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The liquid scintillation counting (LSC) is an established method for direct quantitative measurement of tritiated water, but the generated radiotoxic waste, its storage, handling and proper disposal is the major concern arising from using scintillation cocktail. In our work, CaF 2 (Eu) was chosen as the scintillation material to substitute scintillation cocktail in low-level tritiated water measurement. In order to find out the ideal vial type, grain size of powders and CaF 2 (Eu) powders quantity, three groups of 6 and 20 ml scintillation vials filled with different CaF 2 (Eu) powders were prepared. Using polyethylene vial, which is much smaller than the glass vial, significantly increased the detection efficiency. The highest detection efficiency was achieved by using polyethylene vial and 4 g S sample (average grain sizes: 8.355 µm). In addition, the polyethylene vials with 4 g S sample inside were most sensitive and had the best linear relation between tritium concentration and count rate. Under this condition, the detection limit can be as low as about 13 Bq/mL. The CaF 2 (Eu) powder can be re-used after a simple treatment. What's more, it is nontoxic as well as easy to store and disposal. These make it advantageous compared with traditional scintillation cocktail. With these results, the replacement of the scintillation cocktail with CaF 2 (Eu) powder in low-level tritiated water measurements is promising.

The attacks on the critical infrastructure network have increased sharply, and the strict management measures of the critical infrastructure network have caused its correlation analysis technology for security events to be relatively backward; this makes the critical infrastructure network’s security situation more severe. Currently, there is no common correlation analysis technology for the critical infrastructure network, and most technologies focus on expanding the dimension of data analysis, but with less attention to the optimization of analysis performance. The analysis performance does not meet the practical environment, and real-time analysis is even more impossible; as a result, the efficiency of security threat detection is greatly declined. To solve this issue, we propose the greedy tree algorithm, a correlation analysis approach based on the greedy algorithm, which optimizes event analysis steps and significantly improves the performance, so the real-time correlation analysis can be realized. We first verify the performance of the algorithm through formalization, and then the G-CAS (Greedy Correlation Analysis System) is implemented based on this algorithm and is applied in a real critical infrastructure network, which outperformed the current mainstream products.

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