Direct pyrolysis mass spectrometry analysis of fresh and aged $\hbox{PF}_{6}^{-}$‐doped polythiophenes

In this study, pyrolysis mass spectrometry has been applied for the investigation of thermal characteristics of ‐doped polythiophene (PTh) films. Thermal degradation of PTh showed behaviour identical with ‐doped polythiophene (BF₄‐PTh), indicating that the effects of the dopants in question on the thermal characteristics of the PTh are not significant. Besides the oligomer peaks (up to hexamer), peaks indicating decomposition of the thiophene ring have also been detected as in the case of BF₄‐PTh. However, the data also indicated that the oxidation of the dopant was much more effective, yielding mainly POF₃, H₃PO₄ compounds even for the freshly prepared samples. Upon ageing, the yield of these products increased and exposure to air for more than 50 days also caused degradation of the PTh chains. Furthermore, substitution of the thiophene (Th) ring by fluorine has been recorded and was associated with reactions of Th with the reactive oxidation products of the dopant. Analyses of the de‐doped samples showed that although de‐doping was effective to a certain extent, inward diffusion of the counter ion (C₄H₉)₄N⁺ has also occurred. This diffusion was more efficient than that was observed for BF₄‐PTh samples. Copyright © 2004 Society of Chemical Industry     

Essential work of fracture of poly(ϵ‐caprolactone)/boehmite alumina nanocomposites: Effect of surface coating

The essential work of fracture (EWF) approach was adopted to reveal the effect of nanofillers on the toughness of poly (?‐caprolactone) (PCL)/boehmite alumina (BA) nanocomposites. Synthetic BA particles with different surface treatments were dispersed into the PCL matrix by extrusion melt compounding. The morphology of the composites was studied by scanning electron microscopy. Differential scanning calorimetry and wide‐angle X‐ray scattering were used to detect changes in the crystalline structure of PCL. Also, mode I type EWF tests, dynamic mechanical analysis, and quasi‐static tensile tests were applied to study the effect of the BA nanofillers on the mechanical properties of the nanocomposites. BA was homogeneously dispersed and acted as heterogeneous crystallization nucleant and a nonreinforcing filler in PCL. The tensile modulus and yield strength slightly increased and the yield strain decreased with increasing BA content (up to 10 wt %). The effect of the BA surface treatment with octylsilane was negligible by contrast to that with alkylbenzene sulfonic acid (OS2). Like the tensile mechanical data, the essential and nonessential work of fracture parameters did not change significantly either. The improved PCL/BA adhesion in case of OS2 treatment excluded the usual EWF treatise. This was circumvented by energy partitioning between yielding and necking. The yielding‐related EWF decreased, whereas the nonessential EWF increased with BA content and with better interfacial adhesion. This was attributed to the effect of matrix/filler debonding. © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013     

Investigation of polymerization parameters affecting dopamine selectivity of a polymeric membrane

Summary By means of electrochemical oxidative polymerization, poly (1,3-phenylenediamine) films on a gold electrode were prepared at a potential of 0.8 V. The permeation properties of polymeric films at the different thickness were investigated by cyclic and differential pulse voltammetry techniques. Voltammetric studies showed that polymeric film at the 1.2 mC thickness exhibited selective permeation for dopamine while rejecting ascorbic acid. Then, all the polymerization parameters affecting the permselective characteristics were systematically investigated and the optimal values were determined. Moreover, stability of polymeric membrane was examined. The results showed that polymeric membrane, owing to permselective character, could be used as a dopamine selective membrane. Received: 10 December 1999/Revised version: 12 March 2000/Accepted: 27 March 2000     

Anode effect elimination by subsonic and sonic vibrations

The only method so far used industrially to produce primary aluminum is the combination of the Bayer process with the Hall-Héroult process. The production process of aluminum which was patented by Charles Martin Hall and Paul Louis Toussaint Héroult in 1886, has long been important in our daily lives and that importance is likely to increase year by year. In this study, different subsonic and sonic vibrations, which were obtained from a 0.30 kW, 1,400 rpm three-phase motor, also a 0.55 kW, 2,800 rpm three-phase motor and 0.75 kW frequency converter, were applied to a laboratory-type aluminum electrolysis cell and the possibility of eliminating the anode effect was investigated.     

Size and expansion ratio analysis of micro nozzle gas flow

Size and expansion ratio effects on the flowfield are investigated for micro converging-diverging nozzles. Numerical computations are conducted by using two dimensional augmented Burnett equations and Navier-Stokes equations that were derived from the Boltzmann equation. The Maxwell-Smoluchowski slip boundary condition is used for adiabatic walls, and Steger-Warming flux vector splitting scheme is applied to the convective inviscid flux terms. The results from the augmented Burnett equation are compared with Navier-Stokes and Direct Simulation Monte Carlo (DSMC) results. Then, nozzle-size analysis is conducted for between 2 µm and 100 µm throat width. Influence of the Knudsen number is investigated, and temperature and Mach number variations are presented. In addition, the influence of the expansion ratio is studied with three (1.7:1, 3.4:1, and 6.8:1) different configurations. The results are compared with each other and an experimental data in the literature.     

Single image super resolution based on sparse representation using discrete wavelet transform

Multimedia Tools and Applications - Single image super resolution (SR) based on sparse representation is a promising technique where the SR problem is solved by searching for the most robust...     

A Comparison of Various Artificial Intelligence Methods in the Prediction of Bank Failures

The strong relationship between bank failure and economic growth attaches far more importance to the predictability of bank failures. Consequently, numerous statistical prediction models exist in the literature focusing on this particular subject. Besides, artificial intelligence techniques began to attain an increasing level of importance in the literature due to their predictive success. This study distinguishes itself from the similar ones in the sense that it presents a comparison of three different artificial intelligence methods, namely support vector machines (SVMs), radial basis function neural network (RBF-NN) and multilayer perceptrons (MLPs); in addition to subjecting the explanatory variables to principal component analysis (PCA). The extent of this study encompasses 37 privately owned commercial banks (17 failed, 20 non-failed) that were operating in Turkey for the period of 1997–2001. The main conclusions drawn from the study can be summarized as follows: (i) PCA does not appear to be an effective method with respect to the improvement of predictive power; (ii) SVMs and RBF demonstrated similar levels of predictive power; albeit SVMs was found to be the best model in terms of total predictive power; (iii) MLPs method stood out among the SVMs and RBF methods in a negative sense and exhibits the lowest predictive power.     

Palmprint Recognition by Applying Wavelet-Based Kernel PCA

This paper presents a wavelet-based kernel Principal Component Analysis (PCA) method by integrating the Daubechies wavelet representation of palm images and the kernel PCA method for palmprint recognition. Kernel PCA is a technique for nonlinear dimension reduction of data with an underlying nonlinear spatial structure. The intensity values of the palmprint image are first normalized by using mean and standard deviation. The palmprint is then transformed into the wavelet domain to decompose palm images and the lowest resolution subband coeffcients are chosen for palm representation. The kernel PCA method is then applied to extract non-linear features from the subband coeffcients. Finally, similarity measurement is accomplished by using weighted Euclidean linear distance-based nearest neighbor classifier. Experimental results on PolyU Palmprint Databases demonstrate that the proposed approach achieves highly competitive performance with respect to the published palmprint recognition approaches.     

Improved gait recognition by multiple-projections normalization

Recognizing people by gait promises to be useful for identifying individuals from a distance; in this regard, improved techniques are under development. In this paper, an improved method for gait recognition is proposed. Binarized silhouette of a motion object is first represented by four 1-D signals that are the basic image features called the distance vectors. The distance vectors are differences between the bounding box and silhouette, and extracted using four projections to silhouette. Fourier Transform is employed as a preprocessing step to achieve translation invariant for the gait patterns accumulated from silhouette sequences that are extracted from the subjects’ walk in different speed and/or different time. Then, eigenspace transformation is applied to reduce the dimensionality of the input feature space. Support vector machine (SVM)-based pattern classification technique is then performed in the lower-dimensional eigenspace for recognition. The input feature space is alternatively constructed by using two different approaches. The four projections (1-D signals) are independently classified in the first approach. A fusion task is then applied to produce the final decision. In the second approach, the four projections are concatenated to have one vector and then pattern classification with one vector is performed in the lower-dimensional eigenspace for recognition. The experiments are carried out on the most well-known public gait databases: the CMU, the USF, SOTON, and NLPR human gait databases. To effectively understand the performance of the algorithm, the experiments are executed and presented as increasing amounts of the gait cycles of each person available during the training procedure. Finally, the performance of the proposed algorithm is comparatively illustrated to take into consideration the published gait recognition approaches.     

An upgraded artificial bee colony (ABC) algorithm for constrained optimization problems

Artificial bee colony (ABC) algorithm developed by Karaboga is a nature inspired metaheuristic based on honey bee foraging behavior. It was successfully applied to continuous unconstrained optimization problems and later it was extended to constrained design problems as well. This paper introduces an upgraded artificial bee colony (UABC) algorithm for constrained optimization problems. Our UABC algorithm enhances fine-tuning characteristics of the modification rate parameter and employs modified scout bee phase of the ABC algorithm. This upgraded algorithm has been implemented and tested on standard engineering benchmark problems and the performance was compared to the performance of the latest Akay and Karaboga’s ABC algorithm. Our numerical results show that the proposed UABC algorithm produces better or equal best and average solutions in less evaluations in all cases.