Distance metrics for very large color differences

Small, supra-threshold color differences are typically described with Euclidean distance metrics, or dimension-weighted Euclidean metrics, in color appearance spaces such as CIELAB. This research examines the perception and modeling of very large color differences in the order of 10 CIELAB units or larger, with an aim of describing the salience of color differences between distinct objects in real-world scenes and images. A psychophysical experiment was completed to compare directly large color-difference pairs designed to probe various Euclidean and non-Euclidean distance metrics. The results indicate that very large color differences are best described by HyAB, a combination of a Euclidean metric in hue and chroma with a city-block metric to incorporate lightness differences.     

A neuro‐based approach to designing a Wilkinson power divider

In this paper, a new neuro‐based approach using a feed‐forward neural network is presented to design a Wilkinson power divider. The proposed power divider is composed of symmetrical modified T‐shaped resonators, which are a replacement for quarter‐wave transmission lines in the conventional structure. The proposed technique reduces the size of the power divider by 45% and suppresses unwanted bands up to the fifth harmonics. To verify the concept, a prototype of the power divider has been fabricated and tested, exhibiting good agreement between the predicted and measured results. The results show that the insertion loss and the isolation at the center frequency are about 3.3 ± 0.1 dB and 23 dB, respectively.     

Morphological optimization of electrospun polyacrylamide/MWCNTs nanocomposite nanofibers using Taguchi’s experimental design

The morphological characteristic of electrospun polyacrylamide/multi-walled carbon nanotube (PAAm/MWCNTs) nanocomposite nanofibers is optimized in this work using Taguchi’s experimental design. The optimization is performed considering the effect of PAAm concentration, MWCNTs content, flow rate, and applied voltage on average nanofibers diameter. The reasonable dispersion of MWCNTs in PAAm solution is first ascertained via optical microscopy method. The experimental data required for the optimization process are then provided by statistical calculations on field-emission scanning electron microscopy images of the samples formulated based on a designed L ₉ orthogonal array. PAAm concentration is found to have the most contribution on final fibers morphology according to the results obtained from simultaneous implementation of the analysis of variance and mean effect assessment. Therefore, PAAm concentration, which is in consistence with solution viscosity and surface tension parameter, is found to have the most contribution to forming nanofibers including the finest fiber diameter. On the contrary, the flow rate of solution among the selected parameters shows the least effect on average nanofiber diameter.     

Neural computations in modelling of CO2 capture from Gas stream emissions by Sodium Glycinate solution

The present contribution was performed in order to predict CO₂ loading capacity in aqueous sodium glycinate as a novel class of green solution under wide operating range using radial basis function artificial neural network (RBFANN). The predicted CO₂ loading capacity values were in brilliant agreement with those corresponding experimental values. The estimated values of MSE and R-squared were 0.00045 and 0.997, respectively. Accordingly, statistical and graphical analyses confirm satisfactory prediction of our proposed model.     

Nisin‐loaded alginate‐high methoxy pectin microparticles: preparation and physicochemical characterisation

The bacteriocin nisin has been extensively used as potential natural preservative in the food industry. However, antimicrobial activity of nisin due to its binding with food components and inactivation by enzymatic degradation is reduced when it is applied in food. Encapsulation of nisin is an efficient approach to overcome the problems related to the direct application of this antimicrobial peptide in foods. In this study, nisin was encapsulated in alginate‐high methoxy pectin (HMP) microparticles, and its release studies were performed in water to determine the diffusion and the kinetic behaviour of the matrix. Results showed that the nisin content had a significant influence on encapsulation efficiency (EE), loading capacity (LC) and microparticles size. The values of EE, LC and particle mean diameter were about 47–54%, 16–21% and 57–131 μm, respectively. The nisin‐loaded microparticles showed nearly spherical structure with fold on the surface, as displayed by scanning electron micrograph. Interaction between alginate and HMP was confirmed by the changes in the intensity and wave number of the stretching vibrations of the hydroxyl and carboxyl groups in alginate‐HMP microparticles FTIR spectra. Furthermore, the addition of nisin resulted in a markedly increase in intensity of carboxylic peak at 1620 cm^?1, indicating the presence of nisin inside of the microparticles. The in vitro nisin release from these microparticles followed a sustained release profile consistent with a Fickian diffusion mechanism.     

Crack detection of the cantilever beam using new triple hybrid algorithms based on Particle Swarm Optimization

The presence of cracks in a concrete structure reduces its performance and increases in the size of cracks result in the failure of the structure. Therefore, the accurate determination of crack characteristics, such as location and depth, is one of the key engineering issues for assessment of the reliability of structures. This paper deals with the inverse analysis of the crack detection problems using triple hybrid algorithms based on Particle Swarm Optimization (PSO); these hybrids are Particle Swarm Optimization-Genetic Algorithm-Firefly Algorithm (PSO-GA-FA), Particle Swarm Optimization-Grey Wolf Optimization-Firefly Algorithm (PSO-GWO-FA), and Particle Swarm Optimization-Genetic Algorithm-Grey Wolf Optimization (PSO-GA-GWO). A strong correlation exists between the changes in the natural frequency of a concrete beam and the crack parameters. Thus, the location and depth of a crack in a beam can be predicted by measuring its natural frequency. Hence, the measured natural frequency can be used as the input parameter of the algorithm. In this paper, this is applied to identify crack location and depth in a cantilever beam using the new hybrid algorithms. The results show that among the proposed triple hybrid algorithms, the PSO-GA-FA and PSO-GWO-FA algorithms are much more effective than PSO-GA-GWO algorithm for the crack detection.