Influence of Precracking Techniques on Fracture Toughness of Carbon-Carbon Composites

Carbon-Carbon composites are one such material which give designers significant importance for advanced applications over conventional materials. The remarkable characteristics of carbon-carbon composites had made these products initially extremely useful in the field of aerospace and defense applications. Now, they are presently used in many applications such as biomedical implants, glass, and high temperature glass, etc.In material science, fracture toughness is a trait that depicts the ability of a material to withstand fractures and is one of the most important features in many design applications of any material. A precracked specimen is a sample that is used to accurately assess the distribution of cracks and it is a favored method. This paper describes a comparison of four precracking techniques for carbon-carbon composites using SENB specimen. The potential implications of these techniques on fracture toughness values have been evaluated. The outcome of this work indicates that precracking with a jewel saw is recommended over the other techniques

Jordan-Schwinger realizations of three-dimensional polynomial algebras

A three-dimensional polynomial algebra of order $m$ is defined by the commutation relations $[P_0, P_\pm]$ $=$ $\pm P_\pm$, $[P_+, P_-]$ $=$ $\phi^{(m)}(P_0)$ where $\phi^{(m)}(P_0)$ is an $m$-th order polynomial in $P_0$ with the coefficients being constants or central elements of the algebra. It is shown that two given mutually commuting polynomial algebras of orders $l$ and $m$ can be combined to give two distinct $(l+m+1)$-th order polynomial algebras. This procedure follows from a generalization of the well known Jordan-Schwinger method of construction of $su(2)$ and $su(1,1)$ algebras from two mutually commuting boson algebras.Comment: 10 pages, LaTeX2

Biomedical Event Trigger Identification Using Bidirectional Recurrent Neural Network Based Models

Biomedical events describe complex interactions between various biomedical entities. Event trigger is a word or a phrase which typically signifies the occurrence of an event. Event trigger identification is an important first step in all event extraction methods. However many of the current approaches either rely on complex hand-crafted features or consider features only within a window. In this paper we propose a method that takes the advantage of recurrent neural network (RNN) to extract higher level features present across the sentence. Thus hidden state representation of RNN along with word and entity type embedding as features avoid relying on the complex hand-crafted features generated using various NLP toolkits. Our experiments have shown to achieve state-of-art F1-score on Multi Level Event Extraction (MLEE) corpus. We have also performed category-wise analysis of the result and discussed the importance of various features in trigger identification task.Comment: The work has been accepted in BioNLP at ACL-201

Image Compression Techniques by using Wavelet Transform

This paper is concerned with a certain type of compression techniques by using wavelet transforms. Wavelets are used to characterize a complex pattern as a series of simple patterns and coefficients that, when multiplied and summed, reproduce the original pattern.  The data compression schemes can be divided into lossless and lossy compression. Lossy compression generally provides much higher compression than lossless compression. Wavelets are a class of functions used to localize a given signal in both space and scaling domains. A MinImage was originally created to test one type of wavelet and the additional functionality was added to Image to support other wavelet types, and the EZW coding algorithm was implemented to achieve better compression. Keywords: Wavelet Transforms, Image Compression, Lossless Compression, Lossy Compressio