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During nuclear accidents, decision-makers need to handle considerable data to take appropriate protective actions to protect people and the environment from radioactive material release. In such scenarios, machine learning can be an essential tool in facilitating the protection action decisions that will be made by decision-makers. By feeding machines software with big data to analyze and identify nuclear accident behavior, types, and the concentrations of released radioactive materials can be predicted, thus helping in early warning and protecting people and the environment. In this study, based on the ground deposition concentration of radioactive materials at different distances offsite in an emergency planning zone (EPZ), we proposed classification and regression models for three severe accidents. The objective of the classification model is to recognize the transient situation type for taking appropriate actions, while the objective of the regression model is to estimate the concentrations of the released radioactive materials. We used the Personal Computer Transient Analyser (PCTRAN) Advanced Power Reactor (APR) 1400 to simulate three severe accident scenarios and to generate a source term released to the environment. Additionally, the Radiological Consequence Analysis Program (RCAP) was used to assess the off-site consequences of nuclear power plant accidents and to estimate the ground deposition concentrations of radionuclides. Moreover, ground deposition concentrations at different distances were used as input data for the classification and regression tree (CART) models to obtain an accident pattern and to establish a prediction model. Results showed that the ground deposition concentration at a near distance from a nuclear power plant is a more informative parameter in predicting the concentration of radioactive material release, while the ground deposition concentration at a far distance is a very informative parameter in identifying accident types. In the regression model, the R-square of the training and test data was 0.995 and 0.994, respectively, showing a mean strong linear relationship between the predicted and actual concentration of radioactive material release. The mean absolute percentage error was found to be 26.9% and 28.1% for the training and test data, respectively. In the classification model, the model predicted a scenario (1) of 99.8% and 98.9%, scenario (2) of 98.4% and 91.6%, and scenario (3) of 98.6% and 94.7% for the training and test data, respectively.

Background Non-thermal atmospheric plasma has proven its benefits in sterilization, cauterization and even in cancer reduction. Furthermore, physical plasma generated by dielectric barrier discharge (DBD) promotes wound healing in vivo and angiogenesis in vitro . Moreover, cutaneous blood flow and oxygen saturation can be improved in human skin. These effects are mostly explained by reactive oxygen species (ROS), but electric fields, currents and ultraviolet radiation may also have an impact on cells in the treated area. Usually, single session application is used. The aim of this study was to evaluate the effects of the repetitive use of cold atmospheric plasma (rCAP) on cutaneous microcirculation. Hypothesis The repetitive use of non-thermal atmospheric plasma boosts cutaneous microcirculation effects. Methods Microcirculatory data was assessed at a defined skin area of the radial forearm of 20 healthy volunteers (17 males, 3 females; mean age 39.1 ± 14.8 years; BMI 26.4 ± 4.6 kg/m 2 ). Microcirculatory measurements were performed under standardized conditions using a combined laser Doppler and photospectrometry system. After baseline measurement, CAP was applied by a DBD plasma device for 90 s and cutaneous microcirculation was assessed for 10 min. Afterwards, a second session of CAP application was performed and microcirculation was measured for another 10 min. Then, the third application was made and another 20 min of microcirculatory parameters were assessed. Results Tissue oxygen saturation and postcapillary venous filling pressure significantly increased after the first application and returned to baseline values within 10 min after treatment. After the second and third applications, both parameters increased significantly vs. baseline until the end of the 40-minute measuring period. Cutaneous blood flow was significantly enhanced for 1 min after the first application, with no significant differences found during the remainder of the observation period. The second application improved and prolonged the effect significantly until 7 min and the third application until 13 min. Conclusion: These data indicate that the repetitive use of non-thermal atmospheric plasma boosts and prolongs cutaneous microcirculation and might therefore be a potential tool to promote wound healing.