Nanoraspberry-like palladium/spongy nickel oxide for electrooxidation of five light fuels

The spongy nickel oxide (SNO) was synthesized the solution combustion method. The SNO was selected as a promoter to boost the catalytic activity of nanoraspberry-like palladium (NRPd) toward electrooxidation of five light fuels (LFs): methanol, ethanol, formaldehyde, formic acid, and ethylene glycol. The X-ray powder diffraction, Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscopy, and field emission scanning electron microscope techniques were used for the materials characterization. In comparison with nonpromoted Pd, the NRPd-SNO electrocatalyst shown an excellent efficiency in parameters like the electrochemical active surface area and anti-CO poisoning behavior. The turnover data and the parameters, including reaction order, activation energy, and the coefficients of electron transfer and diffusion, were evaluated for the each process of LFs electrooxidation. The outcome for NRPd-SNO activity toward LFs electrooxidation was compared to some reported electrodes. The SNO increases the removal of intermediates created in the oxidation of LFs that can poison the surface of palladium catalyst. This is due to the presence of the lattice oxygens in SNO structure and Ni switching between its high and low valances. The compatibility of the adsorption process of LFs on the surface of the NRPd-SNO catalyst with different isotherms was determined by studying the Tafel polarization and calculating the surface coverage.     

Compatibilizing Effects on the Phase Morphology and Thermal Properties of Polymer Blends Based on PTT and m-LLDPE

Summary Morphology and thermal characteristics of poly (trimethylene terephthalate) (PTT)/ metallocene linear low-density polyethylene (m-LLDPE) blends with different amounts of a terpolymer based on glycidyl methacrylate (GMA), employed as a possible compatibilizer, were systematically examined. DMTA results show two distinct peaks for all neat blends associated with each phase. Melt crystallization degree of both phases in all the blends was observed to be reduced compared to pure components, except for that of PTT-rich phase in those blends with PTT as the matrix which slightly increases. Addition of the terpolymer to the system is found to effectively reduce the droplet size of the dispersed phase and increase phase adhesion. Also it increases the crystallinity of PTT in the system with PTT as the matrix. The influence of the compatibilizer is ascribed to the chemical interaction of PTT functional end groups with GMA functionalities. The efficiency of the terpolymer as a compatibilizer beyond 5 wt% content decreases which can be ascribed to the interface saturation and formation of micelles in the bulk phases.     

Counting eosinophils in bronchoalveolar lavage fluid images with fuzzy methodology

This paper proposes a new fuzzy approach to count eosinophils, as a measure of inflammation, in bronchoalveolar lavage fluid images, provided by digital camera through microscope. We use fuzzy cluster analysis and fuzzy classification algorithm to determine the number of objects in an image. For this purpose, a fuzzy image processing procedure consisting of five main stages is presented. The first stage is pre-highlighting the objects in the images by using an image pre-processing method for enhancement, which is sharpening the image with the Laplaian high pass filter in order to have acceptable contrast in the image. The second stage is segmentation by clustering with fuzzy c-mean algorithm for portioning. In this stage the clustered data are the rough symbols of objects in the image containing noise. In the third step, first, a Gaussian low pass filter is used for noise reduction. Then, a contrast adoption in the image is done by modifying the membership functions in the image [H.R. Tizhoosh, G. Krell, B. Michaelis, Knowledge-based enhancement of megavoltage images in radiation therapy using a hybrid neuro-fuzzy system, Image and Vision Computing 19(July) (2000) 217–233]. Object recognition, the fourth stage, will be done by using fuzzy labeling for the objects in the image, using a fuzzy classification method. The number of labeled images shows the number of eosinophils in an image which is an index for diagnosing inflammation. The last stage is tuning parameters and verification of the system performance by using a feed forward Neural Network.     

A hybrid modeling approach for forecasting the volatility of S&P 500 index return

Forecasting volatility is an essential step in many financial decision makings. GARCH family of models has been extensively used in finance and economics, particularly for estimating volatility. The motivation of this study is to enhance the ability of GARCH models in forecasting the return volatility. We propose two hybrid models based on EGARCH and Artificial Neural Networks to forecast the volatility of S&P 500 index. The estimates of volatility obtained by an EGARCH model are fed forward to a Neural Network. The input to the first hybrid model is complemented by historical values of other explanatory variables. The second hybrid model takes as inputs both series of the simulated data and explanatory variables. The forecasts obtained by each of those hybrid models have been compared with those of EGARCH model in terms of closeness to the realized volatility. The computational results demonstrate that the second hybrid model provides better volatility forecasts.     

A pseudo‐differential current‐reuse structure for opamp‐sharing pipelined analog‐to‐digital converters

In this paper, a power efficient pseudo‐differential (PD) current‐reuse structure is presented to alleviate the memory effects of opamp‐sharing in pipelined analog‐to‐digital converters. To implement the PD current‐reuse structure, a switched‐capacitor circuit is introduced for multiplying digital‐to‐analog converter, which has a slight modification compared with the conventional switching scheme with no power penalty. In the proposed multiplying digital‐to‐analog converter circuit, the common‐mode offset amplification of the PD structures is eliminated. Moreover, a PD current‐reuse amplifier is developed from the telescopic structure with an inverter‐based gain‐boosting circuit. The effectiveness of the proposed structure is evaluated in comparison with existing current‐reuse techniques. Copyright © 2014 John Wiley & Sons, Ltd.     

Efficient loading and entrapment of tamoxifen in human serum albumin based nanoparticulate delivery system by a modified desolvation technique

Herein, the poorly water-soluble drug, Tamoxifen (Tmx), was loaded in the amphipathic matrix of human serum albumin (HSA) nanoparticles by a modified desolvation method. In order to enhance the drug loading (DL) and drug entrapment efficiency (DEE) (<2% and 10%, respectively), ultrasonication of Tmx-HSA mixture was performed prior to desolvation process. Tmx loading and entrapment efficiency were optimized by employment of the response surface methodology (RSM)-central composite design (CCD) of experiments. Under the optimum conditions of 1.59 mg Tmx/ml concentration, 7.76 pH and 5 h incubation of HSA-Tmx, the DL of 6.7% and DEE of 74% are achievable. Particles with the average size of 195 nm, zeta potential of −21 mV and polydispersity index of 0.09 were produced under these conditions. A more sustained Tmx release behavior was observed from polyethylene glycol (PEG) conjugated nanoparticles in comparison to the non-PEGylated ones. The short-term stability investigation showed no alteration in physicochemical properties of nanoparticles at 4 and 37 °C, but small increase in nanoparticles size was observed after three months of storage at room temperature. This is the first report for efficient production of a Tmx delivery system based on HSA nanoparticles.     

Curcumin and Liver Disease: from Chemistry to Medicine

Curcumin, the natural yellow‐colored active principle, also called turmeric yellow, extracted from the perennial herb Curcuma longa L., has potent biological and pharmacological properties such as antioxidant, anti‐inflammatory, antifungal, antibacterial, anti‐ischemic, antitumor, and anticancer actions. The molecular mechanism of the hepatoprotective action of curcumin is due to its antioxidant properties and inhibitory activity against nuclear factor (NF)‐κB that regulates different proinflammatory and profibrotic cytokines. Overall, scientific reports demonstrate that curcumin has high therapeutic ability for treating hepatic disorders. Here is a systematic discussion of the hepatoprotective activity of curcumin and its possible mechanisms of actions.     

Neuro‐Fuzzy Impact Control (NFIC) for Anti‐Personnel (AP) Mines Detection

An explicit impact control scheme is modified as the main control scheme, while an intelligent control method is designed to deal with uncertainties and varying environment parameters in a mechatronics approach to anti‐personnel (AP) mine detection. The device imitates the manual hand‐prodding technique for mine detection. It inserts a bayonet into the soil and models the dynamics of the manipulator and environment parameters, such as stiffness variation in the soil, to control the impact caused by making contact with a stiff object. An adaptive neuro‐fuzzy plus PID controller is employed to switch from a conventional PID controller to neuro‐fuzzy impact control (NFIC) when an impact is detected. The developed control schemes are validated through experimental work.     

Fuzzy Models for Link Prediction in Social Networks

Predicting missing links and links that may occur in the future in social networks is an attention grabbing topic amid the social network analysts. Owing to the relationship between human‐based system and social sciences in this field, granular computing can help us to model the systems more effectively. The present study aims to propose two new similarity indices, based on granular computing approach and fuzzy logic. It also presents a new hybrid model for creating synergy between various link prediction models. Results show that fuzzy system analysis, in comparison with the crisp approach, can make more effective predictions through better expression of network characteristics. The indices are tested on collaboration networks. It is found that the accuracy of predictions is significantly higher than the crisp approach. It can modify the models for computing the strength of the links and/or predicting the evolutions of the social networks.