Characterization of a cylindrical rod by inversion of acoustic scattering data

In this paper, a new approach is proposed for nondestructive characterization of immersed and embedded isotropic rod-shaped samples by inversion of acoustic scattering data. The normal mode expansion technique is used for modelling the scattered field and the compression incident and compression scattered waves are considered. Genetic algorithm is the inversion technique used for estimating the elastic wave velocities and density of the rods from their measured backscattered pressure spectrum. The inversion technique is capable of computing the parameter values that best fit a particular set of data. A perturbation study is conducted on the sensitivity of the resonance frequencies to changes in elastic properties and density of the rods. The numerical results indicate that proper selection of resonance frequencies leads to accurate measurement of elastic constants and density. The proposed approach showed very good convergence and the results obtained were found to agree very well with available data.     

Rapid and Highly Sensitive Detection of Target DNA Related to COVID-19 Virus With a Fluorescent Bio-conjugated Probe via a FRET Mechanism

Journal of Fluorescence - A novel cyanine 3 (Cy3)-based bio-conjugated sensor has been developed to detect target DNA or extracted RNA from COVID -19 samples using the fluorescence resonance energy...     

Sum of ranking differences in comparison of nickel-coated carbon nanofibers adsorbents in capacity and randomness of 1-butanethiol (1-butyl mercaptan) adsorption

Desulfurization of petroleum derivatives has been one of the main concerns of producers. Application of adsorption in desulfurization is one of the reliable strategies. Thus, development of new and better adsorbents is main goal of researchers. In this work, carbon nanofibers were coated with nickel by electroless plating method to be used in adsorption of 1-butanethiol (1-butyl mercaptan) for the first time. Nickel amount in these structures was varied and adsorption behavior studied. To characterize the prepared adsorbent, concentration variation of mercaptan was determined by attenuated total reflectance Fourier transform infrared spectroscopy with multivariate curve resolution–alternating least squares chemometrics method as a new way. Also, potentiometry method was used to determine the mercaptan concentration during the adsorption process as a standard method. The data which were achieved by the two mentioned methods are processed by sum of ranking differences chemometrics method separately to compare the adsorption behavior of adsorbents. By using sum of ranking differences, it was concluded that with increment in amount of coated nickel, adsorption capacity would increase and random behavior in adsorption process would decrease. Also, this study showed that attenuated total reflectance Fourier transform infrared spectroscopy with multivariate curve resolution–alternating least squares can be applied to study the concentration variations of mercaptan as a reliable method.     

A novel technique based on diffuse reflectance near-infrared spectrometry and back-propagation artificial neural network for estimation of particle size in TiO2 nano particle samples

Determination of particle size is one of the critical parameters in nanotechnology. The relationship between particle size and diffuse reflectance (DR) spectra in near-infrared region has been applied to introduce a method for estimation of particle size. Back-propagation artificial neural network (BP-ANN) as a nonlinear model was applied to estimate average particle size based on near-infrared diffuse reflectance spectra. Thirty five different nano TiO₂ samples with different particle size were analyzed by DR-FTNIR spectrometry and the obtained data were processed by BP-ANN. The network was trained by 30 samples and was evaluated by the remaining 5 samples. In order to establish whether the new method is applicable for estimation of particle size of nano structured samples, the optimized model was applied to analyze 44 nano TiO₂ samples. It was observed that ANN using the back-propagation algorithm is capable of generalization and could correctly predict the average particle size of nano-sized particles.     

Synthesis and characterization of nanocrystalline α-Al2O3 using Al and Fe2O3 (hematite) through mechanical alloying

In this present work, the synthesis of nanocrystalline α-Al₂O₃ using pure aluminum (Al) and Fe₂O₃ (hematite) as the precursors by mechanical alloying technique has been studied. The formation of α-Al₂O₃ nanocrystallites occurs during the solid-state reaction and through the reduction treatment. Also in this paper, effects of milling time on particle size and the lattice strain nanocrystalline α-Al₂O₃ have been investigated. Obtained powders were evaluated by scanning electron microscopy (SEM), transmission electron microscopy (TEM), differential thermal analysis (DTA), thermal gravimetric analysis (TGA) and X-ray diffraction (XRD). The obtained results indicated that a reduction reaction was completed after 2 h milling in a planetary mill. The crystallite size of obtained α-alumina (α-Al₂O₃) was in general about 12 nm. Finally, the results showed appropriate homogeneity and dispersion of related nanocrystalline.     

Infrared spectroscopic and chemometric approach for identifying binding medium in Sukias mansion’s wall paintings

Abstract

This paper addresses the application of infrared spectroscopy in combination with chemometrics to identify wall painting’s binding medium while employing pattern recognition techniques to process FTIR data-set of complex samples. In this regard, based on the historical documents and previous researches, firstly 56 standard samples were prepared to represent strata of Persian wall paintings in the Safavid period in addition to real historic samples from the case study; Sukias mansion. Then, each sample was analysed by the means of FTIR and chemometrics. Finally, SIMCA was applied to the whole region of studied IR spectra which predicted egg yolk as the binding medium of Sukias mansion samples.     

Static and dynamic stability modeling of a capacitive FGM micro-beam in presence of temperature changes

Stability of a functionally graded (FG) micro-beam, based on modified couple stress theory (MCST), subjected to nonlinear electrostatic pressure and thermal changes regarding convection and radiation, is the main purpose of this paper. It is assumed that the functionally graded beam, made of metal and ceramic, follows the volume fraction definition and law of mixtures, and its properties change as an exponential function through its thickness. By changing the ceramic constituent percent of the bottom surface, five different types of the micro-beams are investigated. The static pull-in voltages in presence of temperature changes are obtained by using step-by-step linearization method (SSLM) and, by adapting Runge–Kutta approach, the dynamic pull-in voltages are obtained numerically. Though the temperature distribution through the thickness of FG micro-beam (due to its too small measurement) is considered uniform, owing to the different thermal expansions of layers, temperature changes cause deflection in the micro-beam, and consequently affect pull-in values. Hence the profound effects of different material constituent over the pull-in voltages are illustrated and it is graphically displayed that how in some cases neglecting components of the couple stress leads to inaccurate results.     

Global Convergence Property of Scaled Two-Step BFGS Method

This paper is aimed to extend the scheme of self scaling, appropriate for the quasi-Newton methods, to the two-step quasi-Newton methods. The scaling scheme has been performed during the main approach of updating the current Hessian approximation and prior to the computation of the next quasi-Newton direction whenever necessary. Global convergence property of the new method is explored on uniformly convex functions with the standard Wolfe line search. Preliminary numerical testing has been performed showing that this technique improves the performance of the two-step method substantially.     

EEGBRP: an energy-efficient grid-based routing protocol for underwater wireless sensor networks

Wireless Networks - Underwater sensor networks (UWSN) include a set of sensor nodes equipped with limited batteries. These batteries are not rechargeable and replacement of them is difficult and...