TSTL: the template scripting testing language

International Journal on Software Tools for Technology Transfer - A test harness, in automated test generation, defines the set of valid tests for a system, as well as their correctness properties....     

Application of the dry‐spinning method to produce poly(ε‐caprolactone) fibers containing bovine serum albumin laden gelatin nanoparticles

We designed and manufactured a polymeric system with combined hydrophilic–hydrophobic properties by loading gelatin nanoparticles (GNPs) containing bovine serum albumin (BSA) into poly(ε‐caprolactone) (PCL) fibers. Our ultimate goal was to create a device capable of carrying and releasing protein drugs. Such a system could find several biomedical applications, such as those in controlled release systems, surgical sutures, and bioactive scaffolds for tissue engineering. A two‐step desolvation method was used to produce GNPs, whereas PCL fibers were produced by a dry‐spinning method. The morphological, mechanical, and thermal properties of the produced system were investigated, and the distribution of nanoparticles both inside and on the surface of the fibers was examined. The effect of the particles on the biodegradability of the fibers was also evaluated. In vitro preliminary tests were performed to study the release of BSA from nanoparticle‐laden fibers and to compare this with its release from free nanoparticles. Our results indicate that the distribution of particles inside the fibers was quite homogeneous and only a few of them were present on the surface. The presence of the particles in the fibers did not affect the thermal properties of the PCL polymer matrix, although it created voids that affected the degradation characteristics so the PCL fibers favored faster erosion compared to the plain fibers. Preliminary results indicate that the release from GNP‐laden fibers occurred much more slowly compared to that in the free GNPs. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 44233.     

Preparation of novel 2-(2-oxo-2H-chromen-4-yl)-3- arylthiazolidin-4-one derivatives using an efficient ionic liquid catalyst

An eco-friendly procedure for synthesis of 2-(2-oxo-2H-chromen-4-yl)-3-arylthiazolidin-4-one derivatives by three-component reaction of 2-oxo-2H-chromene-4-carbaldehydes, aromatic amines and thioglycolic acid, with tetramethylbutane-1,4-diammonium acetate as a low-cost ionic liquid catalyst under reflux condition is described. The use of an ionic liquid as a catalyst has the advantages of high yields, short reaction time and environmentally friendly reaction media.     

Reductive removal of hexavalent chromium from aqueous solution using sepiolite-stabilized zero-valent iron nanoparticles: Process optimization and kinetic studies

We studied the optimization of hexavalent chromium (Cr(VI)) removal from aqueous solution using the synthesized zero-valent iron nanoparticles stabilized with sepiolite clay (S-ZVIN), under various parameters such as reaction time (min), initial solution pH and concentration of S-ZVIN (g·L^?1) using response surface methodology (RSM). The kinetic study of Cr(VI) was conducted using three types of the most commonly used kinetic models including pseudo zero-order, pseudo first-order, and pseudo second-order models. The rate of reduction reaction showed the best fit with the pseudo first-order kinetic model. The process optimization results revealed a high agreement between the experimental and the predicted data (R²=0.945, Adj-R²=0.890). The results of statistical analyses showed that reaction time was the most impressive factor influencing the efficiency of removal process. The optimum conditions for maximum response (98.15%) were achieved at the initial pH of 4.7, S-ZVIN concentration of 1.3 g·L^?1 and the reaction time of 75 min.     

Novel chiral poly(amide-imide)/surface modified SiO2 nanocomposites based on N-trimellitylimido-l-methionine: Synthesis and a morphological study

In the present investigation, novel poly(amide-imide) (PAI)/SiO₂ nanocomposites (NCs) containing l-methionine moiety in the main chain were prepared via a simple and fast ultrasonic irradiation process. PAI was synthesized by direct poly condensation reaction of N-trimellitylimido-l-methionine with 4,4′-diamino diphenylether in molten tetra-n-butyl ammonium bromide/triphenyl phosphite as a green condensing agent. Due to the high surface energy and tendency for agglomeration, the surface of SiO₂ NPs was modified with chiral diacid. The obtained NCs were characterized by Fourier transform-infrared (FT-IR) spectroscopy, thermogravimetry analysis, X-ray powder diffraction, field emission-scanning electron microscopy (FE-SEM), and transmission electron microscopy (TEM). The FT-IR spectroscopy indicated that the chiral diacid as the coupling agent was attached on the surface of SiO₂ NPs. FE-SEM, and TEM images showed that SiO₂ NPs were dispersed rather homogeneously in the PAI matrix.     

Channel estimation based on learning automata for OFDM systems

The abundant benefits of Orthogonal Frequency‐Division Multiplexing and its high flexibility have resulted in its widespread applications in many telecommunication standards. One important parameter for improving wireless system's efficiency is the accurate estimation of channel state information. In the literatures, many techniques have been studied in order to estimate the channel state information. Nowadays, the techniques based on intelligent algorithms such as genetic algorithm and particle swarm optimization (PSO) have attracted attention of researchers. With a very low pilot overhead, these techniques are able to estimate the channel frequency response properly only using the received signals. Unfortunately, each of these techniques suffers a common weakness: they have a slow convergence rate. In this paper, a new intelligent and different method has been presented for channel estimation using learning automata, entitled LA estimator, where the learning automata are search agents, and each pair is responsible for searching 1 complex coefficient of the channel frequency response. This method can achieve an accurate channel estimation with a moderate computational complexity in comparison with GA and PSO estimators. Furthermore, with higher convergence rate, our proposed method is capable of providing the same performance as GA and PSO. For a 2‐path fast fading channel, simulation results demonstrate the robustness of our proposed scheme according to the bit error rate and the mean square error.     

Influence of monovalent cations and CuO nanoparticles on X-nanozeolite in uranium anionic species separation from contaminated drinking water

The main purpose of this work was the modification of NaX nanozeolite using copper oxide nanoparticles and various monovalent cations such as K~+, Cs~+, and Ag~+in order to make the negatively charged zeolite surface accessible for anionic forms of uranium which are the most dominant species of uranium in the contaminated radioactive waters at natural p H. Various methods such as the X-ray fluorescence(XRF), X-ray powder diffraction(XRD), field emission scanning electron microscopy(FE-SEM), transmission electron microscopy(TEM), Fourier transform infrared spectroscopy(FT-IR), and atomic absorption spectroscopy(AAS) were used to characterize the final synthesized absorbents. Batch technique was used to study the adsorption behavior of uranium ions from polluted drinking water by Na X nanozeolite and its modified forms. In order to better understand the performance of them, the results were compared with those that were obtained for synthesizing bulk NaX zeolite and Na-form of clinoptilolite natural zeolite. Preliminary results indicated that uranium sorption increased as the loading level of CuO nanoparticles on NaX nanozeolite increased from 2.1 wt% to 11.2 wt%. In addition,from the obtained data, an increase in uranium removal efficiency resulted as charge/ionic radius ratio of exchanged cation decreased. Also, the effect of contact time, solid–liquid ratio, initial concentration and temperature on the adsorption process was studied. It is worth mentioning that, in this study, the sorption of uranium was performed under natural conditions of pH and the presence of competing cations and anions which are available in drinking waters.     

Fabrication of self-ordered nanoporous alumina with 500–750 nm interpore distances using hard anodization in phosphoric/oxalic acid mixtures

A hard anodization (HA) technique is employed using different mixtures of phosphoric/oxalic acid for fast fabrication of alumina nanopore arrays in voltages higher than 200 V. The mixtures enable to avoid the breakdown of porous anodic alumina (PAA) in the high voltages. It is revealed for the first time that continuously tunable pore intervals (D_int) from 500 to 750 nm can be controlled by varying the concentrations of oxalic acid at anodization voltages (U_anod) from 230 to 360 V, far beyond the U_anod in the single electrolyte of phosphoric acid or oxalic acid. The ratios of interpore distance, pore diameter and barrier layer thickness to anodization voltage are in the range of conventional HA process for each acid mixture. In this approach, the PAA film growth rate is 26 µm/h, being 7 times larger than that in typical mild anodization.     

CFD Modeling of Micromixing and Velocity Distribution in a 1.7‐MHz Tubular Sonoreactor

The effect of high‐frequency (1.7 MHz) ultrasound waves on the mixing rate in a new continuous tubular sonoreactor was investigated by CFD modeling. Modeling of piezoelectric transducer (PZT) vibrations was done based on the dynamic mesh model. Results indicate that the acoustic streams were in the direction of wave propagation and their maximum velocity near the PZT surface agreed well with experimental measurements. The micromixing efficiency of the sonoreactor was studied by adopting the Villermaux/Dushman reaction in the modeling. Comparison of the calculated relative segregation index from modeling results with experimental data revealed reasonable accordance.     

Digital communication signals identification using an efficient recognizer

Automatic recognition of the communication signals plays an important role for various applications. Most of the existing techniques require high levels of signal to noise ratio (SNR). In this paper, we propose a high efficient technique for classification of the digital modulations that requires a low level of SNRs. This technique includes two main modules: feature extraction module and the classifier module. In the feature extraction module we use the auto-regressive modeling together other useful features. These features are a combination set of the entropy and energy of the signal, variance of the coefficients wavelet packet transform, fourth order of moment and zero-crossing rate. In the classifier module we have used the two structures of the neural networks: multi-layer perceptron (MLP) neural network and radial basis neural networks. Simulation results show the proposed technique has very high recognition accuracy for identification of the considered digital modulations even at very low SNRs.