Currently, with the huge increase in modern communication and network applications, the speed of transformation and storing data in compact forms are pressing issues. Daily an enormous amount of images are stored and shared among people every moment, especially in the social media realm, but unfortunately, even with these marvelous applications, the limited size of sent data is still the main restriction's, where essentially all these applications utilized the well-known Joint Photographic Experts Group (JPEG) standard techniques, in the same way, the need for construction of universally accepted standard compression systems urgently required to play a key role in the immense revolution. This review is concerned with Different

Abstract Software-Defined Networking (commonly referred to as SDN) is a newer paradigm that develops the concept of a software-driven network by separating data and control planes. It can handle the traditional network problems. However, this excellent architecture is subjected to various security threats. One of these issues is the distributed denial of service (DDoS) attack, which is difficult to contain in this kind of software-based network. Several security solutions have been proposed recently to secure SDN against DDoS attacks. This paper aims to analyze and discuss machine learning-based systems for SDN security networks from DDoS attack. The results have indicated that the algorithms for machine learning can be used to detect DDoS

Surface modeling utilizing Bezier technique is one of the more important tool in computer aided geometric design (CAD). The aim of this work is to design and implement multi-patches Bezier free-form surface. The technique has an effective contribution in technology domains and in ships, aircrafts, and cars industry, moreover for its wide utilization in making the molds. This work is includes the synthesis of these patches in a method that is allow the participation of these control point for the merge of the patches, and the confluence of patches at similar degree sides due to degree variation per patch.  The model has been implemented to represent the surface. The interior data of the desired surfaces designed by M

Attention-Deficit Hyperactivity Disorder (ADHD), a neurodevelopmental disorder affecting millions of people globally, is defined by symptoms of hyperactivity, impulsivity, and inattention that can significantly affect an individual's daily life. The diagnostic process for ADHD is complex, requiring a combination of clinical assessments and subjective evaluations. However, recent advances in artificial intelligence (AI) techniques have shown promise in predicting ADHD and providing an early diagnosis. In this study, we will explore the application of two AI techniques, K-Nearest Neighbors (KNN) and Adaptive Boosting (AdaBoost), in predicting ADHD using the Python programming language. The classification accuracies obtained w

Background: The present study was carried out to compare shear bond strength of sapphire bracket bonded to zirconium surface after using different methods of surface conditioning and assessment of the adhesive remnant index. Materials and methods: The sample composed of 40 zirconium specimens divided into four groups; the first group was the control, the second group was conditioned by sandblast with aluminum oxide particle 50 μm, the third and fourth group was treated by (Nd: YAG) laser (1064nm)(0.888 Watt for 5 seconds) for the 1st laser group and (0.444 Watt for 10 seconds) for the 2nd laser group. All samples were coated by z-prime plus primer. A central incisor sapphire bracket was bonded to all samples with light cure adhesive res

The thermal performance of a flat-plate solar collector (FPSC) using novel heat transfer fluids of aqueous colloidal dispersions of covalently functionalized multi-walled carbon nanotubes with β-Alanine (Ala-MWCNTs) has been studied. Multi-walled carbon nanotubes (MWCNTs) with outside diameters of (< 8 nm) and (20–30 nm) having specific surface areas (SSAs) of (500 m2/g) and (110 m2/g), respectively, were utilized. For each Ala-MWCNTs, waterbased nanofluids were synthesized using weight concentrations of 0.025%, 0.05%, 0.075%, and 0.1%. A MATLAB code was built and a test rig was designed and developed. Heat flux intensities of 600, 800, and 1000 W/m2; mass flow rates of 0.6, 1.0, and 1.4 kg/min; and inlet fluid temperatures of 30, 40, an

Autorías: Wafaa Sabah Mohammed Al-Khafaji, Fatimah Hameed Kzar Al-Masoodi, Suadad Ibrahim Suhail Al-Kinani. Localización: Revista iberoamericana de psicología del ejercicio y el deporte. Nº. 3, 2023. Artículo de Revista en Dialnet.

ackground: Escherichia coli is one of the most
important bacterial pathogen that can cause several
disease to human being . In our study we try to
investigate the sensitivity resistance pattern of
Escherichia coli against three antibiotics ( Amikacin,
Nalidixic acid and Cephalexin).
Methods: For this purpose we collected 51 clinical
isolates of Escherichia coli from stool and urine of
outpatient and inpatient patients from different wards
of AL-SADER Teaching Hospital in AL-NAJAF
AL-ASHRAf, IRAQ, and tested by culture and
sensitivity test .
Results: The results appeared that Amikacin show
the highest percentage of sensitivity ( 66.66 % ) ,
while Cephalexin show the lowest percentage of
sensiti