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• In male figs agaonid wasps deposit a single pollen grain each time they oviposit. • Stigma shape and pollen transfer to the hypopygium ensure very precise pollination. • In female figs wasp behaviour is less precise than in male figs. • In female figs exposed stigmatic papillae and synstigma ensure efficient seed set. • Plant morphology stabilises the mutualism. Active pollination has evolved four times in brood site pollination mutualisms. When pollination is active, the pollinator's behaviour specifically evolves to ensure flower fertilisation. In functionally dioecious Ficus species, the male figs host pollinating wasp larvae, while wasps cannot breed in the female figs because the styles are longer than the wasp ovipositor. Here we investigate the dioecious Ficus hispida. We show that in male figs, every time the wasp has laid an egg into a pistillate flower, it removes one pollen grain from its pollen pockets and places it on the hypopygium. When the wasp inserts its ovipositor into the next flower, the pollen grain is deposited deep within the style. Each ovipositor insertion results in flower pollination and insect oviposition. Because of systematic pollination of the flowers into which the eggs are laid, the wasp larvae benefit from feeding on fertilised endosperm while no seed are produced. In female figs, after probing a flower, the wasp presents pollination behaviour only every five visits. However, if it does occur, this behaviour lasts longer than in male figs and results in the deposition of on average 10 pollen grains on the hypopygium. The exposed sticky papillae on the stigmatic surface collect pollen from the hypopygium and pollen tubes may grow to neighbouring stigmas, ensuring secondary dispersal and efficient ovule fertilisation. Overall, our study demonstrates that the floral morphology of male figs facilitates precise pollen deposition, beneficial for the wasp progeny, while the floral morphology of female figs compensates for wasp pollination behaviour that is not selected in those figs. We conclude that the morphology of the arena in which interactions with its pollinator are played out is the result of selection on the plant to maximize its male and female fitness. Incidentally, this morphology stabilises the mutualistic interaction. [ABSTRACT FROM AUTHOR]

Abstract: In this study, a Geo1 Geo2/Geo1, Geo2\s\s+K queue system with two classes of customers and priorities is considered. The first class of customers are with delay and loss, and the second class of customers are with loss. The second class of customers have preemptive resume priority to the first class of customers. Using the method of matrix analysis, the mean queue length, the loss probability and the system utility for the two classes of customers are derived. Finally, some numerical results are presented by Matlab program. [Copyright &y& Elsevier]

Ship motion prediction is applied to the shipboard stabilized platform to keep the equipment on the platform stable all the time, which is of great practical significance to the safety and efficiency of shipboard equipment operation. Long Short-term Memory (LSTM) Network is a classic time series prediction method that has made remarkable achievements in this field. However, the dynamic frequency range of single LSTM in ship motion prediction is insufficient to meet the stabilized platform with higher precision requirements. To improve the performance of LSTM in ship motion prediction, this paper presents a novel method named as multiscale attention-based LSTM. At first, wavelet transform is employed to decompose ship motion signals into several frequency scales, which makes LSTM to capture the inherent law of ship motion from each frequency scale. And then the weights of different scales are obtained by attention mechanism, which promote the sensitivity of the whole system by paying more attention to significant information and suppress the interference of noise signals. Both of the steps form a multiscale attention mechanism, which promote the adaptability and improve the performance of the LSTM. In addition, to avoid being trapped in local optimization, the two-stage training mechanism is designed for model training based on the model structure. Ship motion data are used to evaluate the feasibility and effectiveness. The experiments show that the proposed method achieves better performance compared with other popular methods. • A multiscale attention-based LSTM architecture is proposed according to the characteristics of ship motion signals. • The importance of different frequency scales is adjusted by the attention mechanism to adapt to different ocean conditions. • A two-stage training mechanism is designed for model training according to the proposed model structure. • The feasibility and effectiveness of the proposed model are verified by the experiments under different ocean conditions. [ABSTRACT FROM AUTHOR]