Ishfaq Ahmad

A new strategy for the synthesis of polyaniline nanostructures using m-CPBA as an oxidant

In this work, we have reported a unique strategy of using Meta-Chloroperoxybenzoic acid (m-CPBA) as an oxidant for Polyaniline (PANI) nanofibers preparation. A templateless rapid mixing method was employed for chemical polymerization of aniline at room temperature under rapid mixing and without use of additional oxidant. PANI nanofibers were fully characterized and their electrochemical properties have been studied in detail. Further, effects of toluene solution on PANI electrochemical properties were also investigated in detail. The results showed that PANI nanofibers, in the presence of toluene as solvent, had relatively high specific capacitance and high columbic efficiency compared to their bulk solution, which was due to the immobile of toluene solution and the fast diffusion of the H+ ions through the polymer material during the redox process.

On the Performance of Phase-I Bivariate Dispersion Charts to Non-Normality

A phase-I study is generally used when population parameters are unknown. The performance of any phase-II chart depends on the preciseness of the control limits obtained from the phase-I analysis. The performance of phase-I bivariate dispersion charts has mainly been investigated for bivariate normal distribution. However, this assumption is seldom fulfilled in reality. The current work develops and studies the performance of phase-I |S| and |G| charts for monitoring the process dispersion of bivariate non-normal distributions. The necessary control charting constants are determined for the bivariate non-normal distributions at nominal false alarm probability (FAP0). The performance of these charts is evaluated and compared in a situation when samples are generated by bivariate logistic, bivariate Laplace, bivariate exponential, or bivariate t5 distribution. The analysis shows that the proper consideration to underlying bivariate distribution in the construction of phase-I bivariate dispersion charts is very important to give a real picture of in or out of control process status. Copyright

Who will cite you back? Reciprocal link prediction in citation networks

Purpose Link prediction in social networks refers toward inferring the new interactions among the users in near future. Citation networks are constructed based on citing each other papers. Reciprocal link prediction in citations networks refers toward inferring about getting a citation from an author, whose work is already cited by you. The paper aims to discuss these issues. Design/methodology/approach In this paper, the authors study the extent to which the information of a two-way citation relationship (called reciprocal) is predictable. The authors propose seven different features based on papers, their authors and citations of each paper to predict reciprocal links. Findings Extensive experiments are performed on CiteSeer data set by using three classification algorithms (decision trees, Naive Bayes, and support vector machines) to analyze the impact of individual, category wise and combination of features. The results reveal that it is likely to precisely predict 96 percent of reciprocal links. The study delivers convincing evidence of presence of the underlying equilibrium amongst reciprocal links. Research limitations/implications It is not a generic method for link prediction which can work for different networks with relevant features and parameters. Practical implications This paper predicts the reciprocal links to show who is citing your work to collaborate with them in future. Social implications The proposed method will be helpful in finding collaborators and developing academic links. Originality/value The proposed method uses reciprocal link prediction for bibliographic networks in a novel way.

The length-biased weighted exponentiated inverted Weibull distribution

Abstract Length-Biased distributions are a special case of the more general form known as weighted distributions. We can exploit the conceptuality of Length-Biased distribution in the development of appropriate models for lifetime data. Its method is adjusting the original probability density function from real data and the expectation of those data. This modification can lead to correct conclusions of the models. Therefore, we introduced the Length-Biased version of the weighted Exponentiated inverted Weibull distribution in this paper. Various properties and the expressions for moments, coefficient of skewness, coefficient of kurtosis, moment generating function, hazard rate function, etc. are derived. The maximum likelihood estimates of the parameters of the proposed distribution are determined. The study results suggest that this distribution is an efficacious model in life time data analysis and other related fields.