An evolutionary lion optimization algorithm‐based image compression technique for biomedical applications

Recently, medical image compression becomes essential to effectively handle large amounts of medical data for storage and communication purposes. Vector quantization (VQ) is a popular image compression technique, and the commonly used VQ model is Linde–Buzo–Gray (LBG) that constructs a local optimal codebook to compress images. The codebook construction was considered as an optimization problem, and a bioinspired algorithm was employed to solve it. This article proposed a VQ codebook construction approach called the L2‐LBG method utilizing the Lion optimization algorithm (LOA) and Lempel Ziv Markov chain Algorithm (LZMA). Once LOA constructed the codebook, LZMA was applied to compress the index table and further increase the compression performance of the LOA. A set of experimentation has been carried out using the benchmark medical images, and a comparative analysis was conducted with Cuckoo Search‐based LBG (CS‐LBG), Firefly‐based LBG (FF‐LBG) and JPEG2000. The compression efficiency of the presented model was validated in terms of compression ratio (CR), compression factor (CF), bit rate, and peak signal to noise ratio (PSNR). The proposed L2‐LBG method obtained a higher CR of 0.3425375 and PSNR value of 52.62459 compared to CS‐LBG, FA‐LBG, and JPEG2000 methods. The experimental values revealed that the L2‐LBG process yielded effective compression performance with a better‐quality reconstructed image.     

Code generation for a family of executable modelling notations

We are investigating semantically configurable model-driven engineering (MDE). The goal of this work is a modelling environment that supports flexible, configurable modelling notations, in which specifiers can configure the semantics of notations to suit their needs and yet still have access to the types of analysis tools and code generators normally associated with MDE. In this paper, we describe semantically configurable code generation for a family of behavioural modelling notations. The family includes variants of statecharts, process algebras, Petri Nets, and SDL88. The semantics of this family is defined using template semantics, which is a parameterized structured operational semantics in which parameters represent semantic variation points. A specific notation is derived by instantiating the family’s template semantics with parameter values that specify semantic choices. We have developed a code-generator generator (CGG) that creates a suitable Java code generator for a subset of derivable modelling notations. Our prototype CGG supports 26 semantics parameters, 89 parameter values, and 7 composition operators. As a result, we are able to produce code generators for a sizable family of modelling notations, though at present the performance of our generated code is about an order of magnitude slower than that produced by commercial-grade generators.     

Study of morphology and gas separation properties of polysulfone/titanium dioxide mixed matrix membranes

Polysulfone (PSf)‐based mixed matrix membranes (MMMs) with the incorporation of titanium dioxide (TiO₂) nanoparticles were prepared. Distribution and agglomeration of TiO₂ in polymer matrix and also surface of membranes were observed by scanning electron microscopy, transmission electron microscopy, and energy dispersive X‐ray. Variation in surface roughness of MMMs with different TiO₂ loadings was analyzed by atomic force microscopy. Physical properties of membranes before and after cross‐linking were identified through thermal gravimetric analysis. At low TiO₂ loadings (≤3 wt%), both CO₂ and CH₄ permeabilities decreased and consequently gas selectivity improved and reached to 36.5 at 3 bar pressure. Interestingly, PSf/TiO₂ 3 wt% membrane did not allow to CH₄ molecules to pass through the membrane and this sample just had CO₂ permeability at 1 bar pressure. Gas permeability increased considerably at high filler contents (≥5 wt%) and CO₂ permeance reached to 37.7 GPU for PSf/TiO₂ 7 wt% at 7 bar pressure. It was detected that, critical nanoparticle aggregation has occurred at higher filler loadings (≥5 wt%), which contributed to formation of macrovoids and defects in MMMs. Accordingly, MMMs with higher gas permeance and lower gas selectivity were prepared in higher TiO₂ contents (≥5 wt%). POLYM. ENG. SCI., 55:367–374, 2015. © 2014 Society of Plastics Engineers     

Effect of foaming temperature on the mechanical properties of produced closed-cell A356Aluminum foams with melting method

In this study an attempt was carried out to determine the effect of production temperature on the mechanical properties and energy absorption behavior of closed-cell A356 alloy foams under uniaxial compression test. For this purpose, three different A356 alloy closed-cell foams were synthesized at three different casting temperatures, 650 °C, 675 °C and 700 °C by adding the same amounts of granulated calcium as thickening and TiH₂ as blowing agent. The samples were characterized by SEM to study the pore morphology at different foaming temperatures. Compression tests of the A356 foams were carried out to assess their mechanical properties and energy absorption behavior. The results indicated that increasing the foaming temperature from 650 °C to 675 °C and 700 °C reduces the relative density of closed cell A356 alloys by 18.3% and 38% respectively and consequently affects the compressive strength and energy absorption of cellular structures by changing them from equiaxed polyhedral closed cells to distorted cells. Also at 700 °C foaming temperature, growth of micro-pores and coalescence with other surrounding pores leads to several big voids.     

Adaptive neural network output feedback stabilization of nonlinear non‐minimum phase systems

This paper presents an adaptive output feedback stabilization method based on neural networks (NNs) for nonlinear non‐minimum phase systems. The proposed controller comprises a linear, a neuro‐adaptive, and an adaptive robustifying parts. The NN is designed to approximate the matched uncertainties of the system. The inputs of the NN are the tapped delays of the system input–output signals. In addition, an appropriate reference signal is proposed to compensate the unmatched uncertainties inherent in the internal system dynamics. The adaptation laws for the NN weights and adaptive gains are obtained using Lyapunov's direct method. These adaptation laws employ a linear observer of system dynamics that is realizable. The ultimate boundedness of the error signals are analytically shown using Lyapunov's method. The effectiveness of the proposed scheme is shown by applying to a translation oscillator rotational actuator model. Copyright © 2009 John Wiley & Sons, Ltd.     

Modeling of bentonite/sepiolite plastic concrete compressive strength using artificial neural network and support vector machine

Plastic concrete is an engineering material, which is commonly used for construction of cut-off walls to prevent water seepage under the dam. This paper aims to explore two machine learning algorithms including artificial neural network (ANN) and support vector machine (SVM) to predict the compressive strength of bentonite/sepiolite plastic concretes. For this purpose, two unique sets of 72 data for compressive strength of bentonite and sepiolite plastic concrete samples (totally 144 data) were prepared by conducting an experimental study. The results confirm the ability of ANN and SVM models in prediction processes. Also, Sensitivity analysis of the best obtained model indicated that cement and silty clay have the maximum and minimum influences on the compressive strength, respectively. In addition, investigation of the effect of measurement error of input variables showed that change in the sand content (amount) and curing time will have the maximum and minimum effects on the output mean absolute percent error (MAPE) of model, respectively. Finally, the influence of different variables on the plastic concrete compressive strength values was evaluated by conducting parametric studies.     

Reduction toxicity of Amphotericin B through loading into a novel nanoformulation of anionic linear globular dendrimer for improve treatment of leishmania major

Amphotericin B (A) as an antileishmanial drug has limited clinical application owing to severe side-effects and low-water solubility. This is the first study reported using Anionic Linear Globular Dendrimer (ALGD) as A carrier for the increase of A solubility rate, decrease its toxicity, and improve its therapeutic effects. ALGD was synthesized and A was loaded into nanoparticles for the first time with the drug-loading efficiency of 82%. Drug loading was confirmed using characterization methods. The drug solubility rate was increased by 478-folds. The results of the study showed that the A toxicity was significantly decreased by 95% in vitro and in vivo environments, which was confirmed by pathology findings and enzymatic evaluation. Furthermore, the nanodrug caused that mortality rate was reached to zero. Moreover, the nanodrug was as potent as the free drug and glucantime (GUL) in reducing the parasite burden and parasite number. These findings indicated the potency of ALGD to decrease the drug side-effects, increase the drug solubility rate, and improve the drug efficacy. Moreover, the nanoformulation was a non-toxic and cost-effective formulation. The conformity between in vitro and in vivo results suggested that the A-loaded ALGD could be considered as a promising candidate in reducing the side-effects of A in leishmaniasis treatment.

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Magnetite (Fe3O4) Nanoparticles‐Catalyzed Sonogashira– Hagihara Reactions in Ethylene Glycol under Ligand‐Free Conditions

A novel application of nanoparticles of paramagnetic magnetite (Fe₃O₄) as an efficient catalyst for carbon‐carbon bond formation via the Sonogashira–Hagihara reaction under heterogeneous ligand‐free conditions in ethylene glycol (EG) is described. By using this catalyst, arylalkynes are produced from the reaction of aryl iodides and activated heteroaryl bromides with alkynes. The results are reproducible using the catalyst, which was prepared from different sources. The catalyst is easily separated by an external magnetic field from the reaction mixture. The separated catalyst can be recycled for several consecutive runs without appreciable loss of its catalytic activity.     

The role of grain orientation in microstructure evolution of pure aluminum processed by equal channel angular pressing

A new approach describing the role of crystallographic orientation in the microstructural refinement of commercially pure aluminum during the successive passes of equal channel angular pressing (ECAP) is introduced. The study is based on analysis of X-ray diffraction texture data that is used to calculate the geometrical position of crystallographic slip planes with respect to the shearing plane of the ECAP die. The angular deviations of {111} slip planes from the macroscopic deformation plane for different processing routes were calculated and compared. The microstructure evolution was investigated using electron back-scattered diffraction (EBSD). The grain size and grain boundary character distribution obtained for each processing route are related to the angles between {111} planes and the shearing plane. It was shown that the more effective routes in grain refinement have higher angles between {111} slip planes and the shearing plane.