Improved oral bioavailability of lipophilic substances can be achieved using self-emulsifying drug delivery systems. However, because the properties of self-emulsifying are greatly influenced by surfactant amount and type, type of oil used, droplet size, charge, cosolvents, and physiological variables, the synthesis of self-emulsifying is highly complex; consequently, only a small number of excipient self-emulsifying formulations has been developed so far for clinical use. This study reports a highly effective procedure for developing self-emulsifying formulations using a novel approach based on the hydrophilic-lipophilic difference theory. Microemulsion characteristics, such as the constituents and amounts of oil and surfactant electrolyte

The purpose of the current article lies in determining  the impact of the suggestive  exercises on the development of  the mental capabilities of children. The researchers used the experimental method with a single-group design, which was appropriate for the core of the current article . The study samble  has been specified as children aged 4-6 years in Umm Al-Rabi'een Kindergarten, with a total of 95 children. The study samble  (15 children) was randomly selected.  . After the exercises were completed, the post-tests have been carried out  on the sample  with similar circumstances as that of  pre-tests.  Researchers used statistical methods in the SPSS program. After the results were presented, analyzed, and discussed, The resear

In this work, a new development of predictive voltage-tracking control algorithm for Proton Exchange Membrane Fuel Cell (PEMFCs) model, using a neural network technique based on-line auto-tuning intelligent algorithm was proposed. The aim of proposed robust feedback nonlinear neural predictive voltage controller is to find precisely and quickly the optimal hydrogen partial pressure action to control the stack terminal voltage of the (PEMFC) model for N-step ahead prediction. The Chaotic Particle Swarm Optimization (CPSO) implemented as a stable and robust on-line auto-tune algorithm to find the optimal weights for the proposed predictive neural network controller to improve system performance in terms of fast-tracking de

In this work, an efficient energy management (EEM) approach is proposed to merge IoT technology to enhance electric smart meters by working together to satisfy the best result of the electricity customer's consumption. This proposed system is called an integrated Internet of things for electrical smart meter (2IOT-ESM) architecture. The electric smart meter (ESM) is the first and most important technique used to measure the active power, current, and energy consumption for the house’s loads. At the same time, the effectiveness of this work includes equipping ESM with an additional storage capacity that ensures that the measurements are not lost in the event of a failure or sudden outage in WiFi network. Then then these measurement

Has been studied both processes Almetzaz and extortion of a substance Alklanda Maysan different amounts of Alcaúlan Guy 70% alcohol solution using the method when the wavelength

Generally, radiologists analyse the Magnetic Resonance Imaging (MRI) by visual inspection to detect and identify the presence of tumour or abnormal tissue in brain MR images. The huge number of such MR images makes this visual interpretation process, not only laborious and expensive but often erroneous. Furthermore, the human eye and brain sensitivity to elucidate such images gets reduced with the increase of number of cases, especially when only some slices contain information of the affected area. Therefore, an automated system for the analysis and classification of MR images is mandatory. In this paper, we propose a new method for abnormality detection from T1-Weighted MRI of human head scans using three planes, including axial plane, co

Reduce the required time for measuring the permeability of clayey soils by using new manufactured cell