The small brown planthopper, Laodelphax striatellus (Fallén) enters the photoperiodic induction of diapause as 3rd or 4th instar nymphs. The photoperiodic response curves in this planthopper showed a typical long-day response type with a critical daylength of approximately 11 h at 25°C, 12 h at 22 and 20°C and 12.5 h at 18°C, and diapause induction was almost abrogated at 28°C. The third stage was the most sensitive stage to photoperiod. The photoperiodic response curve at 20°C showed a gradual decline in diapause incidence in ultra-long nights, and continuous darkness resulted in 100% development. The required number of days for a 50% response was distinctly different between the short- and long-night cycles, showing that the effect of one short night was equivalent to the effect of three long nights at 18°C. The rearing day length of 12 h evoked a weaker intensity of diapause than did 10 and 11 h. The duration of diapause was significantly longer under the short daylength of 11 h than it was under the long daylength of 15 h. The optimal temperature for diapause termination was 26 and 28°C. Chilling at 5°C for different times did not shorten the duration of diapause but significantly lengthened it when chilling period was included. In autumn, 50% of the nymphs that hatched from late September to mid-October entered diapause in response to temperatures below 20°C. The critical daylength in the field was between 12 h 10 min and 12 h 32 min (including twilight), which was nearly identical to the critical daylength of 12.5 h at 18°C. In spring, overwintering nymphs began to emerge in early March-late March when the mean daily temperature rose to 10°C or higher.

Temperature has a significant influence on the development of Laodelphax striatellus (Fallén), an important rice pest insect in east Asia. We set eight constant temperatures from 18 to 32 degrees C in 2 degrees C-increments to check the effect of temperature on the developmental rate of this insect species. The developmental durations of eggs and nymphs were observed daily. To ensure the accuracy of developmental durations, 500 initial samples were taken for the nymphal stage at each temperature. Performance-2 model was used to fit these data because this model can provide the lower and upper developmental thresholds simultaneously. The estimate of lower developmental thresholds of eggs (10.0 degrees C) was different from that of nymphs (7.5 degrees C). And the estimate of upper developmental thresholds of eggs (35.5 degrees C) was also different from that of nymphs (30.2 degrees C). However, for male and female nymphs, the difference in the lower developmental threshold is nonsignificant, and the difference in the upper developmental thresholds is very small (95% confidence interval of the difference: [0.007 degrees C, 0.043 degrees C]). The rate isomorphy hypothesis considers that the lower developmental thresholds of different stages for the same insect might be constant. However, the current study provides a counterexample of this hypothesis that the lower developmental threshold of eggs is different from that of nymphs. Thus, we demonstrate that the rate isomorphy hypothesis does not apply all insects. In addition, we used a popular nonlinear model, Lactin model, to fit the developmental rate data of our experiment. And we found that the estimates of lower and upper developmental thresholds by using Performance-2 model were very approximate to those by using Lactin model. The current study provides reliable estimates of thermal parameters for L. striatellus by using large experimental samples at different temperatures. It would be useful for exploring the relationship of climate change and the outbreak of this insect on rice.

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In the fault diagnosis of steam turbine, multiple faults contain number of uncertainties due to the complexity of fault diagnosis knowledge and the strong correlation and coupling between different faults and massive prior knowledge is needed. In this paper, a fault diagnosis method of steam turbine based on Bayesian network is proposed. Various faults and symptoms are parameterized using Bayesian network nodes. Then rough set theory is used to simplify the complex causal relationship between faults and symptoms, and Noisy-Or model is used to simplify the conditional probability parameters. A diagnostic model of the steam turbine was developed using a simplified Bayesian network. The experiment results are shown to validate the practicality and effectiveness of the proposed designs.