Large negative magnetoresistance in a ferromagnetic shape memory alloy : Ni_{2+x}Mn_{1-x}Ga

5% negative magnetoresistance (MR) at room temperature has been observed in bulk Ni_{2+x}Mn_{1-x}Ga. This indicates the possibility of using Ni_{2+x}Mn_{1-x}Ga as magnetic sensors. We have measured MR in the ferromagnetic state for different compositions (x=0-0.2) in the austenitic, pre-martensitic and martensitic phases. MR is found to increase with x. While MR for x=0 varies almost linearly in the austenitic and pre-martensitic phases, in the martensitic phase it shows a cusp-like shape. This has been explained by the changes in twin and domain structures in the martensitic phase. In the austenitic phase, which does not have twin structure, MR agrees with theory based on s-d scattering model.Comment: 3 pages, 3 figures, Appl. Phys. Lett 86, 202508 (2005

Hydraulic and Hydrologic Model Calibration and Validation for an Earthquake-prone Three-Waters Network

This paper summarises the three-waters network (water, wastewater, storm water) model calibration and validation work undertaken in Christchurch after the devastating 2010–2011 earthquakes. The paper outlines some unusual and unique challenges during model calibration due to continual earthquakes in the region and the post-earthquake rebuild work. In case of water supply network model, the validation peak summer date was chosen carefully so that earthquake-related damage and associated rebuild works would have minimal impact on the captured data. The wastewater network was damaged significantly due to the earthquakes. Wastewater flow data were influenced by earthquake damage and post-earthquake major construction activities. Christchurch’s storm water network faced a number of changes – changes in topography, ground levels, river channels and liquefaction – due to the earthquakes. Ongoing model maintenance and updating was a big challenge during model calibration, and an effective collaboration among various teams – GIS, construction contractors, network operations and survey – was important for data collection, data interpretation, model calibration and validation work