Background: Denture relining is the process of resurfacing of the tissue side of the ill fitting denture, the bond strength at the relining-denture base interface is most important for denture durability.The aim of present study was to evaluate the shear bond strength between the thermosens as relining material and different denture base materials that bonded by thermo fusing liquid. As this corrective procedureis the common chair side procedure in the dental clinic. Material and method: Sixty samples were prepared and divided into three main groups according to the type of denture base materials.Group (A) referred to the heat cure acrylic samples which consisted of 20 samples. Group (B) referred to the high impact acrylic samples which con

Abstract

Objective(s): The study aims to measure the effectiveness of the program on removing dead tissue for burn patients by testing the nurses before the program in addition to testing them again after implementing the educational program.

Methodology: The study is quantitative in nature (one experimental) and will employ pre- and post-testing techniques between October 17, 2020 and March 20, 2022. A non-probability (purposive) sample of 24 nurses working in the Azadi Teaching Hospital's Burns and Plastic Surgery Center was chosen. The experimental survey of nursing practice, a literature review, scientific records, and previous research were all taken into considerat

   Adsorption of lead ions from wastewater by native agricultural waste, precisely tea waste. After the activation and carbonization of tea waste, there was a substantial improvement in surface area and other physical characteristics which include density, bulk density, and porosity. FTIR analysis indicates that the functional groups in tea waste adsorbent are aromatic and carboxylic. It can be concluded that the tea waste could be a good sorbent for the removal of Lead ions from wastewater. Different dosages of the adsorbents were used in the batch studies. A random series of experiments indicated a removal degree efficiency of lead reaching (95 %) at 5 ppm optimum concentration, with adsorbents R² =97.75% for tea. Three mo

Background: The denture base inaccuracies during processing negatively influence the retention and stability of finished complete denture. The aims of this study were to evaluate teeth movement and palatal adaptation of autoclave cured denture bases and their relationship with palatal depths and investments. Materials and methods: A nightly maxillary complete dentures prepared, processed and organized to be tested as follows: 1. Processing methods: water bath and autoclave with both fast and slow cycles. 2. Palatal depth: shallow, medium and deep. 3. Investing medium: stone and silicone. For every finished denture, two measurements were done: first: teeth movement by attaching metallic reference screws on the right and left centrals, first

The taxonomy of Ficus L., 1753 species is confusing because of the intense morphological variability and the ambiguity of the taxa. This study handled 36 macro-morphological characteristics to clarify the taxonomic identity of the taxa. The study revealed that Ficus is represented in the Egyptian gardens with forty-one taxa; 33 species, 4 subspecies and 4 varieties, and classified into five subgenera: Ficus Corner, 1960; Terega Raf., 1838; Sycomorus Raf., 1838; Synoecia (Miq.) Miq., 1867, and Spherosuke Raf.,1838; out of them seven were misidentified. Amongst, four new Ficus taxa were recently introduced to Egypt namely: F. lingua subsp. lingua Warb. ex De Wild. & T. Durand, 1901; F. pumila L., 1753; F. rumphii Blume, 1825, and F. su

MR Younus, 1998

The meniscus has a crucial function in human anatomy, and Magnetic Resonance Imaging (M.R.I.) plays an essential role in meniscus assessment. It is difficult to identify cartilage lesions using typical image processing approaches because the M.R.I. data is so diverse. An M.R.I. data sequence comprises numerous images, and the attributes area we are searching for may differ from each image in the series. Therefore, feature extraction gets more complicated, hence specifically, traditional image processing becomes very complex. In traditional image processing, a human tells a computer what should be there, but a deep learning (D.L.) algorithm extracts the features of what is already there automatically. The surface changes become valuable when