Research trends in gender differences in higher education and science: a co-word analysis

The aim of this study is to map and analyze the structure and evolution of the scientific literature on gender differences in higher education and science, focusing on factors related to differences between 1991 and 2012. Co-word analysis was applied to identify the main concepts addressed in this research field. Hierarchical cluster analysis was used to cluster the keywords and a strategic diagram was created to analyze trends. The data set comprised a corpus containing 652 articles and reviews published between 1991 and 2012, extracted from the Thomson Reuters Web of Science database. In order to see how the results changed over time, documents were grouped into three different periods: 1991–2001, 2002–2007, and 2008–2012. The results showed that the number of themes has increased significantly over the years and that gender differences in higher education and science have been considered by specific research disciplines, suggesting important research-field-specific variations. Overall, the study helps to identify the major research topics in this domain, as well as highlighting issues to be addressed or strengthened in further work.     

Immobilization of palladium on benzimidazole functionalized mesoporous silica nanoparticles: catalytic efficacy in Suzuki–Miyaura reaction and nitroarenes reduction

Journal of Porous Materials - Benzimidazole functionalized mesoporous silica nanoparticles immobilized Pd(0)/Pd(II) has been proposed as an efficient catalyst for the one-pot preparation of biaryls...     

The Acute Toxicity of SiO2 and Fe3O4 Nano-particles on Daphnia magna

Silicon - SiO2 nano-particles are applied in different industries such as ceramic producing, glass making, cosmetic products, medicines, magnetic mixtures, heat and electric insulators and glazing...     

Pd(II)/Pd(0) Anchored on Magnetic Organic–Inorganic Hybrid Mesoporous Silica Nanoparticles: A Nanocatalyst for Suzuki–Miyaura and Heck–Mizoroki Coupling Reactions

Journal of Inorganic and Organometallic Polymers and Materials - 3-Chloropropyltrimethoxysilane (CPTMS) was grafted on the surface of silica coated Fe3O4 core (Fe3O4@MCM-41) and then condensed with...     

Dy3Fe5O12 and DyFeO3 nanostructures: Green and facial auto-combustion synthesis,characterization and comparative study on electrochemical hydrogen storage

Although the technology of hydrogen energy heightened gradually, the application of binary metal oxides as a host for hydrogen sorption has not been widely established. Here we show, with a facial combustion method, the formation of Dy₃Fe₅O₁₂ and DyFeO₃ nanostructures with maximum average particle sizes ranging from 25 to 30 and 16–18 nm, respectively. The physical properties of the samples were served which further reflect in hydrogen storage properties. The discharge capacities of Dy₃Fe₅O₁₂ and DyFeO₃ nanoparticles were obtained at 2000 and 2100 mA h/g, respectively. The hydrogen storage properties were confirmed in their respective current-voltage cycles, prior to chronopotentiometry.     

Two-parameter TSCSP method for solving complex symmetric system of linear equations

We introduce a two-parameter version of the two-step scale-splitting iteration method, called TTSCSP, for solving a broad class of complex symmetric system of linear equations. We present some conditions for the convergence of the method. An upper bound for the spectral radius of the method is presented and optimal parameters which minimize this bound are given. Inexact version of the TTSCSP iteration method (ITTSCSP) is also presented. Some numerical experiments are reported to verify the effectiveness of the TTSCSP iteration method and the numerical results are compared with those of the TSCSP, the SCSP and the PMHSS iteration methods. Numerical comparison of the ITTSCSP method with the inexact version of TSCSP, SCSP and PMHSS are presented. We also compare the numerical results of the BiCGSTAB method in conjunction with the TTSCSP and the ILU preconditioners.     

Application of membrane separation processes in petrochemical industry: a review

In this paper a general review on different membrane processes and membrane reactors was done. As the main aim of this paper is to review the application of membrane processes in petrochemical industry, processes such as olefin/paraffin separation, light solvent separation, solvent dewaxing, phenol and aromatic recovery, dehydrogenation, oxidative coupling of methane and steam reforming of methane were discussed in detail. Besides, separation using polymer-inorganic nano composite membranes and wastewater treatment using membrane bio-reactors were reviewed.     

Using backward elimination with a new model order reduction algorithm to select best double mixture model for document clustering

Probabilistic latent semantic analysis (PLSA) is a double structure mixture model which has got a wide application in text and web mining. This method is capable of establishing hidden semantic relations among the observed features, using a number of latent variables. In this approach, the selection of the correct number of latent variables is critical. In the most of the previous researches, the number of latent topics was selected based on the number of invoked classes. This paper presents a method, based on backward elimination approach, which is capable of unsupervised order selection in PLSA. This method starts with a model having a number of components more than the needed value, and then prunes the mixtures to reach their optimum size. During the elimination process, proper selection of some latent variables which must be deleted is the most essential problem, and its relation to the final performance of the pruned model is straightforward. To treat this problem, we introduce a new combined pruning method which selects the best options for removal, while keeping a low computational cost, at all. We conducted some experiments on two datasets from Reuters-21578 corpus. The obtained results show that this algorithm leads to an optimized number of latent variables and in turn achieves better clustering performance compared to the conventional model selection methods. It also shows superiority over the case in which a PLSA model with a fixed number of latent variables, equal to the real number of clusters, is exploited.     

Hydrothermal synthesis of surface-modified iron oxide nanoparticles

We synthesized surface-modified iron oxide nanoparticles in aqueous phase by heating an aqueous solution of iron sulfate (FeSO₄) at 473 K with a small amount of either n-decanoic acid (C₉H₁₉COOH) or n-decylamine (C₁₀H₂₁NH₂), which is not miscible with water at room temperature. Transmission electron microscopy showed that the addition of n-decanoic acid or decylamine changed the shape of the obtained nanoparticles. X-ray diffraction spectra revealed that the synthesized nanoparticles were in α-Fe₂O₃ or Fe₃O₄ phase while Fourier transform infrared spectroscopy and thermogravimetry indicated the existence of an organic layer on the surface of the nanoparticles. In the synthetic condition, decreased dielectric constant of water at higher temperature increased the solubility of n-decanoic acid or n-decylamine in water to promote the reaction between the surface of iron oxide nanoparticles and the organic reagents. After the synthesis, the used organic modifiers separated from the aqueous phase at room temperature, which may help the environmentally benign synthesis of surface-modified metal oxide nanoparticles.     

PBSVM: Partitioning and biased support vector machine for vocal fold pathology assessment using labeled and unlabeled data sets

Most of the existing classification methods, used for voice pathology assessment, are built based on labeled pathological and normal voice signals. This paper studies the problem of building a classifier using labeled and unlabeled data. We propose a novel learning technique, called Partitioning and Biased Support Vector Machine Classification (PBSVM), which tries to utilize all the available data in two steps: (1) a new heuristically partition-based algorithm, which extracts high quality pathological and normal samples from an unlabeled set, and (2) a more principle approach based on biased formulation of support vector machine, which is fairly robust to mislabeling and unbalance data problem. Experiments with wavelet-based energy features extracted from sustained vowels show that the new recognition scheme is highly feasible and significantly outperform the baseline classical SVM classifier, especially in the situation where the labeled training data is small.