'$ * ROBOTICS AND REMOTE HANDLING CONCEPTS FOR DISPOSAL OF HIGH-LEVEL NUCLEAR WASTE

ABSTRACT This paper summarizes preliminary remote handling and robotic concepts being developed as part of the US Department of Energy's (DOE) Yucca Mountain Project. The DOE is currently evaluating the Yucca Mountain Nevada site for suitability as a possible underground geologic repository for the disposal of high level nuclear waste. The current advanced conceptual design calls for the disposal of more than 12,000 high level nuclear waste packages within a 225 km underground network of tunnels and emplacement drifts. Many of the waste packages may weigh as much as 66 tonnes and measure 1.8 m in diameter and 5.6 m long. The waste packages will emit significant levels of radiation and heat. Therefore, remote handling is a cornerstone of the repository design and operating concepts. This paper discusses potential applications areas for robotics and remote handling technologies within the subsurface repository. It also summarizes the findings of a preliminary technology survey which reviewed available robotic and remote handling technologies developed within the nuclear, mining, rail and industrial robotics and automation industries, and at national laboratories, universities, and related research institutions and government agencies

Comparative analyses of basal rate of metabolism in mammals: data selection does matter.

Basal rate of metabolism (BMR) is a physiological parameter that should be measured under strictly defined experimental conditions. In comparative analyses among mammals BMR is widely used as an index of the intensity of the metabolic machinery or as a proxy for energy expenditure. Many databases with BMR values for mammals are available, but the criteria used to select metabolic data as BMR estimates have often varied and the potential effect of this variability has rarely been questioned. We provide a new, expanded BMR database reflecting compliance with standard criteria (resting, postabsorptive state; thermal neutrality; adult, non-reproductive status for females) and examine potential effects of differential selectivity on the results of comparative analyses. The database includes 1739 different entries for 817 species of mammals, compiled from the original sources. It provides information permitting assessment of the validity of each estimate and presents the value closest to a proper BMR for each entry. Using different selection criteria, several alternative data sets were extracted and used in comparative analyses of (i) the scaling of BMR to body mass and (ii) the relationship between brain mass and BMR. It was expected that results would be especially dependent on selection criteria with small sample sizes and with relatively weak relationships. Phylogenetically informed regression (phylogenetic generalized least squares, PGLS) was applied to the alternative data sets for several different clades (Mammalia, Eutheria, Metatheria, or individual orders). For Mammalia, a 'subsampling procedure' was also applied, in which random subsamples of different sample sizes were taken from each original data set and successively analysed. In each case, two data sets with identical sample size and species, but comprising BMR data with different degrees of reliability, were compared. Selection criteria had minor effects on scaling equations computed for large clades (Mammalia, Eutheria, Metatheria), although less-reliable estimates of BMR were generally about 12-20% larger than more-reliable ones. Larger effects were found with more-limited clades, such as sciuromorph rodents. For the relationship between BMR and brain mass the results of comparative analyses were found to depend strongly on the data set used, especially with more-limited, order-level clades. In fact, with small sample sizes (e.g. <100) results often appeared erratic. Subsampling revealed that sample size has a non-linear effect on the probability of a zero slope for a given relationship. Depending on the species included, results could differ dramatically, especially with small sample sizes. Overall, our findings indicate a need for due diligence when selecting BMR estimates and caution regarding results (even if seemingly significant) with small sample sizes