The logistic regression model is one of the oldest and most common of the regression models, and it is known as one of the statistical methods used to describe and estimate the relationship between a dependent random variable and explanatory random variables. Several methods are used to estimate this model, including the bootstrap method, which is one of the estimation methods that depend on the principle of sampling with return, and is represented by a sample reshaping that includes (n) of the elements drawn by randomly returning from (N) from the original data, It is a computational method used to determine the measure of accuracy to estimate the statistics, and for this reason, this method was used to find more accurate estimates. The maximum potential and the character regression estimations were found in estimating the two-response logistic regression model by adopting the bootstrap method and comparing the estimations according to the standard mean squares of error (MSE).It was evident through comparison that the character regression method in estimating the two-response logistic regression model by adopting the bootstrap method is the best in estimating the logistic regression model parameters as it has less (MSE).
Peer-Reviewed Journal
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A nano-sensor for nitrotyrosine (NT) molecule was found by studying the interactions of NT molecule with new B24N24 nanocages. It was calculated using density functionals in this case. The predicted adsorption mechanisms included physical and chemical adsorption with the adsorption energy of −2.76 to −4.60 and −11.28 to −15.65 kcal mol−1, respectively. The findings show that an NT molecule greatly increases the electrical conductivity of a nanocage by creating electronic noise. Moreover, NT adsorption in the most stable complexes significantly affects the Fermi level and the work function. This means the B24N24 nanocage can detect NT as a Φ–type sensor. The recovery time was determined to be 0.3 s. The sensitivity of pure BN na
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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
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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
... Show MoreAdsorption 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
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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
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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
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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
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