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During the normal operation of pressurized water reactor, fission products activity in the primary coolant will increase when failure of fuel rods happens. The prediction of the status of fuel failure has been paid great attention from industry for the long term with respect to radiological protection, radioactive consequence and the economy. The iodine isotope activity ratio 131I/133I is one of the most important indicators to evaluate fuel reliability in nuclear power industry. In this paper, the production and release of fission products in fuel rods and the primary coolant were simulated by the kinetic model and the typical 131I/133I in the primary coolant were theoretically estimated at the equilibrium conditions for the intact fuels, fuel failure with small defect and large defects, respectively. The radiochemical data in the primary loops were gathered and compiled from thirty six cycles in operating CPR1000 PWR units. The statistical results of these operation data show that the higher volume activities of radio-iodine in the primary loops may result from the dissemination of actinides due to secondary hydriding from the previous cycles and the 131I/133I can be used to well identify the fuel reliability in comparison of the radio-iodine activities. In the cycles with fuel failure, the radio-iodine activities and 131I/133I according to the operation data distribute much wider than the expected because the actual status of fuel failure are more complicated during operation than expected, e.g. changes of fuel failure size, the discrepancy of the location of fuel failure and the relative power of the defective fuel rods. In some cases, it is difficult to distinguish the fuel failure by radio-iodine owing to its low release rate from defective fuel rods. In addition, the size of fuel failure in the specific cycles can be illustrated by the significant change of 131I/133I. It shows that the predictions by the model and the statistical results in operating CPR1000 PWR units are qualitatively in agreement for both intact fuel and fuel failure. It also indicates that the conventional threshold 131I/133I≥0.1 for fuel failure may make the misjudgment due to the overlap of distributions and 131I/133I≥0.15 can distinguish 97% operation date for the fuel failure and 98% operation date for intact fuel rods. The method used and the results in the paper can help to make better understanding of the release of iodine for fuel failure and identity the defective fuel rods during unit operation.

The Colletotrichum musae and Fusarium semitectum Berk., Which often occur in banana after harvest were used as the targets. Three components with antibacterial activity extracted from the leaves of the plant were used as "fungicide". The antibacterial effect of the three component substances on the pathogens of anthracnose and shaft rot was studied. The results showed that the inhibitory rates of the components A, B and C at 2 mg / ml on the pathogens of banana anthracnose and shaft rot reached 82.53%, 79.91%, 78.30% and 82.2%, 78.69% and 81.17% respectively. The EC90 values of component A, B and C for banana anthracnose pathogens were 1.70, 1.85, and 1.69 mg / ml. Respectively, the EC90 values for banana shaft rot pathogens were 1.37, 1.38, and 1.64 mg / ml. The antibacterial effect of the three component substances on the pathogen of banana shaft rot was better than that of anthracnose. The three component substances have great potential for controlling post-harvest anthracnose and shaft rot of bananas.

In recent years, the amount of Internet information data has exploded, and the problem of "information overload" has become a huge challenge for Internet development. Through the prior knowledge of the known data distribution, combined with sample training data to estimate the mathematical model of the overall data. Intelligent recommendation methods based on knowledge graphs and Bayesian networks are a hot spot in the current Internet research, and they are of great significance to search engines and e-commerce. This paper studies the fusion of knowledge graph and Bayesian belief network and its application in personalized recommendation and designs a Bayesian network that describes the purchase behavior of users based on the purchase records of customers across the network. User recommendation is the process of fusing knowledge graphs and Bayesian network reasoning. For the design of the recommendation network, a random reasoning algorithm with approximate reasoning is selected in consideration of calculation scale and accuracy. This paper uses the content filtering technology of KNN algorithm to supplement the sparse matrix, which can improve the quality of collaborative filtering recommendation, thereby improving the quality of final recommendation. In this paper, based on the knowledge graph, the EPCA algorithm and the robust recommendation algorithm are merged to form a robust collaborative recommendation model based on Bayesian probability matrix decomposition. The robust recommendation algorithm further improves the robustness of the recommendation algorithm by introducing the suspected users and target items detected by the EPCA algorithm. Research shows that the PS value of the VarSelect SVD algorithm used in this paper is approximately between 0.36 and 1.04. Compared with the first two algorithms, the PS value of VarSelect SVD has been significantly reduced, and the algorithm robustness has been significantly improved. [ABSTRACT FROM AUTHOR]