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

Asthma is a chronic respiratory disorder in which immunological and structural cells play a role. The limits of conventional medicines necessitate the development of innovative therapeutic techniques for asthma. In the present study, we investigated the possible protective effect of cinnamic acid (CA) on ovalbumin-induced asthma in a mouse model. Sixty albino male mice BALB/c type weighing (20-30) grams were chosen at random and divided into five groups each one contains 12 animal: Group I: PBS/liquid paraffin control. Group II: asthma model group. Group III: cinnamic acid control group; mice received cinnamic acid (50 mg/kg) in liquid paraffin orally by gavage. Group IV: asthma model / group of (25 mg / kg) cinnamic acid; mice received

This study was planned to evaluate the renal function tests and liver function tests and it carried out in Al-Yarmouk hospital,Baghdad –Iraqin patients withtype 1 and type 2 diabetes mellitus by measuring(uric acid,urea and creatinine) ,Aspartate aminotransferase (AST) and Alanine aminotransferase (ALT). Seventy five individuals of Iraqi adults (male) were divided into three groups, 25 patients with type1 diabetes mellitus ,25 patients with type 2 diabetes mellitus and 25 normal individuals were taken as control group. The mean value of uric acid, urea and creatinine was higher significantly in patients thanin control group (P< 0.05),while the correlation(p< 0.01) between age ,creatinine in type 1 and between age and (Urea, Uric acid ,cr

A nano-sensor for nitrotyrosine (NT) molecule was found by studying the interactions of NT molecule with new B24N24 nanocages. It was calculated using density functionals in this case. The predicted adsorption mechanisms included physical and chemical adsorption with the adsorption energy of −2.76 to −4.60 and −11.28 to −15.65 kcal mol−1, respectively. The findings show that an NT molecule greatly increases the electrical conductivity of a nanocage by creating electronic noise. Moreover, NT adsorption in the most stable complexes significantly affects the Fermi level and the work function. This means the B24N24 nanocage can detect NT as a Φ–type sensor. The recovery time was determined to be 0.3 s. The sensitivity of pure BN na

The meniscus has a crucial function in human anatomy, and Magnetic Resonance Imaging (M.R.I.) plays an essential role in meniscus assessment. It is difficult to identify cartilage lesions using typical image processing approaches because the M.R.I. data is so diverse. An M.R.I. data sequence comprises numerous images, and the attributes area we are searching for may differ from each image in the series. Therefore, feature extraction gets more complicated, hence specifically, traditional image processing becomes very complex. In traditional image processing, a human tells a computer what should be there, but a deep learning (D.L.) algorithm extracts the features of what is already there automatically. The surface changes become valuable when

BACKGROUND: HLA-B27 can effect clinical presentation and course of ankylosing spondylitis. Different detection techniques of HLA-B27 are available with variable sensitivities and specificities. OBJECTIVE: To compare serologic and molecular diagnostic techniques of detecting HLA-B27 status and to correlate it with some clinical variables among ankylosing spondylitis patients. PATIENTS AND METHODS: A cross-sectional study was conducted on 83 Iraqi patients with ankylosing spondylitis. Clinical and laboratory evaluations were reported. HLA-B27 status was determined in all patients by real-time PCR using HLA-B27 RealFast™ kit; ELISA method was used as well to detect soluble serum HLA-B27 antigens using Human Leukocyte Antigen® kit. RESULTS: