ABSTRACT Background: The main goal of chemomechanical endodontic treatment is the reduction or elimination of microorganisms from root canal system. The intracanal medicaments were used to enhance the disinfection process. This study was conducted to evaluate the antibacterial effect of thymus vulgaris, tea tree essential oils and cold pressed black seed oil (BSO) against E.faecalis. Materials and methods: E.faecalis was isolated from ten patients in need for endodontic treatment. The sensitivity of E.faecalis to the tested oils was evaluated in different concentrations in agar well diffusion method and compared with calcium hydroxide. The sensitivity of E.faecalis to vapor of the tested oils was also evaluated, in disk vaporization method

يهدف البحث الى تحليل الخيارات الاستراتيجية للاقتراض الخارجي في العراق لاستشراف افضل الخيارات الاستراتيجية المستقبلية في مجال الاقتراض الخارجي في دائرة الدين العام في وزارة المالية ، وقد استخدم الباحث منهج دراسة الحالة وباستعمال اسلوب تحليل خوارزمية ال K-Means لتشخيص كفاءة الاقتراض الخارجي لعينة البحث البالغة (81) قرضا التي اقترضتها وزارة المالية للفترة 2007-2020 . ولقد كان الغرض الرئيسي للبحث المساهمة في تمكين وزا

Flexible joint robot (FJR) manipulators can offer many attractive features over rigid manipulators, including light weight, safe operation, and high power efficiency. However, the tracking control of the FJR is challenging due to its inherent problems, such as underactuation, coupling, nonlinearities, uncertainties, and unknown external disturbances. In this article, a terminal sliding mode control (TSMC) is proposed for the FJR system to guarantee the finite-time convergence of the systems output, and to achieve the total robustness against the lumped disturbance and estimation error. By using two coordinate transformations, the FJR dynamics is turned into a canonical form. A cascaded finite-time sliding mode observer (CFTSMO) is construct

The aim of the present study was to distinguish between healthy children and those with epilepsy by electroencephalography (EEG). Two biomarkers including Hurst exponents (H) and Tsallis entropy (TE) were used to investigate the background activity of EEG of 10 healthy children and 10 with epilepsy. EEG artifacts were removed using Savitzky-Golay (SG) filter. As it hypothesize, there was a significant changes in irregularity and complexity in epileptic EEG in comparison with healthy control subjects using t-test (p< 0.05). The increasing in complexity changes were observed in H and TE results of epileptic subjects make them suggested EEG biomarker associated with epilepsy and a reliable tool for detection and identification of this di

The recent emergence of sophisticated Large Language Models (LLMs) such as GPT-4, Bard, and Bing has revolutionized the domain of scientific inquiry, particularly in the realm of large pre-trained vision-language models. This pivotal transformation is driving new frontiers in various fields, including image processing and digital media verification. In the heart of this evolution, our research focuses on the rapidly growing area of image authenticity verification, a field gaining immense relevance in the digital era. The study is specifically geared towards addressing the emerging challenge of distinguishing between authentic images and deep fakes – a task that has become critically important in a world increasingly reliant on digital med

Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, F

Advanced strategies for production forecasting, operational optimization, and decision-making enhancement have been employed through reservoir management and machine learning (ML) techniques. A hybrid model is established to predict future gas output in a gas reservoir through historical production data, including reservoir pressure, cumulative gas production, and cumulative water production for 67 months. The procedure starts with data preprocessing and applies seasonal exponential smoothing (SES) to capture seasonality and trends in production data, while an Artificial Neural Network (ANN) captures complicated spatiotemporal connections. The history replication in the models is quantified for accuracy through metric keys such as m