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The Monte Carlo code FLUKA is used to simulate the production of a number of positron emitting radionuclides, ¹⁸F, ¹³N, ⁹⁴Tc, ⁴⁴Sc, ⁶⁸Ga, ⁸⁶Y, ⁸⁹Zr, ⁵²Mn, ⁶¹Cu and ⁵⁵Co, on a small medical cyclotron with a proton beam energy of 13 MeV. Experimental data collected at the TR13 cyclotron at TRIUMF agree within a factor of 0.6 ± 0.4 with the directly simulated data, except for the production of ⁵⁵Co, where the simulation underestimates the experiment by a factor of 3.4 ± 0.4. The experimental data also agree within a factor of 0.8 ± 0.6 with the convolution of simulated proton fluence and cross sections from literature. Overall, this confirms the applicability of FLUKA to simulate radionuclide production at 13 MeV proton beam energy.
[⁶⁸Ga(DOTATATE)] has demonstrated its clinical usefulness. Both Fe³⁺ and Cu²⁺, potential contaminants in Gallium-68 generator eluent, substantially reduce the radiochemical (RC) yield of [⁶⁸Ga(DOTATATE)] if the metal/ligand ratio of 1:1 is exceeded. A variety of compounds were examined for their potential ability to reduce this effect. Most had no effect on RC yield. However, addition of phosphate diminished the influence of Fe³⁺ by likely forming an insoluble iron salt. Addition of ascorbic acid reduced Cu²⁺ and Fe³⁺ to Cu⁺ and Fe²⁺ respectively, both of which have limited impact on RC yields. At low ligand amounts (5 nmol DOTATATE), the addition of 30 nmol phosphate (0.19 mM) increased the tolerance of Fe3⁺ from 4 nmol to 10 nmol (0.06 mM), while the addition of ascorbic acid allowed high RC yields (>95%) in the presence of 40 nmol Fe³⁺ (0.25 mM) and 100 nmol Cu²⁺ (0.63 mM). The effect of ascorbic acid was highly pH-dependant, and gave optimal results at pH 3.
The potential of SMART climbing robot combined with a weatherproof cabin for rotor blade maintenance
(2016)
In this paper we present an extension of the action language Golog that allows for using fuzzy notions in non-deterministic argument choices and the reward function in decision-theoretic planning. Often, in decision-theoretic planning, it is cumbersome to specify the set of values to pick from in the non-deterministic-choice-of-argument statement. Also, even for domain experts, it is not always easy to specify a reward function. Instead of providing a finite domain for values in the non-deterministic-choice-of-argument statement in Golog, we now allow for stating the argument domain by simply providing a formula over linguistic terms and fuzzy uents. In Golog’s forward-search DT planning algorithm, these formulas are evaluated in order to find the agent’s optimal policy. We illustrate this in the Diner Domain where the agent needs to calculate the optimal serving order.
20 Years of RoboCup
(2016)