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Deuterium （D） and tritium （T） have been regarded as the first-generation fuels for achieving commercial fusion energy. However， the utilization of the radionuclide tritium introduces concerns related to radioactive safety. This study sought to investigate methods for estimating airborne tritium sources following a fusion reactor incident. An algorithm that combines an adaptive Kalman filter with a deep feedforward neural network was developed to determine the tritium release height and rate. By utilizing observed data both pre- and post-filtering as inputs， the neural network's predictions for the tritium release rate were analyzed. The findings indicate that filtering significantly lowers the prediction errors. Considering a 20% monitoring error， the average relative error for the estimated release height is approximately 3% and that for the release rate is approximately 4%.

The density regression fitting method is generally used to predict total organic carbon(TOC) content of shale reservoir with seismic data. Because it only considers the linear relationship of single factor, the prediction error is large. Aiming at the shortcomings of this method, a TOC content prediction method based on deep learning was proposed. Firstly, the elastic parameter curves with the highest correlation with the TOC content curve were selected from the logging data as the input data of the sample set, and the TOC content curve was used as the output data of the sample set. Based on the sample set, a deep feedforward neural network model for TOC content prediction was constructed. Then, the structure of the network model was adjusted and the parameters of the network were optimized by conjugate gradient method. Finally, the elastic parameter data volume obtained from prestack amplitude versus offset (AVO) inversion was input into the deep feedforward neural network model to predict the final TOC content volume. Through the application of actual logging and seismic data of shale reservoir in Sichuan Basin, the advantages of the new method over the conventional regression fitting method were compared, the practicability and feasibility of the proposed method were verified, which provides a new idea for TOC content prediction of shale reservoir. [ABSTRACT FROM AUTHOR]

Human body surface area (BSA) plays a crucial role in clinical medicine, but most of the existing BSA formulas only use two parameters: height and weight, and adopt the method of matching simple function to estimate the body surface area. Doctors also show that the existing clinical BSA formulas have a large calculation error. To solve these problems, a new BSA regression prediction model is proposed. The regression model consists of two parts: firstly, the factors of body surface area with high correlation are selected by correlation and significance analysis; secondly, a regression model is constructed by training the deep feed-forward neural network with 104 sets of human body data. 5-fold cross validation and independent test set and two verification methods are adopted in the experiments. Firstly, the accuracy of the deep feedforward neural network model and the traditional human surface area calculation formula are evaluated, and the results are compared and analyzed. Secondly, the accuracy of the deep feedforward neural network model and the three algorithm models are evaluated, and the results are compared and analyzed. Compared with the traditional methods, the determination coefficient of the deep feedforward neural network model is higher than that of the two traditional methods, and is six percentage points higher than the traditional method with better results, and the error of deep feedforward neural network model is nearly twice as low as that of the traditional method. Compared with the three algorithm models, the deep feedforward neural network improves the determination coefficient by two percentage points and reduce the error. The experimental results of consistency analysis also show that the 95% consistency limit of the deep feedforward neural network is the smallest and the consistency is the best. Through above experiments, it is proved that the proposed regression framework can better calculate body surface area and obtain more accurate prediction value. [ABSTRACT FROM AUTHOR]