Search

Little information exists on the gonadal development and maturity scale of Octopus ocellatus, an economically important octopus species commonly found in the west Pacific Ocean. A total of 171 O. ocellatus samples were collected monthly off the coast of northeast China (Qingdao, Shandong) from December 2011 to November 2012. Total body weight, mantle length, gonadosomatic index, and reproductive tract parameters of the samples were analyzed in males and females. The process of gonadal development in O. ocellatus was defined based on gonadal histological examination. Nine stages of oocyte development were identified during oogenesis, and a maturity scale of five stages (immature, developing, maturing, mature, and spent) was proposed for both ovary and testis. Additionally, the maturation status and dominant germ cell types of O. ocellatus in relation to monthly variation were identified. The seasonal changes showed that O. ocellatus off the coast of Qingdao is a fast-growing species with a short life span, which reproduces only once in a lifetime and then dies. Its reproductive cycle can be divided into four periods, beginning with the breeding period from April. Males become sexually mature in a smaller size than females, indicating that the mating behavior occurs early with sperm stored in the oviductal glands of the females before fertilization and spawning. This work contributes to the understanding of the maturation process in O. ocellatus, laying a foundation for further study on the mechanism of sperm storage in female octopuses and providing a reference for artificial breeding and fisheries impact assessment of this species.

With the rapid development of Cloud computing, data mining and ANN technology, we try to combined cloud data mining and ANN technologies, Using the Capacity of these technologies such as data acquisition, data storage and analyzing large amounts of complicated non-1inear data, To construct a forecasting model for massive data real-time accurately which is difficult to complete on the traditional mode. This new model utilizes an enterprise sales data to test. The result proves that the model is feasible and effective. According to this model’s prediction result, we can provide scientific and real-time decision support for the enterprise management.