Micro-blast waves using detonation transmission tubing

Micro-blast waves emerging from the open end of a detonation transmission tube were experimentally visualized in this study. A commercially available detonation transmission tube was used (Nonel tube, M/s Dyno Nobel, Sweden), which is a small diameter tube coated with a thin layer of explosive mixture (HMX traces of Al) on its inner side. The typical explosive loading for this tube is of the order of 18 mg/m of tube length. The blast wave was visualized using a high speed digital camera (frame rate 1 MHz) to acquire time-resolved schlieren images of the resulting flow field. The visualization studies were complemented by computational fluid dynamic simulations. An analysis of the schlieren images showed that although the blast wave appears to be spherical, it propagates faster along the tube axis than along a direction perpendicular to the tube axis. Additionally, CFD analysis revealed the presence of a barrel shock and Mach disc, showing structures that are typical of an underexpanded jet. A theory in use for centered large-scale explosions of intermediate strength gave good agreement with the blast trajectory along the tube axis. The energy of these micro-blast waves was found to be J and the average TNT equivalent was found to be . The repeatability in generating these micro-blast waves using the Nonel tube was very good and this opens up the possibility of using this device for studying some of the phenomena associated with muzzle blasts in the near future.

Hydro-mechanical characterization of Barmer 1 bentonite from Rajasthan, India

The conceptual model for deep geological disposal of high level nuclear waste (HLW) is based on multiple barrier system consisting of natural and engineered barriers. Buffer/backfill material is regarded as the most important engineered barrier in HLW repositories. Due to large swelling ability, cation adsorption capacity, and low permeability bentonite is considered as suitable buffer material in HLW repositories. Japan has identified Kunigel VI bentonite, South Korea - Kyungju bentonite, China - GMZ bentonite, Belgium - FoCa clay, Sweden - MX-80 bentonite, Spain - FEBEX bentonite and Canada - Avonseal bentonite as candidate bentonite buffer for deep geological repository program. An earlier study on Indian bentonites by one of the authors suggested that bentonite from Barmer district of Rajasthan (termed Barmer 1 bentonite), India is suited for use as buffer material in deep geological repositories. However, the hydro-mechanical properties of the Barmer 1 bentonite are unavailable. This paper characterizes Barmer 1 bentonite for hydro-mechanical properties, such as, swell pressure, saturated permeability, soil water characteristic curve (SWCC) and unconfined compression strength at different dry densities. The properties of Barmer 1 bentonite were compared with bentonite buffers reported in literature and equations for designing swell pressure and saturated permeability coefficient of bentonite buffers were arrived at. (C) 2013 Elsevier B.V. All rights reserved.

Lesion Detection in Breast Ultrasound Images Using Tissue Transition Analysis

Breast cancer is one of the leading cause of cancer related deaths in women and early detection is crucial for reducing mortality rates. In this paper, we present a novel and fully automated approach based on tissue transition analysis for lesion detection in breast ultrasound images. Every candidate pixel is classified as belonging to the lesion boundary, lesion interior or normal tissue based on its descriptor value. The tissue transitions are modeled using a Markov chain to estimate the likelihood of a candidate lesion region. Experimental evaluation on a clinical dataset of 135 images show that the proposed approach can achieve high sensitivity (95 %) with modest (3) false positives per image. The approach achieves very similar results (94 % for 3 false positives) on a completely different clinical dataset of 159 images without retraining, highlighting the robustness of the approach.

Average Overlap for Clustering Incomplete Data using Symmetric Non-Negative Matrix Factorization

Clustering techniques which can handle incomplete data have become increasingly important due to varied applications in marketing research, medical diagnosis and survey data analysis. Existing techniques cope up with missing values either by using data modification/imputation or by partial distance computation, often unreliable depending on the number of features available. In this paper, we propose a novel approach for clustering data with missing values, which performs the task by Symmetric Non-Negative Matrix Factorization (SNMF) of a complete pair-wise similarity matrix, computed from the given incomplete data. To accomplish this, we define a novel similarity measure based on Average Overlap similarity metric which can effectively handle missing values without modification of data. Further, the similarity measure is more reliable than partial distances and inherently possesses the properties required to perform SNMF. The experimental evaluation on real world datasets demonstrates that the proposed approach is efficient, scalable and shows significantly better performance compared to the existing techniques.