Electrokinetic properties of polypropylene‐layered silicate nanocomposite fibers

Nanocomposite fibers based on polypropylene (PP) polymer were prepared with different content of nanofiller. Filaments were spun from an isotactic iPP homopolymer. Montmorillonite modified by N,N‐dimethyl‐N,N dioctadecylammonium cations was used for preparation of PP nanocomposite fibers. A PP grafted with acrylic acid was added as a coupling agent. Nanocomposite fibers were characterized, i.e., the surface morphology of PP nanocomposite fibers was observed and surface properties were defined by electrokinetic properties determination by zeta potential measurements. For particle distribution observation the plasma etching was involved as a method for sample preparation. The addition of nanoparticles has an impact on ZP value of nanofilled fibers, however, isoelectric point IEP is not significantly influenced by different concentrations of nanofiller. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2009     

Impedance and AC Conductivity of GdCr1−xCoxO3 (x = 0, 0.33, 0.5, 0.67 and 1) Perovskites

Perovskite series GdCr_1?xCo_xO₃ (x = 0, 0.33, 0.5, 0.67 and 1) was obtained using a solution combustion method. The powder XRD was used for identification and structural characterization of the obtained perovskites. All compounds crystallize within the space group Pnma. The morphology of samples was studied using SEM. The impedance and AC conductivity of GdCr_1?xCo_xO₃ were studied using impedance spectroscopy in a frequency range from 10 Hz to 10 MHz and in temperature interval 297–337 K. Changes in electric modulus and DC conductivity, with increasing of the value of x in the structures, were observed. The AC conductivity obeyed the universal power law, σ(ω) = σ(0) + Aωⁿ and revealed semiconductor behavior. The calculated activation energies of existing processes varied with the cobalt content and applied frequency. The impedance spectra showed non‐Debye behavior with a distribution of relaxation times for relaxation and conductive processes. The conduction mechanism for pure orthochromite and orthocobaltite was defined and two types of conduction were observed in the investigated temperature range for the complex perovskites. In order to explain the results, an equivalent circuit with fitted values of circuit components was proposed.     

Preparation and evaluation of functionalized goethite nanorods coated by molecularly imprinted polymer for selective extraction of bisphenol A in aqueous medium

Molecular imprinting method is widely used to recognize various templates. Recognition of bisphenol A (BPA) as a harmful template is important. Herein a novel BPA molecular imprinted polymer nanoparticles (BMIPN) is reported by core-shell surface molecular imprinting technology. In this process, BPA was imprinted by polymerizing methacrylic acid (MAA) as functional monomer, in presence of ethylene glycol dimethacrylate (EGDMA) as a cross-linker and goethite nanorods (GNR) functionalized with fumaric acid. The formation of different materials in different steps was confirmed by Fourier transform infrared spectroscopy (FT-IR), the size and surface morphological of material was observed by field emission scanning electron microscopy (FESEM) and Transmission electron microscopy (TEM). The BMIPN structure was characterized by X-ray diffraction (XRD) and thermal gravimetric analysis (TGA). The important effective factors on adsorption process such as pH, contact time, amount of GNR, initial concentration of BPA, release and reusability and selectivity of BMIPN were studied. The excellent selectivity behavior of BMIPN was evaluated in the presence of phenol (Ph), 4, 4′-biphenol (BPh), hydroquinone (HQ) and 2, 2′-Bis(4-hydroxyphenyl)butane (BPB), as interferences. Based on the isotherm and kinetic studies, the experimental data obey Langmuir isotherm and the pseudo-second-order kinetic models. The results showed that BMIPN had excellent properties for adsorbing BPA in a short time even in the presence of analogous.     

The size and effort estimates in iterative development

The quick delivery of a functionally truncated product is one of the most common results in iterative development, and has become the predominant development approach. One of its drawbacks is the appearance of incomplete artifacts between iterations. Consequently, well-known size-estimation methods can not be used in iterative development. This paper addresses the problem of size estimation in iterative development. We present a novel approach that enables early size estimation using Unified Modeling Language (UML) artifacts. The approach incorporates self-improvement steps that increase the estimation accuracy in subsequent iterations. The demonstration of its applicability and research results are also presented. The results anticipate the possibility of a significant improvement in size and effort estimates by applying the approach presented here.     

Removal of methylene blue dye from aqueous solutions by a new chitosan/zeolite composite from shrimp waste: Kinetic and equilibrium study

The adsorption of methylene blue dye (MBD) from aqueous solutions was investigated using a new composite made up of shrimp waste chitosan and zeolite as adsorbent. Response surface methodology (RSM) was used to optimize the effects of process variables, such as contact time, pH, adsorbent dose and initial MBD concentration on dye removal. The results showed that optimum conditions for removal of MBD were adsorbent dose of 2.5 g/L and pH of 9.0, and initial MBD concentration of 43.75 mg/L and contact time of 138.65 min. The initial concentration of dye had the greatest influence on MBD adsorption among other variables. The experimental data were well fitted by the pseudo-second order kinetic model, while the Freundlich isotherm model indicated a good ability for describing equilibrium data. According to this isotherm model, maximum adsorption capacity of the composite was 24.5 mg/g. Desorption studies showed that the desorption process is favored at low pH under acidic conditions.     

Knowledge discovery with classification rules in a cardiovascular dataset

In this paper we study an evolutionary machine learning approach to data mining and knowledge discovery based on the induction of classification rules. A method for automatic rules induction called AREX using evolutionary induction of decision trees and automatic programming is introduced. The proposed algorithm is applied to a cardiovascular dataset consisting of different groups of attributes which should possibly reveal the presence of some specific cardiovascular problems in young patients. A case study is presented that shows the use of AREX for the classification of patients and for discovering possible new medical knowledge from the dataset. The defined knowledge discovery loop comprises a medical expert's assessment of induced rules to drive the evolution of rule sets towards more appropriate solutions. The final result is the discovery of a possible new medical knowledge in the field of pediatric cardiology.     

A time-efficient convolutional neural network model in human activity recognition

Activity recognition represents the task of classifying data derived from different sensor types into one of predefined activity classes. The most popular and beneficial sensors in the area of action recognition are inertial sensors such as accelerometer and gyroscope. Convolutional neural network (CNN) as one of the best deep learning methods has recently attracted much attention to the problem of activity recognition, where 1D kernels capture local dependency over time in a series of observations measured at inertial sensors (3-axis accelerometers and gyroscopes) while in 2D kernels apart from time dependency, dependency between signals from different axes of same sensor and also over different sensors will be considered. Most convolutional neural networks used for recognition task are built using convolution and pooling layers followed by a few number of fully connected layers but large and deep neural networks have high computational costs. In this paper, we propose a new architecture that consists solely of convolutional layers and find that with removing the pooling layers and instead adding strides to convolution layers, the computational time will decrease notably while the model performance will not change or in some cases will even improve. Also both 1D and 2D convolutional neural networks with and without pooling layer will be investigated and their performance will be compared with each other and also with some other hand-crafted feature based methods. The third point that will be discussed in this paper is the impact of applying fast fourier transform (FFT) to inputs before training learning algorithm. It will be shown that this preprocessing will enhance the model performance. Experiments on benchmark datasets demonstrate the high performance of proposed 2D CNN model with no pooling layers.