PVC membrane potentiometric sensor based on 5-pyridino-2,8-dithia[9](2,9)-1,10-phenanthroline-phane for selective determination of neodymium(III)

Spectrofluorometric studies on the binding properties of 5-pyridino-2,8-dithia[9](2,9)-1,10-phenanthrolinephane (L) toward La3+, Sm3+, Gd3+, Yb3+, and Nd3+ in methanol solution revealed the occurrence of both 1:1 and 2:1 (ligand/metal) complexation with a stability order of Nd3+ > Yb3+ > Gd3+ > Sm3+ > La3+. Consequently, L was used as a suitable neutral ionophore for the preparation of a novel polymeric membrane-selective electrode for Nd3+ ion. The electrode exhibited a Nernstian response over a wide concentration range (1.0 x 10(-6)-1.0 x 10(-2) M) with a low limit of detection of 7.9 x 10(-7) M. The electrode possesses a fast response time of <5 s and can be used for at least 9 weeks without observing any considerable deviation. The proposed electrode revealed a very good selectivity for Nd3+ over a wide variety of alkali, alkaline earth, transition, and heavy metal ions, including members of the lanthanide family other than Nd3+. The potentiometric response of the electrode is independent of the pH of test solution in the pH range 4.0-6.5. The proposed electrode was successfully applied to the recovery of Nd3+ ion from tap water samples and, also, as an indicator electrode, in potentiometric titration of neodymium(III) ions.     

A computer-aided diagnosis system for malignant melanomas

The aim of this study is to provide an efficient way to segment the malignant melanoma images. This method first eliminates extra hair and scales using edge detection; afterward, it deduces a color image into an intensity image and approximately segments the image by intensity thresholding. Some morphological operations are used to focus on an image area where a melanoma boundary potentially exists and then used to localize the boundary in that area. The distributions of texture and a new feature known as AIBQ features in the next step provide a good discrimination of skin lesions to feature extraction. Finally, we rely on quantitative image analysis to measure a series of candidate attributes hoped to contain enough information to differentiate malignant from benign melanomas. The selected features are applied to a support vector machine to classify the melanomas as malignant or benign. By our approach, we obtained 95 % correct classification of malignant or benign melanoma on real melanoma images.     

Synthesis of Polycaprolactone Nanoparticles through Flow-Focusing Microfluidic-Assisted Nanoprecipitation

Polycaprolactone nanoparticles (PCL NPs) were produced by a liquid nonsolvent nanoprecipitation process in a flow-focusing microfluidic device and optimized in terms of particle size, polydispersity index (PDI), and zeta potential ζ. The effects of flow rate ratio (FRR), total flow rate (TFR), the organic solvents tetrahydrofuran and dimethylformamide (DMF), the surfactants polyvinyl alcohol and Tween 80, and polymer molecular weight on the size, PDI, and ζ of PCL NPs were investigated. A stability study was performed to compare the effect of the surfactants on the characteristics of PCL NPs over 7 d. The smallest particles are produced at the highest FRR, TFR, and polymer molecular weight and lowest polymer concentration in DMF. The presence of both surfactants results in smaller NPs.     

Semantic image classification by genetic algorithm using optimised fuzzy system based on Zernike moments

Image classification is a challenging problem of computer vision. This study reports a fuzzy system to semantic image classification. As it is a complex task, various information of digital image, including three color space components and two Zernike moments with different order, are gathered and utilized as an input of fuzzy inference system to materialize a robust rotation/lighting condition and size invariant image classifier. For better performance, all the membership functions are optimized by genetic algorithm after empirical design stage. 90.62 and 96.25 % classification rates for RGB and HSI color spaces confirm the reliability of optimized system in different image conditions given in this contribution.     

Low temperature synthesis of nanocrystalline magnesium aluminate with high surface area by surfactant assisted precipitation method: Effect of preparation conditions

A surfactant assisted co-precipitation method was employed for the low temperature synthesis of magnesium aluminate spinel with nanocrystalline size and high specific surface area. Pluronic P123 triblock copolymer and ammonia solution were used as surfactant and precipitation agent, respectively. The prepared samples were characterized by thermal gravimetric and differential thermal gravimetric analyses (TG/DTG), X-ray diffraction (XRD), N₂ adsorption (BET) and transmission electron microscopy (TEM) techniques. The effects of several process parameters such as refluxing temperature, refluxing time, pH, P123 to metals mole ratio (P123/metals) and calcination temperature on the structural properties of the samples were investigated. The obtained results showed that, among the process parameters pH and refluxing temperature have a significant effect on the structural properties of samples. The results revealed that increase in pH from 9.5 to 11 and refluxing temperature from 40 °C to 80 °C increased the specific surface area of prepared samples in the range of 157–188 m² g^?1 and 162–184 m² g^?1, respectively. The XRD analysis showed the single-phase MgAl₂O₄ was formed at 700 °C.     

Color image segmentation using neuro-fuzzy system in a novel optimized color space

Segmentation is one of the most important pre-processing steps toward pattern recognition and image understanding. It is often used to partition an image into separate regions, which ideally correspond to different real-world objects. In this paper, novel color image segmentation is proposed and implemented using fuzzy inference system in optimized color space. This system, which is designed by neuro-adaptive learning technique, applies a sample image as an input and can reveal the likelihood of being a special color for each pixel through the image. The intensity of each pixel shows this likelihood in the gray-level output image. After choosing threshold value, a binary image is obtained, which can be applied as a mask to segment desired color in input image. Besides using fuzzy systems, optimizing color space for segmentation is another feature of proposed method. This optimizing is implemented by genetic algorithms and influence on system accuracy. Two applications of developed method are discussed, and still it could be applicable in wide range of color image segmentation or object detection purposes.     

Pricing the financial Heston–Hull–White model with arbitrary correlation factors via an adaptive FDM

This paper is concerned with the pricing procedure of one of the most challenging models known as the Heston–Hull–White partial differential equation (PDE) in option pricing, at which the model is a time-dependent 3D linear PDE including three mixed derivative terms. The model comes from the fact that the price, the volatility and the interest rate are assumed to be stochastic processes. To contribute and avoid huge discretized systems, an adaptive distribution of the nodes (viz, nonuniform nodes) is taken into account with emphasis on the hot area of the solution curve. New adaptive finite difference (FD) formulas of higher orders are constructed on these meshes. Then, a set of semi-discretized equations is attained which is tackled by a time-stepping method. Several financial tests are discussed in detail to reveal the superiority of the proposed approach.     

Incremental collusive fraud detection in large-scale online auction networks

The Journal of Supercomputing - An online auction network (OAN) is a community of users who buy or sell items through an auction site. Along with the growing popularity of auction sites, concerns...     

Fuzzy modeling and simulation for lead removal using micellar-enhanced ultrafiltration (MEUF)

In the present paper, a three factor, three-level response surface design based on Box-Behnken design (BBD) was developed for maximizing lead removal from aqueous solution using micellar-enhanced ultrafiltration (MEUF). Due to extremely complexity and nonlinearity of membrane separation processes, fuzzy logic (FL) models have been driven to simulate MEUF process under a wide range of initial and hydrodynamic conditions. Instead of using mathematical model, fuzzy logic approach provides a simpler and easier approach to describe the relationships between the processing variables and the metal rejection and permeation flux. Statistical values, which quantify the degree of agreement between experimental observations and numerically calculated values, were found greater than 91% for all cases. The results show that predicted values obtained from the fuzzy model were in very good agreement with the reported experimental data.