We investigated patterns of variation in alkamides and cichoric acid accumulation in the roots and aboveground parts of Echinacea purpurea (L.) Moench. These phytochemicals were extracted from fresh plant parts with 60% ethanol and quantified by high performance liquid chromatography (HPLC) analysis. Concentrations of alkamides and cichoric acid were measured on a dry-weight basis (mg·g–1). For total alkamides, concentrations among individual plants varied from 5.02 to 27.67 (mean = 14.4%) in roots, from 0.62 to 3.42 (mean = 1.54) in nearly matured seed heads (NMSH), and 0.22 to 5.25 (mean = 0.77) in young tops (about ½ flower heads, ¼ leaves, and ¼ stems). For cichoric acid, concentrations among individual plants varied from 2.65 to 37.52 (mean = 8.95), from 2.03 to 31.58 (mean = 10.9), and from 4.79 to 38.55 (mean = 18.88) in the roots, the NMSH, and the tops, respectively. Dodeca-2E, 4E, 8Z, 10E-tetraenoic acid isobutylamide and dodeca-2E, 4E, 8Z, 10Z-tetraenoic acid isobutylamide (alkamides 8/9) accounted for only 9.5% of the total alkamides in roots, but comprised 87.9% in the NMSH, and 76.6% in the young tops. Correlations of concentrations of alkamides or cichoric acid between those of roots and those of the NMSH were not statistically significant, and either within the roots, the NMSH, and the young tops. However, a significant negative correlation was observed between the concentration of cichoric acid in the roots and in young tops, and a significant positive correlation was observed between total alkamide concentration in the roots and cichoric acid concentration in the young tops. These results may be useful in the genetic improvement of E. purpurea for medicinal use.

Short-term load forecasting plays a vital role in the daily operational management of power utility. To improve the forecasting accuracy, this paper proposes a hybrid EMD-PSO-SVR forecasting model for short-term load forecasting based on empirical mode decomposition (EMD), support vector regression (SVR), and particle swarm optimization (PSO), also considering the effects of temperature, weekends, and holidays. EMD is used to decompose the residential electric load data into a number of intrinsic mode function (IMF) components and one residue; then SVR is constructed to forecast these IMFs and residual value individually. In order to gain optimization parameters of SVR, PSO is implemented to automatically perform the parameter selection in SVR modeling. Then all of these forecasting values are reconstructed to produce the final forecasting result for residential electric load data. Compared with the results from the EMD-SVR model, traditional SVR model, and PSO-SVR model, the result indicates that the proposed EMD-PSO-SVR model performs more effectively and more stably in forecasting the residential short-term load.

Purification and characterization of a chymosin from Rhizopus microsporus var. rhizopodiformis were investigated in the present study. A newly isolated R. microsporus var. rhizopodiformis F518 produced a high level of milk-clotting activity (1,001 SU/mL). A chymosin from the fungus was purified 3.66-fold with a recovery yield of 33.2 %. The enzyme appeared as a single protein band on sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) with a molecular mass of 37.0 kDa. It was optimally active at 60 °C and was stable up to 40 °C. The purified enzyme was an acid protease with an optimum pH of 5.2 and retained 80 % of residual activity within pH 2.0-8.0. The inhibition of 96 and 100 % by pepstatin A at 0.01 and 0.02 mM, respectively, revealed that the enzyme is an aspartic protease. Thus, high milk-clotting activity of the chymosin with good stability will strengthen the potential use of the chymosin as a substitute for calf rennet in cheese manufacturing.

The junction temperature at the fundamental frequency cannot be ignored in a lifetime evaluation of insulated-gate bipolar transistors (IGBTs) with a long-term mission profile. Therefore, it is very important in terms of calculation speed and accuracy to simplify the loss curve when calculating the thermal fluctuation at the fundamental frequency. This paper proposes a mathematical analysis method for the junction temperature fluctuation at the fundamental frequency based on an equivalent sinusoidal halfwave loss. The dynamic and static parameters of the devices are tested by an experimental platform, and accurate device loss models are established through a case study of a 1.5 MW direct-drive wind turbine gridconnected model. The accuracy of the proposed calculation model is compared with that of a time-domain simulation and the two-step loss pulse method. Considering different output frequencies, the accuracy of the proposed method is further discussed. Based on actual wind speed data, the proposed method is used to calculate the junction temperature of an IGBT module in a grid-side converter. The results show that the proposed method can improve the accuracy of the reliability evaluation of wind power converters.INDEX TERMS Junction temperature fluctuation, equivalent sinusoidal half-wave loss, fundamental frequency, reliability, mission profile.

Summary This study employs an undesirable‐output–oriented data envelopment analysis model to measure the carbon emission performance of the power industry throughout China's 30 administrative regions during the period of 2003–2012. Also, it further studies the regional disparity and spatial correlation of the carbon emission performance of China's power industry. The main findings are as follows: (1) The carbon emission performance of China's power industry is at a relatively low level, but shows a rising trend. (2) The regional carbon emission performance of China's power industry is extremely unbalanced: The eastern area ranks first, with the highest performance of 0.851, followed by the central area, whereas the western area falls behind, with the lowest performance of 0.760. Provinces in the eastern area generally perform better than those in other areas. (3) According to spatial analysis, the global Moran's I values of carbon emission performance are significantly positive during the sample period, which indicates that the carbon emission performance is a positive spatial correlation and has an obvious clustering effect. The estimate of the local spatial autocorrelation index confirms the imbalance of spatial distribution of the power sector's carbon emission performance. Based on the above findings, several policy suggestions are presented in this article.