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Seed Germination Requirements of Ficus virens (Moraceae) as Adaptation to Its Hemi-Epiphyte Life Form

Ji M., Qin H., Chen H., Wen B.

Polish Journal of Ecology

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2018

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Vol. 66, nr 1

14--22

EN

Epiphytes and hemi-epiphytes are important floristic, structural and functional components of tropical rainforests. Their specific responses to light, temperature and water conditions during seed germination allow them to coexist with tropical forest trees. Here we investigated the effects of temperature, red to far-red light ratio (R:FR ratio) and water stress on seed germination of Ficus virens in tropical seasonal rainforest in Southwest China. We used incubators to create required temperature regimes, polyester filters to produce R:FR ratio gradients and mannitol solutions to simulate water stress. It was found that seed germination of F. virens was inhibited in the simulated understory conditions, i.e., at lower temperature (22/23°C), especially when combined with the R:FR ratio of 0.25, for which the germination percentage was less than 20%. In contrast, the seed germination percentages in the simulated canopy environment (22/32°C) showed no significant difference between R:FR ratios, with an average seed germination percentage as high as 65.8%. Seed germination delayed and decreased along with increasing water stress and was completely inhibited at -2.5 MPa, which might suggest that it is a kind of adaptation for F. virens seeds to detect the rainy season as germination chance on the canopy. Therefore, our study revealed the physiological mechanism for F. virens to be able to adapt to canopy environment.

2

DFIS: A novel data filling approach for an incomplete soft set

Qin H., Ma X., Herawan T., Zain J. M.

International Journal of Applied Mathematics and Computer Science

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2012

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Vol. 22, no. 4

817-828

EN

The research on incomplete soft sets is an integral part of the research on soft sets and has been initiated recently. However, the existing approach for dealing with incomplete soft sets is only applicable to decision making and has low forecasting accuracy. In order to solve these problems, in this paper we propose a novel data filling approach for incomplete soft sets. The missing data are filled in terms of the association degree between the parameters when a stronger association exists between the parameters or in terms of the distribution of other available objects when no stronger association exists between the parameters. Data filling converts an incomplete soft set into a complete soft set, which makes the soft set applicable not only to decision making but also to other areas. The comparison results elaborated between the two approaches through UCI benchmark datasets illustrate that our approach outperforms the existing one with respect to the forecasting accuracy.

3

P-type iterative learning control of nonlinear systems with uncertainties

Chen P., Qin H., Ye X.

Systems Science

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2004

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Vol. 30, no 3

77-84

EN

This paper deals with the problem of robustness of P-type iterative learning control for uncertain nonlinear system. Besides the vector field, the control matrix and output matrix of the control system considered in this paper all have uncertainties. Iterative learning laws for initial states and for inputs are presented. A new technique has been developed to estimate the tracking errors of iterative control systems, which have an initial state error. Based on the estimation, upper bounds of the norms of the uncertainties of the control matrix and the output matrix are obtained, which guarantee that the iterative learning laws for initial states and for inputs are convergent. The results in this paper show that the P-type iterative learning control has robustness with respect to the uncertainties of the control matrix and the output matrix.