This work presents the use of laser diode in the fiber distributed data interface FDDI networks. FDDI uses optical fiber as a transmission media. This solves the problems resulted from the EMI, and noise. In addition it increases the security of transmission. A network with a ring topology consists of three computers was designed and implemented. The timed token protocol was used to achieve and control the process of communication over the ring. Nonreturn to zero inversion (NRZI) modulation was carried out as a part of the physical (PHY) sublayer. The optical system consists of a laser diode with wavelength of 820 nm and 2.5 mW maximum output power as a source, optical fiber as a channel, and positive intrinsic negative (PIN) photodiode

   The region of Kirkuk and its surrounding areas, including (Baba, Jambour, Qara Chuq, Qaiyarah, Demir Dagh, Bai Hassan, Taq Taq, Makhul, Gilabat as well as southern Mosul and the cities of Erbil and Sulymania, are known as one of the oldest discovered oil fields in northern Iraq. This area presents a significant opportunity for further organic geochemical analysis to describe maturation zones and estimate economically generated hydrocarbons with particular reference to the Sargelu formation, to enhance hydrocarbons productivity. To assess the potential of these oil fields, it is essential to perform correlation, comparisons, and geochemical analyses of the data collected from exploration wells in the surrounding area. This appro

     This article aims to determine the time-dependent heat coefficient together with the temperature solution for a type of semi-linear time-fractional inverse source problem by applying a method based on the finite difference scheme and Tikhonov regularization. An unconditionally stable implicit finite difference scheme is used as a direct (forward) solver. While by the MATLAB routine lsqnonlin from the optimization toolbox, the inverse problem is reformulated as nonlinear least square minimization and solved efficiently. Since the problem is generally incorrect or ill-posed that means any error inclusion in the input data will produce a large error in the output data. Therefore, the Tikhonov regularization technique is applie

Abstract Background: Multidrug-resistant bacteria (MDR) often contaminate hospital environment and cause serious illnesses. Quorum Sensing (QS) regulates a variety of downstream cellular processes, including antibiotics resistance mechanisms and biofilm formation, and causes harm to the host. This study investigates antibacterial susceptibility and biofilm formation of pathogenic bacteria in hospital environment. Methods: Hundred bacterial isolates were collected from various environments in the Medical City hospital. The antimicrobial susceptibility technique was evaluated through disk diffusion method. Next, biofilms formation was detected by the microliter plate assay. Finally, PCR was used to analyze the frequency of QS system gene

     Community detection is an important and interesting topic for better understanding and analyzing complex network structures. Detecting hidden partitions in complex networks is proven to be an NP-hard problem that may not be accurately resolved using traditional methods. So it is solved using evolutionary computation methods and modeled in the literature as an optimization problem.  In recent years, many researchers have directed their research efforts toward addressing the problem of community structure detection by developing different algorithms and making use of single-objective optimization methods. In this study, we have continued that research line by improving the Particle Swarm Optimization (PSO) algorithm using a

Software-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration in a multitenant data center environment due to its programmability. As its usage rapidly expands, network security threats are becoming more frequent, leading SDN security to be of significant concern. Machine-learning (ML) techniques for intrusion detection of DDoS attacks in SDN networks utilize standard datasets and fail to cover all classification aspects, resulting in under-coverage of attack diversity. This paper proposes a hybr