Simultaneous Effects of Slip Conditions and Wall Properties on MHD Peristaltic Flow of a Maxwell Fluid with Heat Transfer
The effects of both wall slip conditions and heat transfer on the magnetohydrodynamics (MHD) peristaltic flow of a Maxwell fluid in a porous planar channel with elastic wall properties have been studied. Mathematical formulation is based upon the modified Darcy’s law. The analytical solution has been derived for the stream function and temperature under the assumptions of small wave number.The results obtained in the analysis have been discussed numerically and explained graphically.
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Japaneses encephalitis (JE) is most common zoonoses caused by Japanese encephalitis virus (JEV) with a high mortality and disability rate. To take timely preventive and control measures, early and rapid detection of JE RNA is necessary. But due to characteristic brief and low viraemia, JE RNA detection remains challenging. In this study, a real-time nucleic acid sequence-based amplification (RT-NASBA) was developed for rapid and simultaneous detection of JEV. Four pairs of primer were designed using a multiple genome alignment of all JEV strains from GenBank. NASBA assay established and optimal reaction conditions were confirmed by using primers and probe on ns1 gene of JEV. The specificity and sensitivity of the assay were compared with RT-PCR by using serial RNA and virus cultivation dilutions. The results showed that JEV RT-NASBA assay was established, and robust signals could be observed in 10 min with high specificity. The limit of dectetion of RT-NASBA was 6 copies per reaction. The assay was thus 100 to 1, 000 times more sensitive than RT-PCR. The cross-reaction was performed with other porcine pathogens, and negative amplification results indicated the high specificity of this method. The novel JEV RT-NASBA assay could be used as an efficient molecular biology tool to diagnose JEV, which would facilitate the surveillance of reproductive failure disease in swine and would be beneficial for public health security.
A Short-Term and High-Resolution Distribution System Load Forecasting Approach Using Support Vector Regression With Hybrid Parameters Optimization
This paper proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of the hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system. The performance of the proposed approach is compared to some classic methods in later sections of this paper.