ABSTRACT
This paper describes the process adopted at the European Organization for Nuclear Research (CERN) to quantify uncertainties affecting the characterization of very-low-level radioactive waste. Radioactive waste is a by-product of the operation of high-energy particle accelerators. Radioactive waste must be characterized to ensure its safe disposal in final repositories. Characterizing radioactive waste means establishing the list of radionuclides together with their activities. The estimated activity levels are compared to the limits given by the national authority of the waste disposal. The quantification of the uncertainty affecting the concentration of the radionuclides is therefore essential to estimate the acceptability of the waste in the final repository but also to control the sorting, volume reduction and packaging phases of the characterization process. The characterization method consists of estimating the activity of produced radionuclides either by experimental methods or statistical approaches. The uncertainties are estimated using classical statistical methods and uncertainty propagation. A mixed multivariate random vector is built to generate random input parameters for the activity calculations. The random vector is a robust tool to account for the unknown radiological history of legacy waste. This analytical technique is also particularly useful to generate random chemical compositions of materials when the trace element concentrations are not available or cannot be measured. The methodology was validated using a waste population of legacy copper activated at CERN. The methodology introduced here represents a first approach for the uncertainty quantification (UQ) of the characterization process of waste produced at particle accelerators.
ABSTRACT
OBJECTIVES: The French population is growing and ageing. It is expected to increase by 2.7% by 2020, and the number of individuals over 65 years of age is expected to increase by 3.3 million, a 33% increase, between 2005 and 2020. As the number of vascular surgery procedures is closely associated with the age of a population, it is anticipated that there will be a significant increase in the workload of vascular surgeons. STUDY DESIGN: A model is presented to predict changes in vascular surgery activity according to population ageing, including other parameters that could affect workload evolution. METHODS: Three types of arterial procedures were studied: infrarenal abdominal aortic aneurysm (AAA) surgery, peripheral arterial occlusive disease (PAOD) procedures and carotid artery (CEA) procedures. Data were selected and extracted from the national PMSI (Medical Information System Program) database. Data obtained from 2000 were used to predict data based on an ageing population for 2008. From this model, a weighted index was defined for each group by comparing expected and observed workloads. RESULTS: According to the model, over this 8-year period, there was an overall increase in vascular procedures of 52.2%, with an increase of 89% in PAOD procedures. Between 2000 and 2009, the total increase was 58.0%, with 3.9% for AAA procedures, 101.7% for PAOD procedures and 13.2% for CEA procedures. The weighted model based on an ageing population and corrected by a weighted factor predicted this increase. CONCLUSION: This weighted model is able to predict the workload of vascular surgeons over the coming years. An ageing population and other factors could result in a significant increase in demand for vascular surgical services.
