced AI algorithms such as extreme learning machine (ELM), Bay

الغرض من هذا العمل هو دراسة الفضاء الإسقاطي ثلاثي الأبعاد PG (3، P) حيث p = 4 باستخدام المعادلات الجبرية وجدنا النقاط والخطوط والمستويات وفي هذا الفضاء نبني (k، ℓ) -span وهي مجموعة من خطوط k لا يتقاطع اثنان منها. نثبت أن الحد الأقصى للكمال (k، ℓ) -span في PG (3،4) هو (17، ℓ) -span ، وهو ما يساوي جميع نقاط المساحة التي تسمى السبريد.

In this paper, we derived an estimator of reliability function for Laplace distribution with two parameters using Bayes method with square error loss function, Jeffery’s formula and conditional probability random variable of observation. The main objective of this study is to find the efficiency of the derived Bayesian estimator compared to the maximum likelihood of this function and moment method using simulation technique by Monte Carlo method under different Laplace distribution parameters and sample sizes. The consequences have shown that Bayes estimator has been more efficient than the maximum likelihood estimator and moment estimator in all samples sizes

Software-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