http://hdl.handle.net/10366/156573 Wed, 22 Apr 2026 23:51:23 GMT 2026-04-22T23:51:23Z http://hdl.handle.net/10366/159475 [EN] Workers who are occupationally exposed to radioactive aerosols are usually subjected to periodic controls of internal contamination by performing bioassays (whole body or partial body monitoring and measurement of excreta samples). The intakes are also estimated by using Static Air Samples (SAS). These measurements are used to estimate the radioactive intakes of the workers. A typical assumption is the workers are chronically (constant) exposed for long periods of time. However, the intakes are random and there are also periods without any exposure (weekends, holidays, etc.). The method presented here considers both facts. Simulations help to choose the most appropriate method of evaluation to minimize the statistical uncertainties in the intake. It has been applied to evaluate workers exposed to UO2 aerosols for a long time (30 years or more for most of them) in the same working area (sintering). Results of measurements of uranium in urine and daily intakes (from SAS) of these workers have been used. For this evaluation, the new Occupational Intakes of Radionuclides (OIR) biokinetic models of the International Commission on Radiological Protection (ICRP) for uranium have been solved. For some workers the evaluation gives a significative deviation between the intake estimated from urine samples and the intake estimated using the SAS values, supporting the idea that the physiological standard parameters of the reference worker are not always applicable. The computations have been implemented in the BIOKMOD code. Sat, 01 Jan 2022 00:00:00 GMT http://hdl.handle.net/10366/159475 2022-01-01T00:00:00Z http://hdl.handle.net/10366/156638 [EN]Maxi-min efficiency criteria are a kind of multi-objective criteria, since they enable us to take into consideration several tasks expressed by different component-wise criteria. However, they are difficult tomanage because of their lack of differentiability. As a consequence, maxi-min efficiency designs are frequently built through heuristic and ad hoc algorithms, without the possibility of checking for their optimality. The main contribution of this study is to prove that the maxi-min efficiency optimality is equivalent to a Bayesian criterion, which is differentiable. In addition, we provide an analytic method to find the prior probability associated with a maxi-min efficient design, making feasible the application of the equivalence theorem. Two illustrative examples show how the proposed theory works. Sun, 01 Jan 2023 00:00:00 GMT http://hdl.handle.net/10366/156638 2023-01-01T00:00:00Z