Oxidative solution polymerization of aniline hydrochloride onto electrospun nanofibers mats of polylactic acid: Preparation method and characterization

In this work, we present the preparation of polylactic acid (PLLA)/polyaniline (PANI) conductive composite nanofibers mats. They are prepared by bulk oxidative solution polymerization of PANI onto electrospun non‐woven fibers mats of PLLA. The PANI ratio in the composite is about 70%w/w. Scanning electron microscopy (SEM) shows that PLLA nanofibers are randomly oriented, beads free with diameters of 186 ± 85 nm, The PLLA/PANI composite nanofibers diameter values are 518 ± 128 nm with a good adherence between PANI and PLLA nanofibers. DSC and XRD measurements reveal an amorphous structure of the electrospun PLLA fibers due to the rapid evaporization of the solvent. FTIR and UV–vis spectra reflect good mutual interactions between PANI and PLLA chains. The DC‐conductivities ( ) far better than other published ones for similar composites prepared by bulk oxidative solution polymerization of PANI onto other electrospun nanofiber mats or with electrospun nanofibers from a solution mixture of PLLA and PANI. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 41618.     

Plasmodium species aware based quantification of malaria parasitemia in light microscopy thin blood smear

Malaria is a serious worldwide disease, caused by a bite of a female Anopheles mosquito. The parasite transferred into complex life round in which it is grown and reproduces into the human body. The detection and recognition of Plasmodium species are possible and efficient through a process called staining (Giemsa). The staining process slightly colorizes the red blood cells (RBCs) but highlights Plasmodium parasites, white blood cells and artifacts. Giemsa stains nuclei, chromatin in blue tone and RBCs in pink color. It has been reported in numerous studies that manual microscopy is not a trustworthy screening technique when performed by nonexperts. Malaria parasites host in RBCs when it enters the bloodstream. This paper presents segmentation of Plasmodium parasite from the thin blood smear points on region growing and dynamic convolution based filtering algorithm. After segmentation, malaria parasite classified into four Plasmodium species: Plasmodium falciparum, Plasmodium ovale, Plasmodium vivax, and Plasmodium malaria. The random forest and K‐nearest neighbor are used for classification base on local binary pattern and hue saturation value features. The sensitivity for malaria parasitemia (MP) is 96.75% on training and testing of the proposed approach while specificity is 94.59%. Beside these, the comparisons of the two features are added to the proposed work for classification having sensitivity is 83.60% while having specificity is 94.90% through random forest classifier based on local binary pattern feature.     

Multi-reservoir Operation Rules: Multi-swarm PSO-based Optimization Approach

Reservoir operation rules are intended to help an operator so that water releases and storage capacities are in the best interests of the system objectives. In multi-reservoir systems, a large number of feasible operation policies may exist. System engineering and optimization techniques can assist in identifying the most desirable of those feasible operation policies. This paper presents and tests a set of operation rules for a multi-reservoir system, employing a multi-swarm version of particle swarm optimization (MSPSO) in connection with the well-known HEC-ResPRM simulation model in a parameterization–simulation–optimization (parameterization SO) approach. To improve the performance of the standard particle swarm optimization algorithm, this paper incorporates a new strategic mechanism called multi-swarm into the algorithm. Parameters of the rule are estimated by employing a parameterization–simulation–optimization approach, in which a full-scale simulation model evaluates the objective function value for each trial set of parameter values proposed with an efficient version of the particle swarm optimization algorithm. The usefulness of the MSPSO in developing reservoir operation policies is examined by using the existing three-reservoir system of Mica, Libby, and Grand Coulee as part of the Columbia River Basin development. Results of the rule-based reservoir operation are compared with those of HEC-ResPRM. It is shown that the real-time operation of the three reservoir system with the proposed approach may significantly outperform the common implicit stochastic optimization approach.     

Colloid Transport through Natural Filter Media

In this project the use of natural media filtration (NMF) was evaluated for its ability to remove colloidal particles from the aqueous phase as a function of ionic strength and filter media type by determining first-order kinetic deposition rate coefficients. Mushroom compost (MC) and leaf compost (LC) were tested as natural filter media and the results were compared to those obtained using sand and granular activated carbon (GAC). The highest deposition rate coefficients were observed for GAC, followed by LC and MC, and the lowest deposition rate coefficients were found for sand. As predicted by the Derjaguin–Landau–Verwey–Overbeek theory, higher deposition rates were obtained at higher solution ionic strengths. Overall the experiments suggest that the NMF process can efficiently remove colloidal particles from surface waters.     

A robust unified approach to analyzing methylation and gene expression data

Microarray technology has made it possible to investigate expression levels, and more recently methylation signatures, of thousands of genes simultaneously, in a biological sample. Since more and more data from different biological systems or technological platforms are being generated at an incredible rate, there is an increasing need to develop statistical methods that are applicable to multiple data types and platforms. Motivated by such a need, a flexible finite mixture model that is applicable to methylation, gene expression, and potentially data from other biological systems, is proposed. Two major thrusts of this approach are to allow for a variable number of components in the mixture to capture non-biological variation and small biases, and to use a robust procedure for parameter estimation and probe classification. The method was applied to the analysis of methylation signatures of three breast cancer cell lines. It was also tested on three sets of expression microarray data to study its power and type I error rates. Comparison with a number of existing methods in the literature yielded very encouraging results; lower type I error rates and comparable/better power were achieved based on the limited study. Furthermore, the method also leads to more biologically interpretable results for the three breast cancer cell lines.     

PiSHi: click the images and I tell if you are a human

This paper introduces pictorial intelligent system for human identification (PiSHi), an image-based captcha which uses three human cognitive abilities to distinguish humans from machines. The first is the human ability to easily recognise the image’s upright orientation. The second is the human brain’s ability in recognising a picture’s content when it is only partially visible. And the third is the human ability in unconscious decision making when encountering pictorial challenges. This work models such complicated human patterns in problem solving for the first time. In order to extract these behavioural patterns and save them in a pattern database, we have implemented our own captcha and performed a series of experiments. PiSHi’s interface presents the user with a set of distorted pictures and asks her to click on the upright orientation of all the pictures in any preferred order. Next, it captures the user’s interaction patterns, compares them with the ones saved in the pattern database, and grants her a corresponding credit. Based on this credit, the user either passes or fails the test, and participates in updating the picture database. Our experiments indicate that human users can solve our proposed captcha effectively—with an accuracy of 99.44 %. Besides, our proposed system is secure against several types of attacks including random guessing and reverse image search engines. The results offer the possibility of utilising the identified human behavioural models in practical captchas.     

Phase Separation and Spatial Morphology in Sodium Silicate Glasses by AFM,Light Scattering and NMR

The morphological and structural properties of sodium silicate (Na₂O–SiO₂) glasses were analyzed using atomic force microscopy (AFM) and light scattering following thermal treatments. AFM observations indicated that the glass surface microstructure evolves during the phase separation mechanisms from continuous interpenetrating phases in the spinodal decomposition process to separated droplets embedded in a continuous matrix for the nucleation/growth one. Raman mapping gave evidence of a phase separation through the nucleation/growth process with formation of silica‐rich clusters characterized by higher polymerization degree as separate droplets. The variations in inhomogeneities versus temperature investigated by Brillouin are exponential for spinodal decomposition and linear in the case of nucleation/growth mechanism. Nuclear magnetic resonance spectroscopy was used to investigate the spatial distribution of the various Q_n species present in thermally treated glasses and allows determining fractal dimension between two and three.     

Accurate analysis of spectral regrowth of nonlinear power amplifier driven by cyclostationary modulated signals

In this paper we present an exact analytical expression to calculate the spectral regrowth at the output of a nonlinear power amplifier (PA) using the higher order cumulants and Poisson summation formula. This PA is driven by the filtered digitally modulated signals. To improve the accuracy of the calculations, the cyclosationarity of the input signal is considered. Moreover, closed-form expressions for the 1-dB compression and saturation points are extracted as a function of the PA model parameters, higher order statistics of the input signal, and the transfer function of the pulse shaping filter. In addition, an analytical expression for the adjacent channel power (ACP) and a closed-form expression of the ACP ratio are derived. This is followed by investigation of the effect of the PA nonlinearity on the performance of receiver. Simulation studies are carried out to verify the accuracy of the derived expressions. Excellent agreement between the analytical and simulation results is achieved.     

An integrated supervised learning solution for monitoring process mean vector

When an out-of-control condition is detected by a control chart, a search begins to identify and eliminate the source(s) of the signal. Identification of the time when a process first changed is an important step in root cause analysis which helps a process engineer to eliminate the source(s) of assignable cause effectively. The time when a change takes place in the process is referred to as the change point. In multivariate environment, since there is more than one variable involved, then root cause analysis is relatively harder compared to the case of univariate because it is not clear exactly which variable has contributed to the out-of-control condition and in what direction its mean has shifted. Hence, a procedure that identifies the change point, performs diagnostic analysis, and specifies the direction of the shift in the mean of the contributing variable(s) all simultaneously could help to conduct root cause analysis effectively. Although different multivariate methods exist in the literature that allow to either estimate change point in the process mean vector or identify the contributing variables leading to the out-of-control condition, but in this research, an integrated supervised learning solution is proposed, which helps to (1) detect of an out-of-control condition, (2) identify the change point leading to shift in the mean vector, (3) specify the variable(s) contributing to the out-of-condition, and (4) identify the direction of the shift in the mean of each contributing variable simultaneously. A real case study is used to evaluate and compare the performance of the proposed integrated approach to existing methods in the literature.     

Natural Vibrations of Open-Variable Thickness Circular Cylindrical Shells in High Temperature Field

The feasibility of using the transfer matrix method (TMM) to analyze open-variable thickness circular cylindrical shells exposed to a high-temperature field is explored theoretically. In the approach to the problem, the thermal degradation (TG) of thermoelastic characteristics of the material is considered. Natural frequencies and mode shapes for the cylindrical shells are investigated in detail by combining the vibration theory with the TMM. The governing equations of vibration for this system are expressed by the matrix differential equations, and the coefficient matrices are derived. After the relationship between the transfer matrix and the coefficient matrix is established, the fourth-order Runge-Kutta method is used numerically to solve the matrix equation. Once the transfer matrix of single component has been obtained, the product of each component matrix can compose the matrix of the entire structure. The frequency equations and mode shape are formulated in terms of the elements of the structural matrices. Finite-element numerical simulation has validated the present formulas of natural frequencies. Numerical illustrations, supplying pertinent information on the implications of the TG, are presented for various curvatures, aspect ratios, boundary conditions, and thickness ratios, and the pertinent conclusions are outlined.