TEPS用EPS基板工艺参数与性能关系试验研究

为了研究工艺参数对可发性聚苯乙烯(expandable polystyrene,EPS)基板热学力学性能的影响,以蒸汽压力和一次发泡堆积密度作为工艺控制参数,通过导热系数、压缩强度试验和扫描电子显微镜(scanning electron microscope,SEM)分析研究EPS基板的热学力学性能及形成机理。结果表明:蒸汽压力上升,导热系数先降后升,压缩强度降低,蒸汽压力为0.08 MPa时,导热系数最低为0.0426 W/(m·K);一次发泡堆积密度增大,导热系数先降后升,压缩强度先升后降,一次发泡堆积密度为14.94 kg/m^(3)时,压缩强度最大为79 kPa。一次发泡堆积密度相同时,孔径增大,导热系数升高,升幅为3.05%;孔径相同时,孔分布越窄、越合理,导热系数越低。壁厚越薄,压缩强度越小,降幅为17.72%。

Time Series Models for Short Term Prediction of the Incidence of Japanese Encephalitis in Xianyang City, P R China

Objective To construct a model of Seasonal Autoregressive Integrated Moving Average （SARIMA） for forecasting the epidemic of Japanese encephalitis （JE） in Xianyang, Shaanxi, China, and provide valuable reference information for JE control and prevention. Methods Theoretically epidemiologic study was employed in the research process. Monthly incidence data on JE for the period from Jan 2005 to Sep 2014 were obtained from a passive surveillance system at the Center for Diseases Prevention and Control in Xianyang, Shaanxi province. An optimal SARIMA model was developed for JE incidence from 2005 to 2013 with the Box and Jenkins approach. This SARIMA model could predict JE incidence for the year 2014 and 2015. Results SARIMA （1, 1, 1） （2, 1, 1）12 was considered to be the best model with the lowest Bayesian information criterion, Akaike information criterion, Mean Absolute Error values, the highest R2, and a lower Mean Absolute Percent Error. SARIMA （1, 1, 1） （2, 1, 1）12 was stationary and accurate for predicting JE incidence in Xianyang. The predicted incidence, around 0.3/100 000 from June to August in 2014 with low errors, was higher compared with the actual incidence. Therefore, SARIMA （1, 1, 1） （2, 1, 1）12 appeared to be reliable and accurate and could be applied to incidence prediction. Conclusions The proposed prediction model could provide clues to early identification of the JE incidence that is increased abnormally （≥0.4/100 000）. According to the predicted results in 2014, the JE incidence in Xianyang will decline slightly and reach its peak from June to August.The authors wish to thank the staff from the CDCs from 13 counties of Xianyang, Shaanxi province, China, for their contribution to Japanese encephalitis cases reporting.

Variation in the emission rate of sounds in a captive group of false killer whales Pseudorca crassidens during feedings: possible food anticipatory vocal activity?

This study examines whether a group of captive false killer whales(P seudorca crassidens) showed variations in the vocal rate around feeding times. The high level of motivation to express appetitive behaviors in captive animals may lead them to respond with changes of the behavioral activities during the time prior to food deliveries which are referred to as food anticipatory activity. False killer whales at Qingdao Polar Ocean World(Qingdao, China) showed signifi cant variations of the rates of both the total sounds and sound classes(whistles, clicks, and burst pulses) around feedings. Precisely, from the Transition interval that recorded the lowest vocalization rate(3.40 s/m/d), the whales increased their acoustic emissions upon trainers' arrival(13.08 s/m/d). The high rate was maintained or intensifi ed throughout the food delivery(25.12 s/m/d), and then reduced immediately after the animals were fed(9.91 s/m/d). These changes in the false killer whales sound production rates around feeding times supports the hypothesis of the presence of a food anticipatory vocal activity. Although sound rates may not give detailed information regarding referential aspects of the animal communication it might still shed light about the arousal levels of the individuals during different social or environmental conditions. Further experiments should be performed to assess if variations of the time of feeding routines may affect the vocal activity of cetaceans in captivity as well as their welfare.

Analysis on Application of Wavelet Neural Network in Wind Electricity Power Prediction

Wind electricity power has fluctuation, and accurate and reasonable wind electricity power prediction is very important for solving wind electricity network and combination. This paper takes an analysis of a lot of actual data of a certain wind electricity field. Through wavelet neural network and time series method rolling, it can predict the overall power of wind electricity field. The result shows that for the original data of sampling time length and large sampling frequency, the model constructed by this paper has very good prediction effect. Because of the fan installation position, wind electricity fan flow effect and other random factor influence, wind electricity field overall power and single unit power distribution have difference. Through comparing with the time series parameters, it puts forward that single wind electricity unit power has smooth effect for overall power of wind electricity field. Finally, it summarizes the prediction effect and puts forward some reasonable suzestions for wind electricity network troblems.