In this research an analysis for improving the fatigue behavior (safety factor of fatigue) of non- articular prosthetic foot (SACH) in the region (Bolt Adapter).The laser peening was carried to the fatigue specimens to improving the fatigue properties of bolt’s material. The tests of mechanical properties and fatigue behavior were carried for material that the bolt manufacture from it, a region where the failure occur and inserted of these properties to the program of engineering analysis (Ansys) to calculate the safety factor of fatigue. The results showed that the safety factor after hardening by laser is increased by 42.8%.

A computational investigation is carried out in the field of charged particle optics with the aid of the numerical analysis methods. The work is concerned with the design of symmetrical double pole piece magnetic lens.  The axial magnetic flux density distribution is determined by using exponential model, from which the paraxial-ray equation is solved to obtain the trajectory of particles that satisfy the suggested exponential model.  From the knowledge of the first and second derivatives of axial potential distribution, the optical properties such as the focal length and aberration coefficients (radial distortion coefficient and spiral distortion coefficient) are determined.  Finally, the pole piece profiles capable of pr

The aim of this research is to employ starch as a stabilizing and reducing agent in the production of CdS nanoparticles with less environmental risk, easy scaling, stability, economical feasibility, and suitability for large-scale production. Nanoparticles of CdS have been successfully produced by employing starch as a reducing agent in a simple green synthesis technique and then doped with Sn in certain proportions (1%, 2%, 3%, 4%, and 5%).According to the XRD data, the samples were crystallized in a hexagonal pattern, because the average crystal size of pure CdS is 5.6nm and fluctuates in response to the changes in doping concentration 1, 2, 3, 4, 5 %wt Sn, to become   4.8, 3.9, 11.5, 13.1, 9.3 nm respectively. An increase in crystal

The fabrication of Solid and Hollow silver nanoparticles (Ag NPs) has been achieved and their characterization was performed using transmission electron microscopy (TEM), zeta potential, UV–VIS spectroscopy, and X-ray diffraction (XRD). A TEM image revealed a quasispherical form for both Solid and Hollow Ag NPs. The measurement of surface charge revealed that although Hollow Ag NPs have a zeta potential of -43 mV, Solid Ag NPs have a zeta potential of -33 mV. According to UV-VIS spectroscopy measurement Solid and Hollow Ag NPs both showed absorption peaks at wavelengths of 436 nm and 412 nm, respectively. XRD pattern demonstrates that the samples' crystal structure is cubic, similar to that of the bulk materials, with

In this paper we present a method to analyze five types with fifteen wavelet families for eighteen different EMG signals. A comparison study is also given to show performance of various families after modifying the results with back propagation Neural Network. This is actually will help the researchers with the first step of EMG analysis. Huge sets of results (more than 100 sets) are proposed and then classified to be discussed and reach the final.

Background: Tooth wear is one of the most common problems in the older dentate population which results from the interaction of three processes (attrition, abrasion and erosion) and it affects all societies, different age groups, and all cultures. This study was achieved to evaluate the prevalence and distribution of tooth wear among institutionalized residents in Baghdad city\ Iraq. Subjects and Methods: This survey was accomplished on four private and one governmental institution in Baghdad city. One-hundred twenty three (61 males, 62 females) aged 50-89 years were participated in this study. The diagnosis and recording of tooth wear were according to criteria of Smith and Knight. Results: The prevalence of tooth wear was 100% with a mean

In this paper, the reliability and scheduling of maintenance of some medical devices were estimated by one variable, the time variable (failure times) on the assumption that the time variable for all devices has the same distribution as (Weibull distribution.

The method of estimating the distribution parameters for each device was the OLS method.

The main objective of this research is to determine the optimal time for preventive maintenance of medical devices. Two methods were adopted to estimate the optimal time of preventive maintenance. The first method depends on the maintenance schedule by relying on information on the cost of maintenance and the cost of stopping work and acc

    This research aims to review the importance of estimating the nonparametric regression function using so-called Canonical Kernel which depends on re-scale the smoothing parameter, which has a large and important role in Kernel  and give the sound amount of smoothing .

We has been shown the importance of this method through the application of these concepts on real data refer to international exchange rates to the U.S. dollar against the Japanese yen for the period from January 2007 to March 2010. The results demonstrated preference the nonparametric estimator with Gaussian on the other nonparametric and parametric regression estima

Rationing is a commonly used solution for shortages of resources and goods that are vital for the citizens of a country. This paper identifies some common approaches and policies used in rationing as well asrisks that associated to suggesta system for rationing fuelwhichcan work efficiently. Subsequently, addressing all possible security risks and their solutions. The system should theoretically be applicable in emergency situations, requiring less than three months to implement at a low cost and minimal changes to infrastructure.

After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings