In this study, hydroxyapatite (HAP, Ca10(PO4)6(OH)2) has been prepared as bioceramic material with biological specifications useful to used for orthopedic and dental implant applications. Wet chemical processing seems to form the fine grain size and uniform characteristic nanocrystalline materials by the interstice factors controlling which affected the grain size and crystallinity in order to give good mechanical and/or constituent properties similar as natural bone. Fluorinated hydroxyapatite [4-6 wt% F, (FHA, Ca10(PO4)6(OH)2–Fx] was developed in new method for its posses to increased strength and to give higher corrosion resistance in biofluids than pure HAP moreover reduces the risk of dental caries. The phase's and functional groups

In this research, we have added nano anatase TiO₂ as a partial replacement of Portland cement by a weight percentage of (0.25 to 1%) for the development of properties for protection against bacteria. The control mix was made by using "the cement to sand" proportion about (1: 2.75) with the "water to cement" proportion of (0.5) to study the structure, porosity, water absorption, density, mechanical properties, as well as anti-bacterial behavior. Inspections have been done such as scanning electron microscopy (SEM), and atomic force microscope (AFM) for mortar. Experimental results showed that after the addition of Nano powders in cement mortar, the structural properties improved significantly with the development of hydration o

Objective: Breast cancer is regarded as a deadly disease in women causing lots of mortalities. Early diagnosis of breast cancer with appropriate tumor biomarkers may facilitate early treatment of the disease, thus reducing the mortality rate. The purpose of the current study is to improve early diagnosis of breast by proposing a two-stage classification of breast tumor biomarkers fora sample of Iraqi women.

Methods: In this study, a two-stage classification system is proposed and tested with four machine learning classifiers. In the first stage, breast features (demographic, blood and salivary-based attributes) are classified into normal or abnormal cases, while in the second stage the abnormal breast cases are

Support vector machine (SVM) is a popular supervised learning algorithm based on margin maximization. It has a high training cost and does not scale well to a large number of data points. We propose a multiresolution algorithm MRH-SVM that trains SVM on a hierarchical data aggregation structure, which also serves as a common data input to other learning algorithms. The proposed algorithm learns SVM models using high-level data aggregates and only visits data aggregates at more detailed levels where support vectors reside. In addition to performance improvements, the algorithm has advantages such as the ability to handle data streams and datasets with imbalanced classes. Experimental results show significant performance improvements in compa

    This article aims to explore the importance of estimating the a semiparametric regression function ,where we suggest a new estimator beside the other combined estimators and then we make a comparison among them by using simulation technique . Through the simulation results we find  that the suggest estimator is the best with the first and second models ,wherealse for the third model we find Burman and Chaudhuri (B&C) is best.

In this research, the methods of Kernel estimator (nonparametric density estimator) were relied upon in estimating the two-response logistic regression, where the comparison was used between the method of Nadaraya-Watson and the method of Local Scoring algorithm, and optimal Smoothing parameter λ was estimated by the methods of Cross-validation and generalized Cross-validation, bandwidth optimal λ has a clear effect in the estimation process. It also has a key role in smoothing the curve as it approaches the real curve, and the goal of using the Kernel estimator is to modify the observations so that we can obtain estimators with characteristics close to the properties of real parameters, and based on medical data for patients with chro

The human kidney is one of the most important organs in the human body; it performs many functions
and has a great impact on the work of the rest of the organs. Among the most important possible treatments is
dialysis, which works as an external artificial kidney, and several studies have worked to enhance the
mechanism of dialysate flow and improve the permeability of its membrane. This study introduces a new
numerical model based on previous research discussing the variations in the concentrations of sodium,
potassium, and urea in the extracellular area in the blood during hemodialysis. We simulated the differential
equations related to mass transfer diffusion and we developed the model in MATLAB Simu