A novel frequency-selective metamaterial to improve helix antenna

A novel frequency-selective metamaterial with negative permittivity and permeability for improving directivity and gain of a helix antenna is presented in this paper.The proposed metamaterial is composed of two Z-shape resonators printed on opposite sides of a dielectric substrate.Two forms of multilayered cells are found to be suitable for antennas and waveguides applications.In addition,a new method of designing a metamaterial-based helix antenna is presented with high directivity and gain.A comparison on radiation properties is given between the conventional and the new metamaterial-based helix antennas.Two comparisons on radiation properties are performed:(1) the effect of proposed Z-structure on monopole,dipole,and helix antennas;(2) the effect of OE3,split-ring resonator (SRR),and proposed Z-structure unit cells on the performance of helix antennas.The results show improvement of parameters such as directivity,gain,and radiation power of the new metamaterial-based helix antenna.Therefore,the combination of Z-structure with the helix antenna shows the best performance.     

A comprehensive review of renewable energy resources for electricity generation in Australia

Recently, renewable energy resources and their impacts have sparked a heated debate to resolve the Australian energy crisis. There are many projects launched throughout the country to improve network security and reliability. This paper aims to review the current status of different renewable energy resources along with their impacts on society and the environment. Besides, it provides for the first time the statistics of the documents published in the field of renewable energy in Australia. The statistics include information such as the rate of papers published, possible journals for finding relative paper, types of documents published, top authors, and the most prevalent keywords in the field of renewable energy in Australia. It will focus on solar, wind, biomass, geothermal and hydropower technologies and will investigate the social and environmental impacts of these technologies.     

Preliminary investigation of the combined effect of heat treatment and incubation temperature on the viability of the probiotic micro-organisms in freshly made yogurt

The combined effects of heat treatment and incubation temperature were studied on the viability and fermentation time of probiotics in ABY 1 probiotic yogurt ( Lactobacillus acidophilus , Bifidobacterium spp. and yogurt bacteria). Three levels of heat treatment (85°C for 30 min, 95°C for 5 min and 95°C for 15 min) and three levels of incubation temperature (37, 40 and 44°C) were selected. At the end of fermentation, the maximum viability of probiotics (for both L. acidophilus and Bifidobacterium spp . ) was observed when the milk was heated at 95°C for 15 min and incubated at 37°C. Incubation time was only affected by the incubation temperature and, at 37°C, the longest incubation time of about 6 h was needed to achieve the highest viable counts of L. acidophilus and Bifidobacterium spp.     

Effects of guar gum and arabic gum on the physicochemical,sensory and flow behaviour characteristics of frozen yoghurt

This study investigated the effect of guar gum and arabic gum on physicochemical, sensory and flow behaviour properties of frozen yoghurt. The results indicated that gums significantly affected the viscosity, overrun and melting rate of frozen yoghurt. The highest overrun value was observed in sample containing 0.5% arabic gum. Frozen yoghurt containing 0.3% guar gum had the highest viscosity. The longest first dripping time was observed in sample containing 0.5% arabic gum. Flow behaviour of samples showed that all frozen yoghurts exhibited shear thinning behaviour. Guar gum at a concentration of 0.2% and arabic gum at a concentration of 0.5% presented the best total acceptability. The results of this study revealed that the frozen yoghurt produced with arabic gum had the better overall sensory and physicochemical characteristics.     

Shear stress distribution prediction in symmetric compound channels using data mining and machine learning models

Shear stress distribution prediction in open channels is of utmost importance in hydraulic structural engineering as it directly affects the design of stable channels. In this study, at first, a series of experimental tests were conducted to assess the shear stress distribution in prismatic compound channels. The shear stress values around the whole wetted perimeter were measured in the compound channel with different floodplain widths also in different flow depths in subcritical and supercritical conditions. A set of, data mining and machine learning algorithms including Random Forest (RF), M5P, Random Committee, KStar and Additive Regression implemented on attained data to predict the shear stress distribution in the compound channel. Results indicated among these five models; RF method indicated the most precise results with the highest R² value of 0.9. Finally, the most powerful data mining method which studied in this research compared with two well-known analytical models of Shiono and Knight method (SKM) and Shannon method to acquire the proposed model functioning in predicting the shear stress distribution. The results showed that the RF model has the best prediction performance compared to SKM and Shannon models.