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The purpose of the experiment was to investigate the performance of the European meat pigeon Ⅱ line and the European meat pigeon line Ⅲ (O Ⅱ ×O Ⅲ) in terms of growth, slaughter, and meat quality performance. In the experiment, forty 0-day-old O Ⅱ ×O Ⅲ hybrid pigeons were selected to determine body weight, body size, slaughter performance, and meat quality (half in male and female), and correlation analysis was carried out. The experimental period was 28 days. The results showed that the population homogeneity of 28-day hybrid pigeons was relatively good. The coefficients of variation of body length, keel length, tibia circumference, tibia length, chest width, and chest depth were all below 7% except for body weight, which was 9.93%. The leg muscle weight and leg muscle rate of the male hybrid pigeons were significantly higher than those of female pigeons (P<0.05). The weight of the hybrid pigeons reached a significant or extremely significant positive correlation level with eight indexes, including body length, shin circumference, shin length, and slaughter weight (P<0.05 or P<0.01). There was no significant difference in meat quality between male and female pigeons at 28 days, but the meat quality of male pigeons was better. The results show that although the 28 day-hybrid pigeon does not completely surpass the parent in terms of growth, slaughter, and meat quality performance, its group uniformity and meat production performance are good, which can provide some references for the breeding and production of high-quality meat pigeons. [ABSTRACT FROM AUTHOR]

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In order to improve the accuracy of the community detection in complex networks, this paper proposed a multiobjective community detection based on adaptive memetic algorithm. In global search, the algorithm applied the Logistic function to set the corresponding crossover probability and mutation probability. and turned the multi-objective optimization problem into minimal optimization of two objectives called kernel K-means( KKM) and ratio cut( RC) at the same time. In local search. it constituted the local optimization target of weights of two objective functions and used a hill-climbing strategy to find the best individual. Experiments on synthetic and real life networks show that, compared with five algorithms based on GAs( genetic algorithms) and Fast Modularity algorithm,the proposed algorithm can effectively improve the accuracy of the community detection and has certain advantages in solving community detection problems in complex networks. [ABSTRACT FROM AUTHOR]