Patient´s knowledge about radiation and radiological protection

Purpose: To obtain and analyse patient´s knowledge and perceptions regarding radiation exposure, from both natural and man-made radiation of medical procedures and interventions. Verify if patients worry about their exposure when undergoing medical exams, are aware of associated risks and means of radiological protection and if their knowledge on medical radiation exposure affects their own decisions. Methods and Materials: On a medical environment a self-applied questionnaire was used as instrument and assigned to patients who would undergo medical imaging exams involving ionising radiation. A total of 300 valid questionnaires were interpreted and statistically analysed through descriptive statistics and Phi & Cramer´s V correlation tests. Results: 44.3% of patients believe most of their exposure derives from electronic appliances and 25% from medical imaging exams, while patient´s with higher education levels tend to consider is comes from the environment. The great majority of patients (95%) consider that only certified personnel should operate medical imaging equipment, but 74% never ask for their qualifications. 66.3% of patients claim that Technologists have more education on radiological protection and about 60% of patients rarely or never worry about radiation exposure when undergoing medical imaging exams. Conclusion: Patients overestimate the risks of industrial radiation exposure while they underestimate the associated risks of medical radiation exposure and the Technologist´s ability to reduce the inherent radiation exposure of medical imaging exams. Patient´s knowledge on radiation and radiological protection is based more on perceptions and beliefs, rather than factual knowledge.

Prediction of the solar radiation using RBF neural networks and ground-to-sky images

In this study, Artificial Neural Networks are applied to multistep long term solar radiation prediction. The networks are trained as one-step-ahead predictors and iterated over time to obtain multi-step longer term predictions. Auto-regressive and Auto-regressive with exogenous inputs solar radiationmodels are compared, considering cloudiness indices as inputs in the latter case. These indices are obtained through pixel classification of ground-to-sky images. The input-output structure of the neural network models is selected using evolutionary computation methods.