RESUMO
This research presents a methodology for volume fraction predictions in water-gas-oil multiphase systems based on gamma-ray densitometry and artificial neural networks. The simulated geometry uses a dual-energy gamma-ray source and dual-modality (transmitted and scattered beams). The Am-241 and Cs-137 sources and two NaI(Tl) detectors have been used in this methodology. Different data from the pulse height distribution were used to train the artificial neural network to evaluate the volume fraction prediction. The MCNPX code has been used to develop the theoretical model for stratified regime and to provide data for the artificial neural network. 5-layers feed-forward multilayer perceptron using backpropagation training algorithm and General Regression Neural Networks has been used with different designs. The artificial neural network design that presented the best results of volume fraction prediction has a mean relative error below 2.0%.
RESUMO
A 48 year old white woman was admitted to the hospital because of several bouts of migratory polyarthritis, weight loss, fever, and abdominal pain over a period of 15 months. She had been taking propylthiouracil 100 mg daily for three years for hyperthyroidism treatment. A test for antineutrophil cytoplasmic autoantibodies (ANCA) was positive with a perinuclear pattern of staining. Antiphospholipid antibodies were also detected. Colonoscopy showed several ulcers on intestinal mucosa and the biopsy specimen showed intense microscopic vasculitis. The patient is well after methylprednisolone pulse therapy and eight months of oral azathioprine. A surveillance colonoscopy showed complete healing of intestinal ulcers. No recurrence of symptoms has occurred and autoantibodies are negative, 10 months after treatment finished. The sequence of events suggests a propylthiouracil induced vasculitis p-ANCA positive and an antiphospholipid syndrome. This is the first report of colonic ulcers diagnosed and successfully treated in such circumstances.
