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     

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.     

Green solid-state fabrication of new nanocomposites based on La–Fe–O nanostructures for electrochemical hydrogen storage application

Nano-sized La–Fe–O (LFO) structures were fabricated via novel free-solvent and green solid-state route using La (acac)₃. H₂O and Fe (acac)₃ complex precursors. Acetylacetonate (acac) in organometallic complex precursors control nucleation and growth of formed crystals with creation spatial barrier around the cations, and prevent nano-product agglomeration. The mechanism of role of acac has been explained in nanostructure formation. Changing of parameters in synthesis reaction consisting La:Fe molar ratio, calcination time and temperature in turn offer a virtuous control over the nanocomposites size and shape which various compositions of La₂O₃/LaFeO₃, LaFeO₃/La₂O₃ and LaFeO₃/Fe₂O₃ obtained. The as-prepared La–Fe–O nano-products were characterized thorough Scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), Fourier transform infrared (FT-IR), UV–Vis, BET and energy dispersive X-ray (EDX) analysis in terms of crystallinity structure, composition, porosity and morphology. Different formed La–Fe–O nanostructures were evaluated for electrochemical hydrogen storage capacity through chronopotentiometry technique in stable current (1 mA). The achieved La–Fe–O nanoparticles could be applied as a favorable candidate active material for electrochemical hydrogen storage. Optical, magnetic and reducible characteristics of La–Fe–O nanostructures have positive effect on electrochemical hydrogen storage capacity. It was found out that the LaFeO₃/Fe₂O₃ nanocomposites have the best electrochemical hydrogen storage performance due to oxidation-reduction process of Fe²⁺/Fe³⁺ components which can help to charge-discharge process of hydrogen to increase the storage capability to 790 mAhg^?1 after 20 cycles. Also, the mixed metal oxides illustrate advanced discharge capacity than other binary oxides.     

Software‐defined networking to improve handover in mobile edge networks

Mobility management and handover for a seamless connection are among all‐time challenges of wireless networks. Software‐defined networking (SDN) has opened new horizons toward research by adding intelligence in edge networks while decoupling the control and data planes. The flexibility and centralized nature of SDN further improve the handover decision algorithms. In this paper, we have improved the network performance with respect to the number of handovers and the handover delay by applying an LTE‐SDN architecture and a novel handover decision algorithm based on predicting the future locations of a moving vehicle. The proposed algorithm decouples the handover procedure into two phases of preparation and execution. In the preparation phase, which occurs in the control plane, the handover decision and resource allocation take place, and in the execution phase, handover gets executed similar to the LTE architecture. The results of our research indicate that our proposed LTE‐SDN performance is improved with respect to the number of handovers, handover delay, and signaling overhead by 24%, 16%, and 20%, respectively. On the other hand, average Reference Signal Received Quality (RSRQ) value is decreased by 4% as a tradeoff for the improvements gained.     

Application of ANFIS-PSO as a novel method to estimate effect of inhibitors on Asphaltene precipitation

Asphaltene precipitation in petroleum industries is known as major problems. To solve problems there are approaches for inhibition of asphaltene precipitation, Asphaltene inhibitors are known effective and economical approach for inhibition and prevention of asphaltene precipitation. In the present study Adaptive neuro-fuzzy inference system (ANFIS) was coupled with Particle swarm optimization (PSO) to create a novel approach to predict effect of inhibitors on asphaltene precipitation as function of crude oil properties and concentration and structure of asphaltene inhibitors.in order to training and testing the algorithm, a total number of 75 experimental data was gathered from the literature. The results of this model showed that average absolute relative deviation (AARD), the coefficient of determination (R²) and root mean square error (RMSE) for the dataset of the algorithm are 2.5058, 0.99342 and 0.64238 respectively. According to the graphical and statistical reports, the proposed ANFIS-PSO has acceptable potential for investigation of effect on asphaltene inhibitors.     

Preparation and characterization of symmetric and asymmetric pure polysulfone membranes for CO2 and CH4 separation

Preparation of pure polysulfone (PSf) membrane for CO₂/CH₄ separation was aimed in this study. Accordingly, the effects of different variables such as: type and concentration of alcohol as external nonsolvent in the coagulation bath, solvent type in the casting solution and also presence of butanol (BuOH) as internal nonsolvent in polymer solution were examined. CO₂ and CH₄ permeabilities of prepared membranes in different coagulation baths follow this order: ethanol‐50% (EtOH‐50%) > isopropyl alcohol‐50% (IPA‐50%) > ethanol‐100% (EtOH‐100%) > IPA‐100%. According to scanning electron microscopy photographs, membrane asymmetry decreased in higher concentration of alcohols and a high symmetric membrane was prepared using IPA‐100% as external nonsolvent. CO₂/CH₄ selectivity improved in the following order: IPA‐100% > EtOH‐100% > IPA‐50% > EtOH‐50%. Then, a high CO₂/CH₄ selectivity (36.40) was obtained employing pure IPA in coagulation bath. When a mixture of NMP/THF was used instead of NMP as solvent, CO₂/CH₄ selectivity increased from 7.10 to 18.50. Thickness of membranes decreased from 124.70 to 72.11 μm by addition of BuOH concentration from 0 to 10 wt% as internal nonsolvent. Consequently, an enhancement in gas permeability was observed in higher BuOH concentrations. POLYM. ENG. SCI., 54:1686–1694, 2014. © 2013 Society of Plastics Engineers