Silybum marianum, from which silymarin (SM) is extracted, is a medicinal herb. In the Biopharmaceutics Classification System, it is of the class II type, meaning it is almost completely insoluble in water. It has a number of therapeutic properties, including anti-inflammatory as well as properties that promote wound healing.

This research target is to promote the dissolution and solubility of SM by employing a technique called solid dispersion and then incorporating the formula of solid dispersion into a topical gel that can be used for wound healing.

Solid dispersion is a technique used to enhance solubility and dissolve pharmaceuticals that are not water-soluble. This method is widely used because of its low cos

Abstract

The aim of the current research is to prepare an integrated learning program based on mathematics standards for the next generation of the NYS and to investigate its impact on the development of the teaching performance of middle school mathematics teachers and the future thinking skills of their students. To achieve the objectives of the research, the researcher prepared a list of mathematics standards for the next generation, which were derived from a list of standards. He also prepared a list of the teaching competencies required for middle school mathematics teachers in light of the list of standards, as well as clarified the foundations of the training program and its objectives and the mathematical

Skull image separation is one of the initial procedures used to detect brain abnormalities. In an MRI image of the brain, this process involves distinguishing the tissue that makes up the brain from the tissue that does not make up the brain. Even for experienced radiologists, separating the brain from the skull is a difficult task, and the accuracy of the results can vary quite a little from one individual to the next. Therefore, skull stripping in brain magnetic resonance volume has become increasingly popular due to the requirement for a dependable, accurate, and thorough method for processing brain datasets. Furthermore, skull stripping must be performed accurately for neuroimaging diagnostic systems since neither no

Skull image separation is one of the initial procedures used to detect brain abnormalities. In an MRI image of the brain, this process involves distinguishing the tissue that makes up the brain from the tissue that does not make up the brain. Even for experienced radiologists, separating the brain from the skull is a difficult task, and the accuracy of the results can vary quite a little from one individual to the next. Therefore, skull stripping in brain magnetic resonance volume has become increasingly popular due to the requirement for a dependable, accurate, and thorough method for processing brain datasets. Furthermore, skull stripping must be performed accurately for neuroimaging diagnostic systems since neither non-brain tissues nor

The basic solution to overcome difficult issues related to huge size of digital images is to recruited image compression techniques to reduce images size for efficient storage and fast transmission. In this paper, a new scheme of pixel base technique is proposed for grayscale image compression that implicitly utilize hybrid techniques of spatial modelling base technique of minimum residual along with transformed technique of Discrete Wavelet Transform (DWT) that also impels mixed between lossless and lossy techniques to ensure highly performance in terms of compression ratio and quality. The proposed technique has been applied on a set of standard test images and the results obtained are significantly encourage compared with Joint P

   Predicting permeability is a cornerstone of petroleum reservoir engineering, playing a vital role in optimizing hydrocarbon recovery strategies. This paper explores the application of neural networks to predict permeability in oil reservoirs, underscoring their growing importance in addressing traditional prediction challenges. Conventional techniques often struggle with the complexities of subsurface conditions, making innovative approaches essential. Neural networks, with their ability to uncover complicated patterns within large datasets, emerge as a powerful alternative. The Quanti-Elan model was used in this study to combine several well logs for mineral volumes, porosity and water saturation estimation. This model goes be