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The rough K-means clustering algorithm has strong ability to deal with data with uncertain boundaries. However, this algorithm also has limitations such as sensitivity to the selection of initial clustering centers, and use of fixed weights and thresholds resulting in unstable clustering results and decreased accuracy. A lot of research has been devoted to solving these problems from different angles. With introduction of artificial bee colony (ABC) algorithm, the algorithm is improved from three aspects. Firstly, based on the ratio of the number of objects in lower approximate set and the boundary set to the product of the difference of the objects in the dataset, a more reasonable method of dynamically adjusting the weights of approximation and boundary set is designed. Secondly, in order to speed up the convergence speed of the algorithm, an implementation method of adaptive threshold ε associated with the number of iterations is given. Thirdly, by constructing the fitness function of the nectar source location, the bee colony is guided to search for high-quality nectar sources globally. The best position of honey source obtained by each iteration is taken as the initial cluster center, and the cluster is carried out on the basis of this. Experimental results show that the improved algorithm improves the stability of the clustering results and obtains better clustering effect.

Copyright of Journal of Henan University of Science & Technology, Natural Science is the property of Editorial Office of Journal of Henan University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Orchidaceae, widely distributed in various terrestrial ecosystems except for Antarctica, Arctic and extreme desert areas, comprises more than 28 000 species in more than 800 genera of 5 subfamilies. As one of the hotspots of orchid biodiversity in the world, Southeast Asia accounted for about 1/3 of all orchid species. In this paper, we reviewed the species diversity, evolution and dispersal history, as well as classified the habits and pollination systems of orchid species in Southeast Asia. The results are as follows:(1)A total of 8 855 orchid species, which belongs to 5 subfamilies, 17 tribes, 26 subtribes and 240 genera in Southeast Asia, were enumerated and evaluated.(2)The main habits of orchids in Southeast Asia include more than 6 000 epiphytic species of 127 genera, more than 2 000 terrestrial species of 97 genera, about 100 saprophytic species of 13 genera and more than 40 vine species of 4 genera.(3)According to the pollination system of 79 genera of Orchidaceae in Southeast Asia, 44 genera contained automatic self-pollination species. Rewarding pollination systems involve the forms of pollen(only found in subfamily Apostasioideae), fragrance oils(only found in subfamily Vanilloideae)and nectar(found in all five subfamilies)as rewards. Additionally, deceptive pollination systems exist widely in all five subfamilies, including food-deceptive system, sexual mimicry, shelter mimicry, oviposition-site mimicry and pheromone mimicry. Orchidaceae in Southeast Asia show a high diversity of species, habits and pollination systems. The summary of orchid biological characteristics provides some theoretical foundations and context information for the conservation. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao is the property of Chinese Journal of Applied Ecology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Food Research & Development is the property of Food Research & Development Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper studied the pricing problem of crowdsourcing model from the perspective of participants. which included enterprises, crowdsourcing platforms and members. On the basis of considering membership credibility and task aggregation degree, it designed an allocation pricing scheme for crowdsourcing tasks with geographical factor. Guided by service cost, task value, member income, it constructed a multi objective programming model by combining different tasks, and designed the elitism bee colony algorithm for the model. In this algorithm, through the use of nectar source information and considering the improvement of bees, it significantly improved the search efficiency. Then analyzing E-commerce enterprise operation data, this paper got crowdsourcing membership characteristics, task completion rate and other basic information, and carried out the simulation experiment. Simulation results show that the task packaging pricing strategy has significant advantages in task completion rate, enterprise total cost, and the three parts total revenue. Through the model and data experiments, it can be seen that crowdsourcing has its own characteristics in pricing and publishing. For nondifferentiated services, it only needs to consider the reputation of members, for differentiated services, the task assignment needs to be packaged. [ABSTRACT FROM AUTHOR]

Tilia amurensis is one of (co-)dominant species in the broadleaved-conifer mixed forest in Northeast China, with high commercial and nectariferous values. We estimated the quantity of nectar secretion from individual trees to population or stand levels based on observation and statistical analysis. An equation for individual-tree nectar secretion was established, which was used to estimate nectar quantity at the stand level. We analyzed the relationships between nectar secretion and basal area or stem volume. The booming time for single flower was in average 6-8 days, with a nectar secretion period of about five days. The quantity for the entire period was estimated at 8.58 mg per flower. Sugar contents in the nectar, average 37.7%, showed diurnal variations, being high in the mid-noon and low in the early morning and late afternoon. The average diameter (DBH) of the species was approximately 40 cm, which was estimated to possess as much as 18×10 4 single flowers and 1.56 kg (or pure sugar 0.588 kg) of nectar. At the stand level, the nectar production potential was 79-147 kg (or 0.0686-0.1285 m 3 , pure sugar 29.78-55.42 kg) per hectare. There was a close correlation between nectar quantity and basal area or timber volume at both individual and stand levels, which could be used to estimate the nectar quantity for macro-scale forest area based on inventory data.