A novel method for rapid comparative quantitative analysis of nuclear fuel cycles

One of the greatest obstacles facing the nuclear industry is that of sustainability, both in terms of the finite reserves of uranium ore and the production of highly radiotoxic spent fuel which presents proliferation and environmental hazards. Alternative nuclear technologies have been suggested as a means of delivering enhanced sustainability with proposals including fast reactors, the use of thorium fuel and tiered fuel cycles. The debate as to which is the most appropriate technology continues, with each fuel system and reactor type delivering specific advantages and disadvantages which can be difficult to compare fairly. This paper demonstrates a framework of performance metrics which, coupled with a first-order lumped reactor model to determine nuclide population balances, can be used to quantify the aforementioned pros and cons for a range of different fuel and reactor combinations. The framework includes metrics such as fuel efficiency, spent fuel toxicity and proliferation resistance, and relative cycle performance is analysed through parallel coordinate plots, yielding a quantitative comparison of disparate cycles. © 2011 Elsevier Ltd. All rights reserved.

Performing quantitative MFM measurements on soft magnetic nanostructures.

We have extended our previous work (Rawlings et al 2010 Phys. Rev. B 82 085404) on simulating magnetic force microscopy (MFM) images for magnetically soft samples to include an accurate representation of coated MFM tips. We used an array of square 500 nm nanomagnets to evaluate our improved MFM model. A quantitative comparison between model and experiment was performed for lift heights ranging from 20 to 100 nm. No fitting parameters were used in our comparison. For all lift heights the qualitative agreement between model and experiment was significantly improved. At low lift heights, where the magnetic signal was strong, the difference between theory and experiment was less than 30%.

Quantitative convergence analysis of multi-agent systems

We introduce a characterization of contraction for bounded convex sets. For discrete-time multi-agent systems we provide an explicit upperbound on the rate of convergence to a consensus under the assumptions of contractiveness and (weak) connectedness (across an interval.) Convergence is shown to be exponential when either the system or the function characterizing the contraction is linear. Copyright © 2007 IFAC.

Statistics anxiety, trait anxiety, learning behavior, and academic performance

The present study investigated the relationship between statistics anxiety, individual characteristics (e.g., trait anxiety and learning strategies), and academic performance. Students enrolled in a statistics course in psychology (N=147) filled in a questionnaire on statistics anxiety, trait anxiety, interest in statistics, mathematical selfconcept, learning strategies, and procrastination. Additionally, their performance in the examination was recorded. The structural equation model showed that statistics anxiety held a crucial role as the strongest direct predictor of performance. Students with higher statistics anxiety achieved less in the examination and showed higher procrastination scores. Statistics anxiety was related indirectly to spending less effort and time on learning. Trait anxiety was related positively to statistics anxiety and, counterintuitively, to academic performance. This result can be explained by the heterogeneity of the measure of trait anxiety. The part of trait anxiety that is unrelated to the specific part of statistics anxiety correlated positively with performance.