Microcracking and fracture process in cement mortar and concrete: a comparative study using acoustic emission technique

This article reports the acoustic emission (AE) study of precursory micro-cracking activity and fracture behaviour of quasi-brittle materials such as concrete and cement mortar. In the present study, notched three-point bend specimens (TPB) were tested under crack mouth opening displacement (CMOD) control at a rate of 0.0004 mm/sec and the accompanying AE were recorded using a 8 channel AE monitoring system. The various AE statistical parameters including AE event rate , AE energy release rate , amplitude distribution for computing the AE based b-value, cumulative energy (I E) pound and ring down count (RDC) were used for the analysis. The results show that the micro-cracks initiated and grew at an early stage in mortar in the pre peak regime. While in the case of concrete, the micro-crack growth occurred during the peak load regime. However, both concrete and mortar showed three distinct stages of micro-cracking activity, namely initiation, stable growth and nucleation prior to the final failure. The AE statistical behavior of each individual stage is dependent on the number and size distribution of micro-cracks. The results obtained in the laboratory are useful to understand the various stages of micro-cracking activity during the fracture process in quasi-brittle materials such as concrete & mortar and extend them for field applications.

Process Development for Functionalization of Cotton with Silver Nanoparticles Synthesized by Bio-based Approaches

Cotton is a widely used raw material for textiles but drawbacks regarding their poor mechanical properties often limit their applications as functional materials. The present investigation involved process development for one step coating of cotton with silver nanoparticles (SNP) synthesized using Azadirachta indica and Citrus limon extract to develop functional textiles. Addition of starch to functional textiles led to efficient binding of nanoparticles to fabric and led to drastic decrease in release of silver from fabricated textiles after ten washing cycles enhancing their environment friendliness. Differential scanning calorimetry, scanning electron microscopy, FT-IR analysis and mechanical studies demonstrated efficient binding of nanoparticles to fabric through bio-based processes. The functionalized textiles developed by the bio-based methods showed significant antibacterial activity against E. coli and S. aureus (with 99% microbial reduction). Present work offers a simple procedure for coating SNP using bio-based approaches with promising applications in specialized functions.

Electrical switching in amorphous Si-Te-Ge thin films: Impact of input energy on crystallization process and switching parameters

Electrical switching studies on amorphous Si15Te74Ge11 thin film devices show interesting changes in the switching behavior with changes in the input energy supplied; the input energy determines the extent of crystallization in the active volume, which is reflected in the value of SET resistances. This in turn, determines the trend exhibited by switching voltage (V-t) for different input conditions. The results obtained are analyzed on the basis of the amount of Joule heat generated, which determines the temperature of the active volume. Depending on the final temperature, devices are rendered either in the intermediate state with a resistance of 5*10(2) Omega or the ON state with a resistance of 5*10(1) Omega. (C) 2013 Elsevier B.V. All rights reserved.

Dynamic multi-relational Chinese restaurant process for analyzing influences on users in social media

We study the problem of analyzing influence of various factors affecting individual messages posted in social media. The problem is challenging because of various types of influences propagating through the social media network that act simultaneously on any user. Additionally, the topic composition of the influencing factors and the susceptibility of users to these influences evolve over time. This problem has not been studied before, and off-the-shelf models are unsuitable for this purpose. To capture the complex interplay of these various factors, we propose a new non-parametric model called the Dynamic Multi-Relational Chinese Restaurant Process. This accounts for the user network for data generation and also allows the parameters to evolve over time. Designing inference algorithms for this model suited for large scale social-media data is another challenge. To this end, we propose a scalable and multi-threaded inference algorithm based on online Gibbs Sampling. Extensive evaluations on large-scale Twitter and Face book data show that the extracted topics when applied to authorship and commenting prediction outperform state-of-the-art baselines. More importantly, our model produces valuable insights on topic trends and user personality trends beyond the capability of existing approaches.