Burst‐aware virtual machine migration for improving performance in the cloud

Burst is a common pattern in the user's requirements, which suddenly increases the workload of virtual machines (VMs) and reduces the performance and energy efficiency of cloud computing systems (CCS). Virtualization technology with the ability to migrate VMs attempts to solve this problem. By migration, VMs can be dynamically consolidated to the users' requests. Burst temporarily increases the workload. Ignoring this issue will lead to incorrect decisions regarding the migration of VMs. It increases the number of migrations and Service Level Agreement Violations (SLAVs) due to overload. This may cause waste of resources, increase in energy consumption, and misplaced VMs. Therefore, a burst‐aware method for these issues is proposed in this paper. The method consists of two algorithms: one for determining the migration time and the other for the placement of VMs. We use the PlanetLab real dataset and CloudSim simulator to evaluate the performance of the proposed method. The results confirm the advantages of the method regarding performance compared to benchmark methods.     

Monolayer Boron Nitride Substrate Interactions with Graphene Under In-Plane and Perpendicular Strains: A First-Principles Study

Effects of strain on the electronic and optical properties of graphene on monolayer boron nitride (BN) substrate are investigated using first-principle calculations based on density functional theory. Strain-free graphene/BN has a small band gap of 97 meV at the K point. The magnitude of band gap increases with in-plane biaxial strain while it decreases with the perpendicular uniaxial strain. The \( \varepsilon_{2} (\omega ) \) spectrum of graphene/BN bilayer for parallel polarization shows red and blue shifts by applying the in-plane tensile and compressive strains, respectively. Also the positions of peaks in the \( \varepsilon_{2} (\omega ) \) spectrum are not significantly changed under perpendicular strain. The calculated results indicate that graphene on the BN substrate has great potential in microelectronic and optoelectronic applications.     

Preparation of Pulse Electrodeposited Ni-B Coating with RSM Software and Evaluation of Its Microhardness and Electrochemical Behavior

Metallurgical and Materials Transactions A - In this study, the electrodeposition parameters were optimized to attain Ni-B alloy coatings with high hardness and corrosion resistance. The optimum...     

Two new methods for ranking of Z‐numbers based on sigmoid function and sign method

This paper introduces two processes of ranking methods on Z‐numbers that are effectively able to deal with uncertain decision‐making data. Decision making is based on recommended Z‐ numbers. For this purpose first, the Z‐number is transformed to a fuzzy number and then the ranking method by using the sigmoid function and the sign method is used to mention fuzzy numbers. For the next step, the method is extended to related Z‐numbers. Finally, we use it to prioritize the items and solve some examples.     

Using structural information for distributed recommendation in a social network

Social networks are social structures that depict relational structure of different entities. The most important entities are usually located in strategic locations within the network. Users from such positions play important roles in spreading the information. The purpose of this research is to make a connection between, information related to structural positions of entities and individuals advice selection criteria in a friendship or trust network. We explore a technique used to consider both frequency of interactions and social influence of the users. We show, in our model, that individual positions within a network structure can be treated as a useful source of information in a recommendation exchange process. We then implement our model as a trust-based exchange mechanism in NetLogo, which is a multi-agent programmable modeling environment. The experimental results demonstrate that structural position of entities can indeed retain significant information about the whole network. Utilizing social influence of entities leads to an increase in overall utility of the system.     

Color image segmentation using neuro-fuzzy system in a novel optimized color space

Segmentation is one of the most important pre-processing steps toward pattern recognition and image understanding. It is often used to partition an image into separate regions, which ideally correspond to different real-world objects. In this paper, novel color image segmentation is proposed and implemented using fuzzy inference system in optimized color space. This system, which is designed by neuro-adaptive learning technique, applies a sample image as an input and can reveal the likelihood of being a special color for each pixel through the image. The intensity of each pixel shows this likelihood in the gray-level output image. After choosing threshold value, a binary image is obtained, which can be applied as a mask to segment desired color in input image. Besides using fuzzy systems, optimizing color space for segmentation is another feature of proposed method. This optimizing is implemented by genetic algorithms and influence on system accuracy. Two applications of developed method are discussed, and still it could be applicable in wide range of color image segmentation or object detection purposes.     

Selective voltammetric determination of norepinephrine in the presence of acetaminophen and tryptophan on the surface of a modified carbon nanotube paste electrode

A new electrochemical sensor for the determination of norepinephrine (NE), acetaminophen (AC) and tryptophan (TRP) is described. The sensor is based on carbon paste electrode (CPE) modified with 5-mino-3′,4′-dimethyl-biphenyl-2-ol (5ADB) and takes the advantages of carbon nanotubes (CNTs), which makes the modified electrode highly sensitive for the electrochemical detection of these compounds. Under the optimum pH of 7.0, the oxidation of NE occurs at a potential about 170 mV less positive than that of the unmodified CPE. Also, square wave voltammetry (SWV) was used for the simultaneous determination of NE, AC and TRP at the modified electrode.     

Nigella sativa L. improves lipid profile and prevents atherosclerosis: Evidence from an experimental study on hypercholesterolemic rabbits

The effects of Nigella sativa (NS) on lipid profile, C-reactive protein, oxidized LDL, antioxidant capacity, Apo A and Apo B and fatty streak formation in hypercholesterolemic male rabbits were examined. Fifteen rabbits were distributed into three groups of five each [normal diet, hypercholesterolemic diet (1% cholesterol) and hypercholesterolemic diet (1% cholesterol) supplemented with 5% NS]. They received rabbit laboratory chow diet for a period of 8 weeks. At the start and end of the study, fasting blood was taken from all animals. NS significantly decreased fatty streak formation as well as total cholesterol (TC) and low-density lipoprotein (LDL) in hypercholesterolemic male rabbits. NS showed positive effects on other factors (triacylglycerol, high-density lipoprotein, C-reactive protein, oxidized LDL, antioxidant capacity, Apo A and Apo B), but these were insignificant. Based on favorable efficiency of NS on TC and LDL reduction and its anti-atherogenic cardioprotective properties, it is suggested using this plant with fat-rich diets simultaneously may reduce their adverse health effects.