The aim of these investigations is to provide a stochastic deep neural network optimization procedure for the numerical solutions of the fractional order influenza disease system. The fractional order form of the derivatives to solve the nonlinear models provide more realistic solutions as compared to integer order derivatives. The mathematical system based on the influenza model is categorized into population of birds (susceptible, infected) and humans (susceptible, infected with avian and mutant strains, recovered after avian and mutant strains). A stochastic platform is constructed using the feed-forward deep neural network with two hidden layers having 20 and 32 numbers of neurons, sigmoid activation function in both layers and optimization is performed through a competent Bayesian regularization for solving the model. A dataset is created using the traditional explicit Runge-Kutta solver, which is performed to lessen the mean square error by separating the data into training as 70%, while 15% for both authentication and testing. The accuracy is observed through different statistical operators including regression and state transitions, while correctness is achieved based on the results overlapping and minor absolute error.

Energy is an important parameter that gives clues about the developments in economies. Energy consumption in countries gives a summary of the economic structures of those countries. It is known in the literature that energy consumption has a stimulating effect on economic growth. Today, the attention-grabbing component of economic growth, apart from the real sector determinants, is considered to be financial development. In this study, the relationship between energy consumption and financial development in MENA countries is examined. Econometric analyzes are carried out by Hatemi J (2011) Panel Hidden Cointegration Test and Panel VECM analysis in the study covering the period between 1980 and 2017. The test results have showed that there is no cointegration between the original values of the variables, but there is a long-term relationship between some of the positive and negative components. Therefore, it has been determined that there is a hidden cointegration between the series. In addition, as a result of the causality test, no causality relationship is found between the components of energy consumption and financial development in the short-run. In the long run, a two-way causality relationship has been found between the positive component of energy consumption and the negative component of financial development, and a one-way causality relationship is determined from the positive component of financial development to the positive component of energy consumption. In light of these findings, the cointegration and especially the long-term causality relationship between the components of the variables reveal that the change in financial development indicators is effective on energy consumption. In conclusion, practices in the field of financial development should be emphasized as an indicator that can guide energy policies. \nKeywords: Energy Consumption, Financial Development, MENA Countries, Panel Hidden Cointegration

Graph theory can be applied to Biology for better results. Tree diagram plays an important role in Phylogenetic analysis. The interconnections and inter distance of various taxa can be visualise easily with the help of these diagrams. Trees are connected acyclic graphs. Suppose we are given a distance matrix D of order 2 x 2 (in the case of 2 taxa) or 3 x 3(in the case of three taxa). This work explains the construction of a tree in R2 or R3 space from the given distance matrix D such that the taxa preserves the distance same as that of the given matrix D. The paper include theorems to find out the number of tree structures and isomorphic tree structures possible in each case and also the transformation from one tree structure to another on the basis of operations on graph.

The study of spatial and subcellular organization of proteins has opened up another layer of cellular regulation which cannot be inferred directly from the level of protein and gene expression. The function of a protein is closely linked to the subcellular compartment where it is expressed since there are variations in the redox conditions, pH and interaction partners, between different organelles. The measurement of these intracellular proteomic and spatiotemporal variations can therefore provide a novel readout of the phenotypic state of individual human cells by shedding more light on the roles of heterogeneity in tumor formation.

Two simple and effective positivity preserving numerical algorithms are proposed\nfor the numerical solution of partial differential equations. The proposed techniques\nare based on the use of a suitable transformations while preserving the convergence\nprocess. An operator splitting method is proposed to avoid the singularity introduced\nby one of the transformations. For the second approach, in order to avoid the severe\nnonlinearities introduced by the transformation and reduce the computational time,\noperator splitting is required. The physical property of positivity is satisfied for every\nchoice of positive initial conditions. Numerical examples are presented to demonstrate\ncomputationally the application of the method.

Distributed Denial of Service attacks (DDoS) penetrate numerous computer system and implant malicious codes thereby making them ready for launching a collaborative attack. These attacks paralyze the target system mainly the web server by exhausting their network resources of the target server. The threats posed by DDoS attacks on the Internet demands for effective detection and mitigation methods of these attacks. In the paper, we proposed an integrated method for detection and mitigation of DDoS attack using machine learning and a line of defences respectively. The detection phase consists of feature selection through ensemble feature selection algorithm and classification using machine learning algorithm. Feature selection algorithms are important as they reduce the dimension of the dataset. Selection of an efficient classification model will improve the detection rate of the proposed system. In the mitigation phase, we introduce two lines of defences to minimize the exhaustion of victim server’s resources. Using the existing dataset, we show experimentally that it is possible to detect the presence of attacks and mitigate them to a minimum level. The proposed integrated method yields an accuracy of 97.8% in detecting the attacks and able to reduce the utilization of processor upto an average of 25.95%.

The latest on the cloud resource management research has revealed that virtual machine (VM) consolidation allows of effectively manage physical resources of cloud data centers. However, a tremendous waste of physical resources has been pointed as one of the research challenges related to the development of new methods for VM management in a cloud data center in order to effectively deliver a wide range of IT services to clients. This paper investigates a problem of SLA-aware VM consolidation under dynamic workloads, uncertainty, and changing number of VMs. For this purpose, the authors propose a dynamic VM management method based on a beam search which takes into account four types of resources (CPU, memory, network throughput, and storage throughput) and six quality metrics. Optimal beam search algorithm parameters for the defined problem are determined using a new power-aware integral estimation method. The authors define two objectives for the problem, namely a power consumption minimization and an SLA violations minimization aiming to determine model parameters for both objectives separately. As a result, the authors conclude that the SLA violations minimization is a preferred optimization technique compared to the power consumption minimization because of significant improvement of SLA quality metrics accompanied by the decreased number of VM migrations and by slight deterioration in the power consumption. The proposed method is evaluated using regular widespread hardware configurations and Bitbrains workload traces. The experiments show that the proposed approach can ensure the efficient use of cloud resources in terms of the SLA violation and the number of VM migrations.

Objectives: Health literacy has a key role in protecting and improving health and helping individuals to make decisions about their health. This study aimed to analyze the effects of university students’ e-health literacy on their healthy lifestyle behaviors.\nMethods: This is a descriptive and cross-sectional study. It was conducted with 1,714 students in a university in the east of Turkey during the 2018-2019 academic year. The data were collected using a demographic information form, the e-Health Literacy Scale and the Healthy Lifestyle Behaviors Scale II in face-to-face interviews.\nResults: The participants’ mean e-Health Literacy Scale score was 27.80±6.12, and their mean Healthy Lifestyle Behaviors Scale II score was 125.87±19.18. These scores did not vary significantly by age, gender or residence. They did vary significantly by year of study and faculty. A positive significant relationship was found between the participants’ total e-Health Literacy Scale and Healthy Lifestyle Behaviors Scale II scores.\nConclusion: The participants’ mean e-Health Literacy Scale score was above the moderate level, and their mean Healthy Lifestyle Behaviors Scale II score was at a moderate level. Higher mean e-Health Literacy Scale scores correlated with higher mean Healthy Lifestyle Behaviors Scale II scores.