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A filament stretching rheometer is used to investigate the extensional flow-induced crystallization of two commercial grade isotactic poly-1-butene samples. The degree of crystallinity of the stretched fibers is quantified using differential scanning calorimetry measurements as a function of extension rate and accumulated Hencky strains. All the measurements are performed using the Janeschitz-Kriegel protocol. The samples are first melted to erase their thermal and mechanical history. They are then quickly quenched to T = 98°C after which the stretch is imposed. The deformed filament is then allowed to crystallize fully at T = 98°C. The extensional rheology of both the samples shows only minimal strain hardening. For the case of the lower molecular weight sample, the percent crystallinity increases from 46% under quiescent conditions to a maximum of 63% at an extension rate of = 0.05 s −1. This corresponds to an increase of nearly 50% above the quiescent case. The high molecular weight sample shows similar trends achieving an increase in crystallinity of 25%. The experiments show an optimal extension rate for which the extensional flow has the maximum impact on the polymer crystallinity. The percent crystallinity of both the samples is observed to increase with increasing strain for a fixed extension rate. Small angle X-ray scattering shows that the observed increase in crystallinity is likely due to the increasing orientation and alignment of the polymer chains in extensional flows which enhances the thread-like precursors responsible for the formation of the crystals in the shish-kebab morphology.

In the present article, the authors have studied the dynamical behavior of delay‐varying computer virus propagation (CVP) model with fractional order derivative, and it is found that the chaotic attractor exists in the considered fractional order system. In order to eliminate the chaotic behavior of fractional order delay‐varying CVP model, feedback controlmethod is used. This article also dealswith the synchronization between controlled and chaotic delay‐varying CVPmodel via active controlmethod. The fractional derivative is described in the Caputo sense. Numerical simulation results are carried out by means of Adams‐Boshforth‐Moultonmethod with the help ofMATLAB, and the results are successfully depicted through graphs .Copyright © 2015 John Wiley & Sons, Ltd.

Many graph clustering algorithms have been proposed in recent past researches, each algorithm having its own advantages and drawbacks. All these algorithms rely on a very different approach so it's really hard to say that which one is the most efficient and optimal if we talk in the sense of performance. It is really hard to decide that which algorithm is beneficial in case of highly complex networks like PPI networks which consist of thousands of nodes. The paper proposes an effective data comparison of RNSC (Restricted Neighbourhood Search Clustering) and MCL (Markov Clustering) algorithms based on Erdos-Renyi and Power-Law Distribution graphs. The basic parameters used for comparison are Edge Density, Run Time, Number of Nodes, Cluster Size and Singleton Cluster. Our approach is an effective one because firstly we have used two types of graph generators, Erdos-Renyi and Scaled-Free for generation of input graphs which are very much closer to the real input graphs and secondly we have generated input graphs having more than 1000 nodes, so in our approach we have used both the algorithms for clustering highly complex input graphs just like PPI networks. For comparison and analysis purpose we have collected data sets and generated some graphs based on these parameters. The proposed approach depicts which algorithm is best to be used for clustering such complex graphs and also some fields for extension if possible in both them. All graphs used in this thesis are unweighted and undirected. General TermsGraph Clustering, Data mining et. al.

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