Cloud computing has bring a revolution in the field of computing. Many algorithms are proposed to make it even more efficient. In cloud computing Virtualization plays an important role and whole performance of cloud depends on VM allocation and Migration. As lots of energy is consumed in this technology so algorithms to save energy and improve efficiency are proposed called Green algorithms. In this paper a green algorithm for VM Migration is proposed using metaheuristic algorithm called ACO. The variant of ACO used in this paper is Max-Min Ant System. Results show that Max-Min Ant System gives best result as compared to other approaches in terms of VM Migrations, VM consolidation and energy consumptions.

The development of undergraduate laboratories is more expensive if a hand on training approach is considered. Additionally, relatively much inexpensive software can work a standard personal computer (PC) into a virtual lab. The fundamental issues are to set up balance between real and virtual labs to rectify cost related issue while graduating engineers with sufficient practice. This article is useful in a virtual experiment for work of teachers and students in Electronics and Provides a precise and extensive design scheme by which learners can taught engineering experimentation and virtual instrumentation. This virtual experiment platform support classroom teaching using which the teacher can constitute a hi-tech atmosphere, as well as provides exercises and tests self learning of students. A Simulation enriches coaching and enlarges teaching quality. The educational problem is to improve student's experience of learning Electronics and telecommunication fundamentals using Lab VIEW. It was widely used in the laboratory sessions, to improve students for the project work.

In the last few years, jargon, such as machine learning (ML) and artificial intelligence (AI), have been ubiquitous in both popular science media as well as the academic literature. Many industries have tried the current suite of ML and AI algorithms with various degrees of success. Mineral processing, as an industry, is looking at AI for two reasons. First of all, as with other industries, it is pertinent to know if AI algorithms can be used to enhance productivity. The second reason is specific to the mining industry. Of late, the grade of ores is reducing, and the demand for ethical mining (with as little effect on ecology as possible) is increasing. Thus, mineral processing industries also want to explore the possible use of AI in solving these challenges. In this review paper, first, the challenges in mineral processing that can potentially be solved by AI are presented. Then, some of the most pertinent developments in the domain of ML and AI (applied in the domain of mineral processing) are discussed. Lastly, a top-level modus operandi is presented for a mineral processing industry that might want to explore the possibilities of using AI in its processes. Following are some of the new paradigms added by this review. This review presents a holistic view of the domain of mineral processing with an AI lens. It is also one of the first reviews in this domain to thoroughly discuss the use of AI in ethical, green, and sustainable mineral processing. The AI process proposed in this paper is a comprehensive one. To ensure the relevance to industry, the flow was made agile with the spiral system engineering flow. This is expected to drive rapid and agile investigation of the potential of applying ML and AI in different mineral processing industries.

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The discovery of cosmic microwave background (CMB) was a paradigm shift in the study and fundamental understanding of the early Universe and also the Big Bang phenomenon. Cosmic microwave background is one of the richest and intriguing sources of information available to cosmologists and one parameter of special interest is baryon density of the Universe. Baryon density can be primarily estimated by analyzing CMB data or through the study of big bang nucleosynthesis (BBN). Hence, it is necessary that both of the results found through the two methods are in agreement with each other. Although there are some well established statistical methods for the analysis of CMB to estimate baryon density, here we explore the use of deep learning in this respect. We correlate the baryon density obtained from the power spectrum of simulated CMB temperature maps with the corresponding map image and form the dataset for training the neural network model. We analyze the accuracy with which the model is able to predict the results from a relatively abstract dataset considering the fact that CMB is a Gaussian random field. CMB is anisotropic due to temperature fluctuations at small scales but on a larger scale CMB is considered isotropic, here we analyze the isotropy of CMB by training the model with CMB maps centered at different galactic coordinates and compare the predictions of neural network models.