New natural 2-acetoxy fatty acids using chemical ionization and electron impact mass spectrometry

The phospholipids of the spongePolymastia gleneni contain saturated long chain (C_22–30)-acetoxy fatty acids. Their structures were assigned based on chromatographic and spectrometric data as well as comparison with a synthetic sample. The use of capillary gas chromatography combined with chemical ionization and electron impact mass spectrometry was instrumental in the eludication of structures, since only a very small amount of crude lipids was available. Part 10 of “Phospholipids in Marine Organisms.” For Part 9 in this series, see reference 12.     

A light refractory concrete for thermal insulation

Compositions have been developed for nonshrinking vibrocast thermally insulating light refractory concretes with densities of 1.0, 1.3, 1.5, and 1.8 g/cm³ for temperatures of use up to 1400°C. The structure and properties have been examined for the concrete of density 1.3 g/cm³. __________ Translated from Novye Ogneupory, No. 4, pp. 29–32, April, 2008.     

Adsorption of Cr(III), Ni(II), Zn(II), Co(II) ions onto phenolated wood resin

In this study, phenolated wood resin was used an adsorbent for the removal of Cr(III), Ni(II), Zn(II), Co(II) ions by adsorption from aqueous solution. The adsorption of metal ions from solution was carried at different contact times, concentrations and pHs at room temperature (25°C). For individual metal ion, the amount of metal ions adsorbed per unit weight of phenolated wood resin at equilibrium time increased with increasing concentration and pH. Also, when the amounts of metal ions adsorbed are compared to each other, it was seen that this increase was order of Cr(III) > Ni(II) > Zn(II) > Co(II). This increase was order of Cr(III) > Ni(II) > Co(II) > Zn(II) for commercial phenol–formaldehyde resin. Kinetic studies showed that the adsorption process obeyed the intraparticle diffusion model. It was also determined that adsorption isotherm followed Langmuir and Freundlich models. Adsorption isotherm obtained for commercial phenol–formaldehyde resin was consistent with Freundlich model well. Adsorption capacities from Langmuir isotherm for commercial phenol–formaldehyde resin were higher than those of phenolated wood resin, in the case of individual metal ions. Original adsorption isotherm demonstrated the monolayer coverage of the surface of phenolated wood resin. Adsorption kinetic followed the intraparticle diffusion model. The positive values of ΔG° determined using the equilibrium constants showed that the adsorption was not of spontaneous nature. It was seen that values of distribution coefficient (K_D) decreasing with metal ion concentration in solution at equilibrium (C_e) indicated that the occupation of active surface sites of adsorbent increased with metal ions. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 101: 2838–2846, 2006     

Zinc Diffusion into InP via a Narrow Gap from a Planar Zn<Subscript>3</Subscript>P<Subscript>2</Subscript>-Based Source

An original technology of zinc diffusion into InP via a narrow gap is described, which allows reproducible formation of p–n junctions with preset depth of doping and retained surface morphology of the doped layers. Using the proposed method, desired charge carrier distribution profiles in Zn-doped InP layers were obtained. It has been experimentally confirmed that the method of cross-sectional scanning electron microscopy allows to precision measure of the zinc diffusion depth.     

Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange

Prediction of stock price index movement is regarded as a challenging task of financial time series prediction. An accurate prediction of stock price movement may yield profits for investors. Due to the complexity of stock market data, development of efficient models for predicting is very difficult. This study attempted to develop two efficient models and compared their performances in predicting the direction of movement in the daily Istanbul Stock Exchange (ISE) National 100 Index. The models are based on two classification techniques, artificial neural networks (ANN) and support vector machines (SVM). Ten technical indicators were selected as inputs of the proposed models. Two comprehensive parameter setting experiments for both models were performed to improve their prediction performances. Experimental results showed that average performance of ANN model (75.74%) was found significantly better than that of SVM model (71.52%).     

Supplier selection with an integrated methodology in unknown environment

This paper proposes an integrated methodology and its solution for supplier selection problem. A two-stage stochastic programming model and fuzzy TOPSIS methods are consolidated in this methodology. After pre-research phase, in the second part of the methodology, fuzzy TOPSIS is used for ranking potential suppliers considering qualitative data under fuzzy environment. In the third part of the methodology a group of ranked potential suppliers are included in a two-stage stochastic programming model for evaluation. In the two-stage stochastic programming model demand is assumed as uncertain and different scenarios are generated for this parameter. With this methodology, supplier evaluation procedure can be done in unknown environment. Our methodology is interested with a problem in multi-product, multi-period and multi-sourcing environment.     

Comparison of different classifier algorithms for diagnosing macular and optic nerve diseases

Abstract: The aim of this research was to compare classifier algorithms including the C4.5 decision tree classifier, the least squares support vector machine (LS-SVM) and the artificial immune recognition system (AIRS) for diagnosing macular and optic nerve diseases from pattern electroretinography signals. The pattern electroretinography signals were obtained by electrophysiological testing devices from 106 subjects who were optic nerve and macular disease subjects. In order to show the test performance of the classifier algorithms, the classification accuracy, receiver operating characteristic curves, sensitivity and specificity values, confusion matrix and 10-fold cross-validation have been used. The classification results obtained are 85.9%, 100% and 81.82% for the C4.5 decision tree classifier, the LS-SVM classifier and the AIRS classifier respectively using 10-fold cross-validation. It is shown that the LS-SVM classifier is a robust and effective classifier system for the determination of macular and optic nerve diseases.