Flux Creep and Flux Jumping

We consider the flux jump instability of the Bean's critical state arising in the flux creep regime in type-II superconductors. We find the flux jump field, $B_j$, that determines the superconducting state stability criterion. We calculate the dependence of $B_j$ on the external magnetic field ramp rate, $\dot B_e$. We demonstrate that under the conditions typical for most of the magnetization experiments the slope of the current-voltage curve in the flux creep regime determines the stability of the Bean's critical state, {\it i.e.}, the value of $B_j$. We show that a flux jump can be preceded by the magneto-thermal oscillations and find the frequency of these oscillations as a function of $\dot B_e$.Comment: 7 pages, ReVTeX, 2 figures attached as postscript file

Visual Analytics of Surveillance Data on Foodborne Vibriosis, United States, 1973–2010

Foodborne illnesses caused by microbial and chemical contaminants in food are a substantial health burden worldwide. In 2007, human vibriosis (non-cholera Vibrio infections) became a notifiable disease in the United States. In addition, Vibrio species are among the 31 major known pathogens transmitted through food in the United States. Diverse surveillance systems for foodborne pathogens also track outbreaks, illnesses, hospitalization and deaths due to non-cholera vibrios. Considering the recognition of vibriosis as a notifiable disease in the United States and the availability of diverse surveillance systems, there is a need for the development of easily deployed visualization and analysis approaches that can combine diverse data sources in an interactive manner. Current efforts to address this need are still limited. Visual analytics is an iterative process conducted via visual interfaces that involves collecting information, data preprocessing, knowledge representation, interaction, and decision making. We have utilized public domain outbreak and surveillance data sources covering 1973 to 2010, as well as visual analytics software to demonstrate integrated and interactive visualizations of data on foodborne outbreaks and surveillance of Vibrio species. Through the data visualization, we were able to identify unique patterns and/or novel relationships within and across datasets regarding (i) causative agent; (ii) foodborne outbreaks and illness per state; (iii) location of infection; (iv) vehicle (food) of infection; (v) anatomical site of isolation of Vibrio species; (vi) patients and complications of vibriosis; (vii) incidence of laboratory-confirmed vibriosis and V. parahaemolyticus outbreaks. The additional use of emerging visual analytics approaches for interaction with data on vibriosis, including non-foodborne related disease, can guide disease control and prevention as well as ongoing outbreak investigations