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

Volunteerism is an element included in many human cultures. It represents a positive cooperative act between individuals and groups. It expresses the social value systems. As a social phenomenon, it develops in societies according to innumerous circumstances and conditions. This study uses a functional approach that assumes that volunteering performs six functions for volunteers. Namely, we assume that volunteering (1) creates a sense of protection (2) meets significant cultural values (3) improves professional status of volunteers, (4) strengthens their social relationships, (5) helps them achieve a better understanding of life, and finally, (6) enhances their outlook and self-esteem. The central aim of the study is to discuss these fun

Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature

The present work aimed to investigate the neuraminidase (nan1) gene expression in 32 different clinical isolates of Pseudomonas aeruginosa to explore the role of the enzyme in different types of infection and might give a better understanding of host cell-pathogens interaction. In addition, the effect of monosaccharide D-mannose on neuraminidase gene expression in eight isolates was studied by utilizing a reverse transcription-quantitative polymerase chain reaction (RT-qPCR). The results demonstrated that the highest expression of nan1 gene was in otitis samples (208,913.81) which were significantly higher than that from other infections (P < 0.01). While, the concentrations of gene copies obtained from urin

Recommendation systems are now being used to address the problem of excess information in several sectors such as entertainment, social networking, and e-commerce. Although conventional methods to recommendation systems have achieved significant success in providing item suggestions, they still face many challenges, including the cold start problem and data sparsity. Numerous recommendation models have been created in order to address these difficulties. Nevertheless, including user or item-specific information has the potential to enhance the performance of recommendations. The ConvFM model is a novel convolutional neural network architecture that combines the capabilities of deep learning for feature extraction with the effectiveness o

The steady 3-D raw water turbulent flow is numerically investigated. This flow is formed of solid silica sand (quartz) carried by water in stainless steel pipe. The flow in a straight pipe and flow in a pipe with a sudden contraction are analyzed using a two-way coupled Eulerian-Lagrangian approach. Erosion rate is estimated by Oka erosion model combined with the constant coefficient of restitution. The effect of solid particles mass flow rate, inlet velocity, particle diameter, internal pipe diameter, orientation, contraction coefficient, and wall pipe contraction angle on erosion rate are examined. The predicted erosion is distributed homogenously for straight pipe, while the step wall area of the contraction is the most eroded part. The

The aim of the research is to investigate potential effects of the finance industry and block-chain to general business of financing in particular, as well as its shortcomings and difficulties. To answer the research questions, the researcher used the objective narrative-analytical descriptive approach and included a qualitative analysis of Blockchain technology. The process of Blockchain technology based on their industries, the authors were selected based on their reputation in the Blockchain field. The research found that Blockchain can improve the efficiency of the banking industry's various sections. It has the ability to upgrade and transfer wages across borders, financial reporting and compliance, as well as trade finance

Optimization of gas lift plays a substantial role in production and maximizing the net present value of the investment of oil field projects. However, the application of the optimization techniques in gas lift project is so complex because many decision variables, objective functions and constraints are involved in the gas lift optimization problem. In addition, many computational ways; traditional and modern, have been employed to optimize gas lift processes. This research aims to present the developing of the optimization techniques applied in the gas lift. Accordingly, the research classifies the applied optimization techniques, and it presents the limitations and the range of applications of each one to get an acceptable level of accura