Improved electrochemical performance of nitrocarburised stainless steel by hydrogenated amorphous carbon thin films for bone tissue engineering

In this study, hydrogenated amorphous carbon thin films, structurally similar to diamond‐like carbon (DLC), were deposited on the surface of untreated and plasma nitrocarburised (Nitrocarburizing‐treated) stainless steel medical implants using a plasma‐enhanced chemical vapour deposition method. The deposited DLC thin films on the nitrocarburising‐treated implants (CN+DLC) exhibited an appropriate adhesion to the substrates. The results clearly indicated that the applied DLC thin films showed excellent pitting and corrosion resistance with no considerable damage on the surface in comparison with the other samples. The CN+DLC thin films could be considered as an efficient approach for improving the biocompatibility and chemical inertness of metallic implants.Inspec keywords: tissue engineering, bone, biomedical materials, electrochemistry, amorphous state, carbon, hydrogen, thin films, plasma CVD, adhesion, corrosion resistance, surface hardeningOther keywords: electrochemical performance, plasma nitrocarburised stainless steel medical implants, hydrogenated amorphous carbon thin films, bone tissue engineering, plasma‐enhanced chemical vapour deposition method, adhesion, corrosion resistance, biocompatibility, chemical inertness, metallic implants, C:H     

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.     

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.     

A fast algorithm for feature extraction of hyperspectral images using the first order statistics

Multimedia Tools and Applications - A new supervised feature extraction method appropriate for small sample size situations is proposed in this work. The proposed method is based on the first-order...     

Experimental Investigation on the Removal of p-Toluic Acid from Aqueous Solution using Functionalized Polymeric Sorbent

Polystyrene-divinylbenzene copolymer (PsDVB) was covalently functionalized with monoethanolamine (MEA), diethanolamine (DEA), and triethanolamine (TEA) by a simple method. The functionalized sorbents were characterized in terms of functionality and morphology, and used for the removal of p-toluic acid (p-TA) from aqueous solution. It was found that DEA-PsDVB has higher adsorption capacity than MEA- and TEA-PsDVB due to more accessible nitro and hydroxyl groups on its surface. Further investigation on the adsorptive properties of DEA-PsDVB indicated that the maximum uptake of p-TA occurred at the optimum pH of 5.3. The kinetics data was successfully represented by the pseudo-first-order model, and the behavior of the adsorption isotherms followed the Freundlich model well. Thermodynamic studies showed that the adsorption of p-TA onto DEA-PsDVB was an endothermic and spontaneous process along with the positive change in entropy. The regeneration of DEA-PsDVB was performed with 0.1 M NaOH solution, and results showed that 99% of the initial capacity was conserved after eight successive adsorption/regeneration cycles.     

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.     

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...     

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.