Poly-ether-ether-ketone (PEEK) was introduced in dentistry as an alternative to metal alloys.

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod

يعد هذا النص أحد النصوص المسمارية المصادرة التي بحوزة المتحف العراقي، ويحمل الرقم المتحفي (235869)، قياساته )12،7x 6x 2،5سم). يتضمن مدخولات كميات من الشعير،أرخ النص الى عصر أور الثالثة (2012-2004 ق.م) و يعود الى السنة الثالثة من حكم الملك أبي-سين (2028-2004 ق.م)،أن الشخصية الرئيسة في هذا النص هو)با-اَ-كا مسمن الماشية( من مدينة أري-ساكرك، ومقارنته مع النصوص المسمارية المنشورة التي تعود الى أرشيفه يبلغ عددها (196) نصاً تضمنت نشاطاته م

mixtures of cyclohexane + n-decane and cyclohexane + 1-pentanol have been measured at 298.15, 308.15, 318.15, and 328.15 K over the whole mole fraction range. From these results, excess molar volumes, VE , have been calculated and fitted to the Flory equations. The VE values are negative and positive over the whole mole fraction range and at all temperatures. The excess refractive indices nE and excess viscosities ?E have been calculated from experimental refractive indices and viscosity measurements at different temperature and fitted to the mixing rules equations and Heric – Coursey equation respectively to predict theoretical refractive indices, we found good agreement between them for binary mixtures in this study. The variation of th

Background: Diabetes mellitus type 2 has been known for many years as the most common endocrine metabolic disorder that affect the oral cavity and cause many oral diseases including candidiasis. In this study, the incidence of Candida spp. in the saliva of controlled and uncontrolled diabetic patients were determined and compared with non diabetic group. Material and method: The sample consists of 200 subjects: 100 diabetic patients [57 (28.5%) uncontrolled diabetes, 43 (21.5%) controlled diabetes] and 100 (50%) non diabetic groups. Saliva samples was obtained from the subjects and cultured on selective media using appropriate microbiological method to observe the presence of Candida spp. Results: The results revealed a significant associat

In this study, a mathematical model for the kinetics of solute transport in liquid membrane systems (LMSs) has been formulated. This model merged the mechanisms of consecutive and reversible processes with a “semi-derived” diffusion expression, resulting in equations that describe solute concentrations in the three sections (donor, acceptor and membrane). These equations have been refined into linear forms, which are satisfying in the special conditions for simplification obtaining the important kinetic constants of the process experimentally.

Background: fixed orthodontic appliances deleterious influence on gingival health is well documented. Association between weight status and gingival health is presented in many studies. This study aimed to evaluate how early the impact of fixed orthodontic therapy on patients` gingival health, and if there are differences of that impact among different weight status groups. Materials and Methods: Sample consisted of 54 patients (25 males, 29 females; age limits are 16 -18 years) going under the course of treatment with fixed orthodontic appliance. Patients were categorized according to their Body Mass Index (BMI) into 3 weight status groups considering WHO charts in 2007 (underweight, normal weight, overweight and obese), then determinat

???? ?? ??? ????? ???? ?????? ?????????? ????? ??????? ???? ?????? ????? ??? ??? ????? ?? ???? ??? ????? ????? ???? ????? ????? ?? 0-3cm, 10cm, 20cm, 30cm, 40cm ???????? ????? ?? ???? ????? ???????? ?? ???? ????? ?????? CR-39??????? ?? ??? ??? ?????????? ???????????? ???????? ???? n.cm-2.s-1 5 x 103?? ?????? ?????????? Am241- Be??? ???? ??????? ????????? ??? ?? ???? ????? ?????????? ??? ?? ????? ??????? ?????? 0.881±0.086??? ?? ??????? ????? ??? ????? ??? ?? ????? ????? ??? ???????? ???0.441±0.036 ??? ?? ???????