Enhancement in electrical conductivity of liquid crystals by graphene metal oxide composites.

Enhancing the electrical conductivity of liquid crystal (LC) circumvents challenges for application in advanced electronic components. Toward this, using additives made of different nanostructures that could result in functional LCs is suggested. In this paper, various concentrations of graphene (Gr)/metal-oxide (Fe3O4) nanocomposite (GMN) (0.0001-1 w%) were added to E7 nematic LC. We found that the role of anisotropic Gr flakes, their edges as well as surface-decorated-metal-oxide-additives have significant impact on electrical properties of E7. A range of appropriate additives of such a nanocomposite enhances the electrical conductivity of LCs. This effect can be traced through the decrease in the formation of GMN aggregates in the E7 and increase in the electrostatic field at the edges of the Gr sheets. Moreover, the presence of metal-oxide nanoclusters due to the presence of oxygen vacancies and defects facilitates the construction of conductive network for improving the charge transfer pathways and contributes to a stronger interaction of the Gr surface with charged species. These factors can provide Gr layers as dipole moments and lead to signal propagation in the dielectric medium. Our finding conveys a pathway toward significant enhancement of electrical conductivity in the LC family which can be useful for functional applications.

Expert decision support system for aeroacoustic source type identification using clustering.

This paper presents an Expert Decision Support System for the identification of time-invariant, aeroacoustic source types. The system comprises two steps: first, acoustic properties are calculated based on spectral and spatial information. Second, clustering is performed based on these properties. The clustering aims at helping and guiding an expert for quick identification of different source types, providing an understanding of how sources differ. This supports the expert in determining similar or atypical behavior. A variety of features are proposed for capturing the characteristics of the sources. These features represent aeroacoustic properties that can be interpreted by both the machine and by experts. The features are independent of the absolute Mach number, which enables the proposed method to cluster data measured at different flow configurations. The method is evaluated on deconvolved beamforming data from two scaled airframe half-model measurements. For this exemplary data, the proposed support system method results in clusters that mostly correspond to the source types identified by the authors. The clustering also provides the mean feature values and the cluster hierarchy for each cluster, and for each cluster member, a clustering confidence. This additional information makes the results transparent and allows the expert to understand the clustering choices.

Automatic source localization and spectra generation from sparse beamforming maps.

Beamforming is an imaging tool for the investigation of aeroacoustic phenomena and results in high-dimensional data that are broken down to spectra by integrating spatial regions of interest. This paper presents two methods that enable the automated identification of aeroacoustic sources in sparse beamforming maps and the extraction of their corresponding spectra to overcome the manual definition of regions of interest. The methods are evaluated on two scaled airframe half-model wind tunnel measurements and on a generic monopole source. The first relies on the spatial normal distribution of aeroacoustic broadband sources in sparse beamforming maps. The second uses hierarchical clustering methods. Both methods are robust to statistical noise and predict the existence, location, and spatial probability estimation for sources based on which regions of interest are automatically determined.

Central composite design and genetic algorithm applied for the optimization of ultrasonic-assisted removal of malachite green by ZnO Nanorod-loaded activated carbon.

Maximum malachite green (MG) adsorption onto ZnO Nanorod-loaded activated carbon (ZnO-NR-AC) was achieved following the optimization of conditions, while the mass transfer was accelerated by ultrasonic. The central composite design (CCD) and genetic algorithm (GA) were used to estimate the effect of individual variables and their mutual interactions on the MG adsorption as response and to optimize the adsorption process. The ZnO-NR-AC surface morphology and its properties were identified via FESEM, XRD and FTIR. The adsorption equilibrium isotherm and kinetic models investigation revealed the well fit of the experimental data to Langmuir isotherm and pseudo-second-order kinetic model, respectively. It was shown that a small amount of ZnO-NR-AC (with adsorption capacity of 20mgg(-1)) is sufficient for the rapid removal of high amount of MG dye in short time (3.99min).

Highly efficient simultaneous ultrasonic assisted adsorption of brilliant green and eosin B onto ZnS nanoparticles loaded activated carbon: Artificial neural network modeling and central composite design optimization.

In this work, central composite design (CCD) combined with response surface methodology (RSM) and desirability function approach (DFA) gives useful information about operational condition and also to obtain useful information about interaction and main effect of variables concerned to simultaneous ultrasound-assisted removal of brilliant green (BG) and eosin B (EB) by zinc sulfide nanoparticles loaded on activated carbon (ZnS-NPs-AC). Spectra overlap between BG and EB dyes was extensively reduced and/or omitted by derivative spectrophotometric method, while multi-layer artificial neural network (ML-ANN) model learned with Levenberg-Marquardt (LM) algorithm was used for building up a predictive model and prediction of the BG and EB removal. The ANN efficiently was able to forecast the simultaneous BG and EB removal that was confirmed by reasonable numerical value i.e. MSE of 0.0021 and R(2) of 0.9589 and MSE of 0.0022 and R(2) of 0.9455 for testing data set, respectively. The results reveal acceptable agreement among experimental data and ANN predicted results. Langmuir as the best model for fitting experimental data relevant to BG and EB removal indicates high, economic and profitable adsorption capacity (258.7 and 222.2 mg g(-1)) that supports and confirms its applicability for wastewater treatment.

Response surface methodology approach for optimization of adsorption of Janus Green B from aqueous solution onto ZnO/Zn(OH)2-NP-AC: Kinetic and isotherm study.

The Janus Green B (JGB) adsorption onto homemade ZnO/Zn(OH)2 nanoparticles loaded on activated carbon (AC) which characterized by FESEM and XRD analysis has been reported. Combination of response surface methodology (RSM) and central composite design (CCD) has been employed to model and optimize variables using STATISTICA 10.0 software. The influence of parameters over pH (2.0-8.0), adsorbent (0.004-0.012g), sonication time (4-8min) and JGB concentration (3-21mgL(-1)) on JGB removal percentage was investigated and their main and interaction contribution was examined. It was revealed that 21mgL(-1) JGB, 0.012g ZnO/Zn(OH)2-NP-AC at pH 7.0 and 7min sonication time permit to achieve removal percentage more than 99%. Finally, a good agreement between experimental and predicted values after 7min was achieved using pseudo-second-order rate equation. The Langmuir adsorption is appropriate for correlation of equilibrium data. The small amount of adsorbent (0.008-0.015g) is applicable for successful removal of JGB (RE>99%) in short time (7min) with high adsorption capacity (81.3-98.03mgg(-1)).

Analysis of the stent expansion in a stenosed artery using finite element method: application to stent versus stent study.

In this article, finite element method is used to investigate the mechanical behavior of a stent and to determine the biomechanical interaction between the stent and the artery in a stenting procedure. The main objective of this study is to reach to a model close to a real condition of coronary stent placement. Unlike most of the models proposed in the literature, all the steps of the deployment of a stent in the stenotic vessel (i.e. pressure increasing, constant load pressure and pressure decreasing) are simulated in this article to show the behavior of the stent in different stages of implantation. The results indicate that the first step of deployment, that is, pressure increasing, plays a main role in the success of stent implantation. So that, in order to compare the behavior of different types of stents, it is sufficient to compare their behavior at the end of pressure increasing step. In order to show the application of the findings in stent versus stent studies, three commercially available stents (the Palmaz-Schatz, Multi-Link and NIR stents) are modeled and their behavior is compared at the end of pressure increasing step. The effect of stent design on the restenosis rate is investigated. According to the findings, the possibility of restenosis is lower for Multi-Link and NIR stents in comparison with Palmaz-Schatz stent which is in good agreement with clinical results. Therefore, the testing methodology outlined here is proposed as a simple and economical alternative for "stent versus stent" complicated clinical trials.

A specific CBP/p300-dependent gene expression programme drives the metabolic remodelling in late stages of spermatogenesis.

Histone hyperacetylation is thought to drive the replacement of histones by transition proteins that occur in elongating spermatids (ElS) after a general shut down of transcription. The molecular machineries underlying this histone hyperacetylation remain still undefined. Here, we focused our attention on the role of Cbp and p300 in histone hyperacetylation and in the preceding late-gene transcriptional activity in ElS. A strategy was designed to partially deplete Cbp and p300 in ElS. These cells progressed normally through spermiogenesis and showed normal histone hyperacetylation and removal. However, a genome-wide transcriptomic analysis, performed in the round spermatids (RS) and ElS, revealed the existence of a gene regulatory circuit encompassing genes presenting high expression levels in pre-meiotic cells, undergoing a repressed state in spermatocytes and early post-meiotic cells, but becoming reactivated in ElS, just prior to the global shutdown of transcription. Interestingly, this group of genes was over-represented within the genes affected by Cbp/p300 knock down and were all involved in metabolic remodelling. This study revealed the occurrence of a tightly regulated Cbp/p300-dependent gene expression programme that drives a specific metabolic state both in progenitor spermatogenic cells and in late transcriptionally active spermatids and confirmed a special link between Cpb/p300 and cell metabolism programming previously shown in somatic cells.

The effect of iron deficiency anemia on intelligence quotient (IQ) in under 17 years old students.

The aim of this study was to evaluate the effects of iron deficiency on intelligence of 11-17 years students. This study conducted on the 540 students (11-17 years) that educated at guidance and high school of Boroujerd city. Laboratory investigations were included serum iron, TIBC (total iron binding capacity) and ferretin. Riven matrix was used in order to determine intelligence quotient. Data were analyzed using SPSS 13 and chi2 and t-tests. Results showed that 78 (14.4%) students had iron deficiency and 14 (25.9%) had iron deficiency anemia. There was no significant difference between different sexes for iron deficiency distribution (p > 0.05), while iron deficiency anemia was significantly higher in girls as compared with boys (p > 0.05). Mean quotient was 115 +/- 12.1 in iron deficiency students, while it was 113.7 +/- 13.9 in patients without iron deficiency. There was also no significant difference between normal and iron deficient students for intelligence quotient (p > 0.05).