Human herpes virus-8 (HHV-8) infection has increased recently in Arabic countries. HHV-8 in healthy persons does not necessarily cause life-threatening infection, and however, it causes a more severe infection among immunocompromised patients. The distribution of HHV-8 genotypes varies according to ethnicity and depends on the geographic region prior rapid development of global travel. A cross sectional prospective study included a hundred healthy blood donor samples with a mean age of (36.60±10.381), 81% were positive for molecular detection of HHV-8 DNA. PCR results for HHV-8 were strongly related with risk factors such as the number of sexual relations, previous surgeries, blood transfusion, dental operation, and the number of b

Tourist business organizations face a challenging and the risks dynamic environment reflected its impact on the community and generate extra under pressure in the responsibilities and burdens of exceptional and affected much of factors, accidents and risks as a result of the actions and attitudes of disasters variety may exceed the geography of States border, which requires the absorption of risks facing the tourism and how to manage and deal with them scientific and reasonable grounds for the diagnosis and treatment of risk and how to reduce the aggravation and the different kinds.

As risks affecting the most important and vital to organizations as a tourist aspects of the market share and styles tourist

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In This Paper, some semi- parametric spatial models were estimated, these models are, the semi – parametric spatial error model (SPSEM), which suffer from the problem of spatial errors dependence, and the semi – parametric spatial auto regressive model (SPSAR). Where the method of maximum likelihood was used in estimating the parameter of spatial error          ( λ ) in the model (SPSEM), estimated  the parameter of spatial dependence ( ρ ) in the model ( SPSAR ), and using the non-parametric method in estimating the smoothing function m(x) for these two models, these non-parametric methods are; the local linear estimator (LLE) which require finding the smoo

In this paper, the computational method (CM) based on the standard polynomials has been implemented to solve some nonlinear differential equations arising in engineering and applied sciences. Moreover, novel computational methods have been developed in this study by orthogonal base functions, namely Hermite, Legendre, and Bernstein polynomials. The nonlinear problem is successfully converted into a nonlinear algebraic system of equations, which are then solved by Mathematica®12. The developed computational methods (D-CMs) have been applied to solve three applications involving well-known nonlinear problems: the Darcy-Brinkman-Forchheimer equation, the Blasius equation, and the Falkner-Skan equation, and a comparison between the met

Modern civilization increasingly relies on sustainable and eco-friendly data centers as the core hubs of intelligent computing. However, these data centers, while vital, also face heightened vulnerability to hacking due to their role as the convergence points of numerous network connection nodes. Recognizing and addressing this vulnerability, particularly within the confines of green data centers, is a pressing concern. This paper proposes a novel approach to mitigate this threat by leveraging swarm intelligence techniques to detect prospective and hidden compromised devices within the data center environment. The core objective is to ensure sustainable intelligent computing through a colony strategy. The research primarily focusses on the

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