Thirty nine (12.8%) isolates of Staphylococcus aureus were isolated from 304 healthy human (Nasal swabs). It was found that percentage of males that have S. aureus is more than female's percentage. These isolates (39) were tested with different tests. Twenty seven isolates (69.23 %) were positive for Staphylococcus protein —A (SPA) ,thirty seven ( 94.8 %) were positive for tube coagulase , thirty five ( 89.7 % ) were positive with clumping factor and thirty two ( 82.05 %) had 13 — hemolytic on blood agar. It was found that 100% of the isolates (39 isolates) were positive with one, two or three tests (tube coagulase, clumping factor and SPA).

Traumatic spinal cord injury is a serious neurological disorder. Patients experience a plethora of symptoms that can be attributed to the nerve fiber tracts that are compromised. This includes limb weakness, sensory impairment, and truncal instability, as well as a variety of autonomic abnormalities. This article will discuss how machine learning classification can be used to characterize the initial impairment and subsequent recovery of electromyography signals in an non-human primate model of traumatic spinal cord injury. The ultimate objective is to identify potential treatments for traumatic spinal cord injury. This work focuses specifically on finding a suitable classifier that differentiates between two distinct experimental

Background: Chronic otitis media (COM) of mucosal or squamous type is a common problem in otolaryngology practice, the active form of COM is characterized by discharge of pus and is treated by antibiotics to start with, the appropriate antibiotic should be prescribed to avoid antibiotic abuse and guarantee good outcome. Objectives:The objective of this study is to identify the causative organisms of active chronic active otitis media both (mucosal, squamous) type and test their sensitivity to various anti- microbial agents &compare with abroad studies.Methods:A prospective study was done on eighty patients, different ages and sexes were taken and carful history and examination was done, examination under microscope was done with carf

Routing protocols are responsible for providing reliable communication between the source and destination nodes. The performance of these protocols in the ad hoc network family is influenced by several factors such as mobility model, traffic load, transmission range, and the number of mobile nodes which represents a great issue. Several simulation studies have explored routing protocol with performance parameters, but few relate to various protocols concerning routing and Quality of Service (QoS) metrics. This paper presents a simulation-based comparison of proactive, reactive, and multipath routing protocols in mobile ad hoc networks (MANETs). Specifically, the performance of AODV, DSDV, and AOMDV protocols are evaluated and analyz

The use of non-parametric models and subsequent estimation methods requires that many of the initial conditions that must be met to represent those models of society under study are appropriate, prompting researchers to look for more flexible models, which are represented by non-parametric models                  

          In this study, the most important and most widespread estimations of the estimation of the nonlinear regression function were investigated using Nadaraya-Watson and Regression Local Ploynomial, which are one of the types of non-linear

Software-defined networking (SDN) presents novel security and privacy risks, including distributed denial-of-service (DDoS) attacks. In response to these threats, machine learning (ML) and deep learning (DL) have emerged as effective approaches for quickly identifying and mitigating anomalies. To this end, this research employs various classification methods, including support vector machines (SVMs), K-nearest neighbors (KNNs), decision trees (DTs), multiple layer perceptron (MLP), and convolutional neural networks (CNNs), and compares their performance. CNN exhibits the highest train accuracy at 97.808%, yet the lowest prediction accuracy at 90.08%. In contrast, SVM demonstrates the highest prediction accuracy of 95.5%. As such, an

Abstract:In this paper, some probability characteristics functions (moments, variances,convariance, and spectral density functions) are found depending upon the smallestvariance of the solution of some stochastic Fredholm integral equation contains as aknown function, the sine wave function

Background: The study aim was to evaluate thermocycling effect on microleakage of occlusal and cervical margins of MOD cavity filled with bulk filled composites in comparison to incrementally placed nanohybrid composite and to evaluate the difference in microleakage between enamel and dentin margins for the three materials groups. Materials and method: Forty eight maxillary first premolars were prepared with MOD cavities. Samples were divided into three groups of sixteen teeth according to material used: Grandio: Grandio. SDR: SDR +Grandio. X-tra: X-tra base + Grandio. Each group was subdivided into two according to be thermocycled or not. After 24 hrs immersion in 2% methylene blue, samples weresectioned and microleakage was estimated. Res

As a marker of systemic inflammation, raised (C-reactive protein (CRP)) concentrations which are still within the normal range have been associated with an increased inflammation of chronic renal diseases (CRD). The current study aimed to establish potential determinats of raised CRP concentrations in patients who treated in Heamodialysis room,then study the relationship between CRP& some biochemical parameters related CRD We used a CRP latex reagents Kit which is based on an immunological reaction between CRP antisera bounded to the biologically inert latex particles or with CRP in the test specimens of 19 patients with (CRD) mean age 48 years ,range = 30?65 & in 21 healthy subjects as control group their age range = 30 ?45 years. The