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ObjectiveTo analyze the monitoring results of foodborne diseases in Liaoning Province, so as to provide scientific basis for prevention and control.MethodsThe case of foodborne disease from foodborne disease surveillance and report system in Liaoning Province in 2014-2019 were collected. The stool samples of patients were collected for pathogen detection, and the prevalence status and causes of foodborne diseases were analyzed.ResultsIn Liaoning Province, the high risk group was the age of 25-44 years group (33.38%, 57 151/171 219). Cases were mainly in summer (65.35%, 111 896 /171 219). The detection rate of Vibrio parahemolyticus (4.37%, 867/19 845) was higher than the other pathogenic bacteria. Aquatic animals(19.86%, 33 002/166 211), fruits (14.58%, 24 239/ 166 211) and meat (12.59%, 20 929/166 211) were the main suspected exposure foods. The most risky packaging was bulk food (41.79%, 70 941/169 743) and family was the most frequent place(70.65%, 119 517/169 157). The main clinical symptoms were digestive system symptoms (98.55%, 168 735/171 219) and diarrhea.ConclusionLiaoning Province should further improve the monitoring and reporting network of foodborne diseases, strengthen the surveillance of foodborne diseases among high-risk group, focus on the safety of seafood consumption, especially in summer, when making bulk seafood in families, and increase food safety and health education to avoid foodborne diseases.

ObjectiveTo analyze the incidence and epidemiological characteristics of Vibrio parahaemolyticus infection in Liaoning Province, and to provide scientific basis for prevention and control measures.MethodsThe case information of Vibrio parahaemolyticus infection in Liaoning from 2014 to 2019 was collected, and the stool samples of patients were collected for detection. SPSS 17.0 software was used for statistical analysis. The Mantel-Haenszel was used to calculate the OR and 95% CI of different sex, age, occupation and regions.ResultsDetection rate of Vibrio parahaemolyticus was 4.37% from 2014 to 2019 in Liaoning Province. There were significant differences in the patients of Vibrio parahaemolyticus among different regions (5.59% and 2.86%, χ2= 87.627, P

In order to improve the efficiency and adaptability of cognitive radar jamming decision-making, a fusion algorithm (Ant-QL) based on ant colony and Q-Learning is proposed in this paper. The algorithm does not rely on a priori information and enhances adaptability through real-time interactions between the jammer and the target radar. At the same time, it can be applied to single jammer and multiple jammer countermeasure scenarios with high jamming effects. First, traditional Q-Learning and DQN algorithms are discussed, and a radar jamming decision-making model is built for the simulation verification of each algorithm. Then, an improved Q-Learning algorithm is proposed to address the shortcomings of both algorithms. By introducing the pheromone mechanism of ant colony algorithms in Q-Learning and using the ε -greedy algorithm to balance the contradictory relationship between exploration and exploitation, the algorithm greatly avoids falling into a local optimum, thus accelerating the convergence speed of the algorithm with good stability and robustness in the convergence process. In order to better adapt to the cluster countermeasure environment in future battlefields, the algorithm and model are extended to cluster cooperative jamming decision-making. We map each jammer in the cluster to an intelligent ant searching for the optimal path, and multiple jammers interact with each other to obtain information. During the process of confrontation, the method greatly improves the convergence speed and stability and reduces the need for hardware and power resources of the jammer. Assuming that the number of jammers is three, the experimental simulation results of the convergence speed of the Ant-QL algorithm improve by 85.4%, 80.56% and 72% compared with the Q-Learning, DQN and improved Q-Learning algorithms, respectively. During the convergence process, the Ant-QL algorithm is very stable and efficient, and the algorithm complexity is low. After the algorithms converge, the average response times of the four algorithms are 6.99 × 10−4 s, 2.234 × 10−3 s, 2.21 × 10−4 s and 1.7 × 10−4 s, respectively. The results show that the improved Q-Learning algorithm and Ant-QL algorithm also have more advantages in terms of average response time after convergence. [ABSTRACT FROM AUTHOR]