The article presents new stochastic rules of nucleotide sequences in single-stranded DNA of eukaryotic and prokaryotic genomes. These discovered rules are candidates for the role of universal genomic rules. To reveal such rules, the authors represent any of genomic sequences in single-stranded DNA as a set of n parallel texts (or layers), each of which is written based on one of the different n-plets alphabets (n = 1, 2, 3, ...). Then comparison analysis of percentages of the 4n kinds of n-plets in the n parallel texts in such sequence is fulfield. In the result, unexpected stochastic rules of invariance of total sums of percentages for certain tetra-groupings of n-plets in different parallel texts of genomic DNA sequences are revealed.The presented rules significantly expand modern knowledge about stochastic regularities in long single-stranded DNA sequences, and they can be considered as generalizations of the second Chargaff's rule. A tensor family of matrix representations of interrelated DNA-alphabets of 4 nucleotides, 16 doublets, 64 triplets, and 256 tetraplets is used in the study. Some analogies of the discovered genetic phenomena with phenomena of Gestalt psychology are noted. The authors connect the received results about the genomic percentages rules with a supposition of P. Jordan, who is one of the creators of quantum mechanics and quantum biology, that life's missing laws are the rules of chance and probability of the quantum world.

Precise extrapolative mining and analysis of relevant dataset during or after any disease outbreak can assist the government, stake holders and relevant agencies in the health sector to make important decisions with respect to the disease outbreak control and management. While prior works has concentrated on non-stationary long term data, this work focuses on a short term non-stationary and relatively noisy data. Particularly, a distinctive nonparametric machine learning method based kernel-controlled probabilistic Gaussian process regression model has been proposed and employed to model and analyze Covid-19 pandemic data acquired over a period of approximately six weeks. To accomplish the aim, the MATLAB 2018a computational and machine learning environment was engaged to develop and perform the Gaussian process extrapolative analysis. The results displayed high scalability and optimal performance over the commonly used machine learning methods such as the Neural networks, Neural-Fuzzy networks, Random forest, Regression tree, Support Vector machines, K-nearest neighbor and Discriminant linear regression models. These results offer a solid foundation for conducting research on reliable prognostic estimations and analysis of contagious disease emergence intensity and spread.

The ring signature does hide the member of actually signature in the all possible signer. This technique was introduced by Rivest, Tauman and Shamir in 2001. This paper presents elliptic curve cryptosystem using signcryption algorithm with the anonymity feature. In the present paper, combines ring signature scheme with signcryption method to produce the anonymity feature for the signcryption scheme and discuss the characteristic of the security. Because elliptic curve cryptosystem yields like as small bandwidth requirements, low computation load, and high security and methodology of ring signature gives without revealing the actual signer to all possible signers. Combining the benefits of these two methodology, the result is an efficient anonymous signcryption algorithm and highly secure.

There exist numerous numerical methods for solving the initial value problems of ordinary differential equations. The accuracy level and computational time are not the same for all of these methods. In this article, the Modified Euler method has been discussed for solving and finding the accurate solution of Ordinary Differential Equations using different step sizes. Approximate Results obtained by different step sizes are shown using the result analysis table. Some problems are solved by the proposed method then approximated results are shown graphically compare to the exact solution for a better understanding of the accuracy level of this method. Errors are estimated for each step and are represented graphically using Matlab Programming Language and MS Excel, which reveals that so much small step size gives better accuracy with less computational error. It is observed that this method is suitable for obtaining the accurate solution of ODEs when the taken step sizes are too much small.

Cloud computing is a widely acceptable computing environment, and its services are also widely available. But the consumption of energy is one of the major issues of cloud computing as a green computing. Because many electronic resources like processing devices, storage devices in both client and server site and network computing devices like switches, routers are the main elements of energy consumption in cloud and during computation power are also required to cool the IT load in cloud computing. So due to the high consumption, cloud resources define the high energy cost during the service activities of cloud computing and contribute more carbon emissions to the atmosphere. These two issues inspired the cloud companies to develop such renewable cloud sustainability regulations to control the energy cost and the rate of CO2 emission. The main purpose of this paper is to develop a green computing environment through saving the energy of cloud resources using the specific approach of identifying the requirement of computing resources during the computation of cloud services. Only required computing resources remain ON (working state), and the rest become OFF (sleep/hibernate state) to reduce the energy uses in the cloud data centers. This approach will be more efficient than other available approaches based on cloud service scheduling or migration and virtualization of services in the cloud network. It reduces the cloud data center's energy usages by applying a power management scheme (ON/OFF) on computing resources. The proposed approach helps to convert the cloud computing in green computing through identifying an appropriate number of cloud computing resources like processing nodes, servers, disks and switches/routers during any service computation on cloud to handle the energy-saving or environmental impact.