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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]

In previous studies oncolytic measles viruses (MVs) have shown significant antitumor activity against various tumors. In our research recombinant MV-Hu191 (rMV-Hu191), established via reverse genetics technology and expressing enhanced green fluorescent protein (EGFP), was evaluated for its therapeutic effects and related mechanisms against nephroblastoma cell lines. We built three different constructs based on rMV-Hu191 to express EGFP effectively. Our experiments showed that rMV-Hu191 expressing EGFP could efficiently infect and replicate in nephroblastoma cell lines. Caspase-induced apoptosis exerted a significant impact on MV-induced cell death, which was accompanied by emission of cellular ATP and high-mobility group protein 1 (HMGB1) and by translocation of calreticulin (CRT). Intratumoral injection of rMV-Hu191-EGFP resulted in significant regression of tumors in a G401 xenograft model. Our results indicate that the MV-Hu191 strain, which is widely used in China, is an appropriate vector for expression of foreign genes and could serve as a potentially good candidate for nephroblastoma therapy mediated by induction of apoptosis-associated immunogenic cell death (ICD)., Competing Interests: None., (AJTR Copyright © 2021.)