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.     

Designing a double-negative metamaterial photonic crystal using the Thue–Morse sequence

Journal of Computational Electronics - This study investigates the propagation of electromagnetic waves through a one-dimensional quasi-photonic crystal with the transfer matrix method. Our...     

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.     

MODELING OF DRESSING FORCES DURING ROTARY DIAMOND CUP DRESSING OF VITRIFIED CBN GRINDING WHEELS

This paper presents a mathematical model of dressing of vitrified CBN grinding wheels by a diamond cup dresser. It predicts the dressing forces during rotary diamond cup dressing of vitrified CBN grinding wheels. This model is based on the fracture of abrasive grits, the fracture of the bond and the contact forces between dresser and grinding wheel. It considers the kinematical influences and in particular speed ratio and overlap factor during the dressing process. A Weibull distribution is used to predict the probability of bond fracture and also the collision number between the diamond grits of a rotating dresser and the CBN grits. This model is validated by experimental results. The theoretical modeling values agree reasonably well with the experimental results. On the basis of this model the effect of different cup dressing parameters on dressing forces is theoretically discussed with the aim of establishing appropriate dressing process configurations. Furthermore the presented model provides a basis for further prediction of wheel topography and the grinding process.     

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.     

Traffic inputs for mechanistic-empirical pavement design guide using weigh-in-motion systems in Alberta

Mechanistic-empirical pavement design guide (MEPDG) uses axle load spectra and the number of axle applications to characterise traffic loads for pavement design. Alberta Transportation installed weigh-in-motion (WIM) systems at six highway locations to characterise traffic loads in Alberta for MEPDG design. Seasonal and regional trends in traffic characteristics of the six WIM sites were investigated and compared with the default values in the MEPDG for the years 2009 and 2010. Truck traffic classification (TTC) and axle load distribution factor (ALDF) for the WIM sites showed deviations from the MEPDG defaults. Seasonal variations were also evident in the distribution of different classes of truck throughout the year. Differences are attributed to cold climate conditions and special truck traffic in Alberta because of local industries. Influence of the differences between site-specific traffic characteristics and the MEPDG defaults on the performance of both flexible and rigid pavements for Alberta conditions was investigated through a sensitivity analysis. It was found that the flexible pavement performance is sensitive to TTC and ALDF, and the rigid pavement performance is most sensitive to ALDF.     

Automatic selection and fusion of color spaces for image thresholding

Automatic selection of color models has a great significance for machine vision purposes like image segmentation, object recognition, etc. Typically, selection of a proper color model is a problem that can just solve by testing the models on the target one by one. To achieve a proper color model, in this article, we propose a new method which is shaped on the basis of clustering and relation among models. The proposed method is verified experimentally for two different images (in thresholding purpose). The experimental results show that this method has a suitable power for automatic purposes.