Bromocriptine mesylate is a semisynthetic ergot alkaloid derivative with potent dopaminergic activity, used in the treatment of pituitary tumors, Parkinson's disease (PD), hyperprolactinaemia, neuroleptic malignant syndrome, and type 2 diabetes ,the oral bioavailability is  approximately 6%, therefore aim  its  prepare and evaluate bromocriptine mesylate  as liquid self nano emulsifying drug delivery system to enhance its solubility , dissolution and stability . Solubility study was made in different vehicles to select the best excipients for dissolving bromocriptine mesylate. Pseudo-ternary phase diagrams were constructed at 1:1, 2:1, 3:1 and 4:1 ratios of surfactant and co-surfactant, four formulations were pre

Metasurface polarizers are essential optical components in modern integrated optics and play a vital role in many optical applications including Quantum Key Distribution systems in quantum cryptography. However, inverse design of metasurface polarizers with high efficiency depends on the proper prediction of structural dimensions based on required optical response. Deep learning neural networks can efficiently help in the inverse design process, minimizing both time and simulation resources requirements, while better results can be achieved compared to traditional optimization methods. Hereby, utilizing the COMSOL Multiphysics Surrogate model and deep neural networks to design a metasurface grating structure with high extinction rat

Background: The purpose of this study was to evaluate the effect of in vitro long-term simulation of oral conditions on the bond strength of PEEK CAD/CAM lingual retainers.

Material and methods: The sample consisted of 12 PEEK CAD/CAM retainers each composed of 2 centrally perforated 3x4mm pads joined by a connector. They were treated by 98% sulfuric acid for 1 minute and then conditioned with Single Bond Universal and bonded to the lingual surface of premolar teeth by 3M Transbond TM System. Half of the retainers were artificially aged using a 30-day water storage and 5000 thermocycling protocol before bond strength testing to compare with the non-aged specimens.

Results: The artificially aged retainers showed a marginally

A novel welded demountable shear connector for sustainable steel-concrete composite structures is proposed. The proposed connector consists of a grout-filled steel tube bolted to a compatible partially threaded stud, which is welded on a steel section. This connector allows for an easy deconstruction at the end of the service life of a building, promoting the reuse of both the concrete slabs and the steel sections. This paper presents the experimental evaluation of the structural behavior of the proposed connector using a horizontal pushout test arrangement. The effects of various parameters, including the tube thickness, the presence of grout infill, and the concrete slab compressive strength, were assessed. A nonlinear finite element mode

The provision of openings in serviceable reinforced concrete beams may result in a substantial decline in the beam's capacity and integrity, indicating the necessity of opening strengthening. The present study investigates the experimental response of reinforced concrete T-beams with multiple web-strengthened openings disposed in shear span to static and impact loads. Fourteen RC T-beams were tested in two groups, each of seven beams. The first group was tested under static loading up to failure, while the second group was tested under repeated impact loading until the width of shear cracks reached 0.3 mm. The residual static strengths of the beams subjected to impact loading were then determined. The test variables considered were

Transmission lines are generally subjected to faults, so it is advantageous to determine these faults as quickly as possible. This study uses an Artificial Neural Network technique to locate a fault as soon as it happens on the Doukan-Erbil of 132kv double Transmission lines network. CYME 7.1-Programming/Simulink utilized simulation to model the suggested network. A multilayer perceptron feed-forward artificial neural network with a back propagation learning algorithm is used for the intelligence locator's training, testing, assessment, and validation. Voltages and currents were applied as inputs during the neural network's training. The pre-fault and post-fault values determined the scaled values. The neural network's p