Simulations of clustering of vortices in superconductors: a possible origin of the second peak

Abstract We describe by use of molecular dynamics simulations how the attraction, at short range, between the vortex cores aids the clustering of vortices. Our simulations suggest that the short range core attraction and ensuing vortex clustering is the mechanism responsible for the second peak in the magnetisation loops of superconductors.

Vortices Clustering: The Origin of the Second Peak in the Magnetisation Loops of High Temperature Superconductors

We study vortex clustering in type II Superconductors. We demonstrate that the ``second peak'' observed in magnetisation loops may be a dynamical effect associated with a density driven instability of the vortex system. At the microscopic level the instability shows up as the clustering of individual vortices at (rare) preferential regions of the pinning potential. In the limit of quasi-static ramping the instability is related to a phase transition in the equilibrium vortex system.Comment: 11 pages + 3 figure

A qualitative study of nursing student experiences of clinical practice

BACKGROUND: Nursing student's experiences of their clinical practice provide greater insight to develop an effective clinical teaching strategy in nursing education. The main objective of this study was to investigate student nurses' experience about their clinical practice. METHODS: Focus groups were used to obtain students' opinion and experiences about their clinical practice. 90 baccalaureate nursing students at Shiraz University of Medical Sciences (Faculty of Nursing and Midwifery) were selected randomly from two hundred students and were arranged in 9 groups of ten students. To analyze the data the method used to code and categories focus group data were adapted from approaches to qualitative data analysis. RESULTS: Four themes emerged from the focus group data. From the students' point of view," initial clinical anxiety", "theory-practice gap"," clinical supervision", professional role", were considered as important factors in clinical experience. CONCLUSION: The result of this study showed that nursing students were not satisfied with the clinical component of their education. They experienced anxiety as a result of feeling incompetent and lack of professional nursing skills and knowledge to take care of various patients in the clinical setting

Deconvolution and elastography based on dimensional ultrasound

This paper is in two parts and addresses two of getting more information out of the RF signal from three-dimensional (3D) mechanically-swept medical ultrasound . The first topic is the use of non-blind deconvolution improve the clarity of the data, particularly in the direction to the individual B-scans. The second topic is imaging. We present a robust and efficient approach to estimation and display of axial strain information. deconvolution, we calculate an estimate of the point-spread at each depth in the image using Field II. This is used as of an Expectation Maximisation (EM) framework in which ultrasound scatterer field is modelled as the product of (a) a smooth function and (b) a fine-grain varying function. the E step, a Wiener filter is used to estimate the scatterer based on an assumed piecewise smooth component. In the M , wavelet de-noising is used to estimate the piecewise smooth from the scatterer field. strain imaging, we use a quasi-static approach with efficient based algorithms. Our contributions lie in robust and 3D displacement tracking, point-wise quality-weighted , and a stable display that shows not only strain but an indication of the quality of the data at each point in the . This enables clinicians to see where the strain estimate is and where it is mostly noise. deconvolution, we present in-vivo images and simulations quantitative performance measures. With the blurred 3D taken as OdB, we get an improvement in signal to noise ratio 4.6dB with a Wiener filter alone, 4.36dB with the ForWaRD and S.18dB with our EM algorithm. For strain imaging show images based on 2D and 3D data and describe how full D analysis can be performed in about 20 seconds on a typical . We will also present initial results of our clinical study to explore the applications of our system in our local hospital. © 2008 IEEE

Urban design for air quality

Urban design influences where air pollution is produced, how it disperses through streets and neighbourhoods, and where, when, and how much people are exposed. This guide explains how good urban design can improve air quality using simple principles that benefit air quality and providing practical guidance and illustrations outlining how to implement them in urban areas