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SUMMARYThe objective of this study is to develop an analytical model that can predict the building-relevant deflections induced by tunnelling or mining subsidence. The model takes into account soil-structure interactions due to differences in stiffness between the ground and the building.The ground is modelled by the Winkler model with an initial ground curvature equivalent to the freefield ground movements. The building is modelled by a horizontal beam with uniform loading. The static and cinematic equilibrium of both the ground and the building are then calculated to assess the final building and ground shape, and the building deflection is derived.The resulting analytical model is used to investigate the influence of the ground and the building's mechanical properties, the building load and the initial value of the free-field ground curvature (hogging or sagging). The model appears to be more comprehensive than those reported elsewhere that address the problem with numerical models. In particular, the analytical model makes it possible to distinguish two different final situations-with continuous or discontinuous contact between the ground and the building. The model is compared with numerical results and used to analyse a case study.

Background:Mycobacterium tuberculosis complex (MTBC) and non-tuberculous mycobacteria (NTM) may or may not have same clinical presentations, but the treatment regimens are always different. Laboratory differentiation between MTBC and NTM by routine methods are time consuming and cumbersome to perform. We have evaluated the role of GenoType® Mycobacterium common mycobacteria/additional species (CM/AS) assay for differentiation between MTBC and different species of NTM in clinical isolates from tuberculosis (TB) cases.Materials and Methods:A total of 1080 clinical specimens were collected from January 2010 to June 2012. Diagnosis was performed by Ziehl-Neelsen staining followed by culture in BacT/ALERT 3D system (bioMerieux, France). A total of 219 culture positive clinical isolates (BacT/ALERT® MP cultures) were selected for differentiation by p-nitrobenzoic acid (PNB) sensitivity test as and BIO-LINE SD Ag MPT64 TB test considering as the gold standard test. Final identification and differentiation between MTBC and different species of NTM were further confirmed by GenoType® Mycobacterium CM/AS assay (Hain Lifescience, Nehren, Germany).Results:Out of 219 BacT/ALERT® MP culture positive isolates tested by PNB as 153 MTBC (69.9%) and by GenoType® Mycobacterium CM/AS assay as 159 (72.6%) MTBC and remaining 60 (27.4%) were considered as NTM species. The GenoType® Mycobacterium CM/AS assay was proved 99.3% sensitive and 98.3% specific for rapid differentiation of MTBC and NTM. The most common NTM species were; Mycobacterium fortuitum 20 (33.3%) among rapid growing mycobacteria and Mycobacterium intracellulare 11 (18.3%) among slow growing mycobacteria.Conclusion:The GenoType® Mycobacterium assay makes rapid and accurate identification of NTM species as compared with different phenotypic and molecular diagnostic tool and helps in management of infections caused by different mycobacteria.

Spatially distributed sensor nodes in wireless sensor networks (WSNs) can be used to monitor large unmanned areas. However, there are many limitations to WSNs, and the influence and accessibility of the sensors in these networks are limited to localized areas. Another popular technology today is cloud computing (CC). CC can provide a potent and scalable processing and storage infrastructure that can be used to perform the analysis of online as well as offline data streams provided by the sensors. It is possible to virtualize the sensor networks to provide these networks as a utility service. In this paper, we propose "Mils-Cloud," which is a sensor-cloud architecture utilizing this infrastructure for developing architecture for the integration of military tri-services in a battlefield scenario. We propose a hierarchical architecture of sensor-cloud with users having different levels of priority. The results show that reserving about 20%-25% of resources actually boosts the performance of the system for priority users without compromising the availability for normal users.

Centrality measures have been proved to be a salient computational science tool for analyzing networks in the last two to three decades aiding many problems in the domain of computer science, economics, physics, and sociology. With increasing complexity and vividness in the network analysis problems, there is a need to modify the existing traditional centrality measures. Weighted centrality measures usually consider weights on the edges and assume the weights on the nodes to be uniform. One of the main reasons for this assumption is the hardness and challenges in mapping the nodes to their corresponding weights. In this paper, we propose a way to overcome this kind of limitation by hybridization of the traditional centrality measures. The hybridization is done by taking one of the centrality measures as a mapping function to generate weights on the nodes and then using the node weights in other centrality measures for better complex ranking.