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Yao-zhong Ding,Jian-hua Zhou,Li-na Ma,Yan-ni Qi,Gang Wei,Jie Zhang,Yong-guang Zhang 대한수의학회 2014 Journal of Veterinary Science Vol.15 No.3
A reverse transcription loop-mediated isothermalamplification (RT-LAMP) assay was developed to rapidlydetect foot-and-mouth disease virus serotype C (FMDV C). By testing 10-fold serial dilutions of FMDV C samples,sensitivity of the FMDV C RT-LAMP was found to be 10times higher than that of conventional reverse transcription-PCR (RT-PCR). No cross-reactivity with A, Asia 1, or OFMDV or swine vesicular disease virus (SVDV) indicatedthat FMDV C RT-LAMP may be an exciting novel method for detecting FMDV C.
A Prediction Model of the Sum of Container Based on Combined BP Neural Network and SVM
Ding, Min-jie,Zhang, Shao-zhong,Zhong, Hai-dong,Wu, Yao-hui,Zhang, Liang-bin Korea Information Processing Society 2019 Journal of information processing systems Vol.15 No.2
The prediction of the sum of container is very important in the field of container transport. Many influencing factors can affect the prediction results. These factors are usually composed of many variables, whose composition is often very complex. In this paper, we use gray relational analysis to set up a proper forecast index system for the prediction of the sum of containers in foreign trade. To address the issue of the low accuracy of the traditional prediction models and the problem of the difficulty of fully considering all the factors and other issues, this paper puts forward a prediction model which is combined with a back-propagation (BP) neural networks and the support vector machine (SVM). First, it gives the prediction with the data normalized by the BP neural network and generates a preliminary forecast data. Second, it employs SVM for the residual correction calculation for the results based on the preliminary data. The results of practical examples show that the overall relative error of the combined prediction model is no more than 1.5%, which is less than the relative error of the single prediction models. It is hoped that the research can provide a useful reference for the prediction of the sum of container and related studies.
( Yao Wang ),( Yong-dong Dai ),( Zhong-lin Yang ),( Rui Guo ),( Yuan-bing Wang ),( Zhu L. Yang ),( Lei Ding ),( Hong Yu ) 한국균학회 2021 Mycobiology Vol.49 No.4
A cordycipitoid fungus infecting Hepialidae sp. in Nepal was supposed to be identical to Cordyceps liangshanensis, originally described from southwestern China, and thus, transferred to the genus Metacordyceps or Papiliomyces in previous studies. However, our multi-gene (nrSSU-nrLSU-tef-1a-rpb1-rpb2) phylogenetic and morphological studies based on the type specimen and additional collections of C. liangshanensis revealed that the fungus belongs to the genus Ophiocordyceps (Ophiocordycipitaceae). Therefore, a new combination O. liangshanensis was made, and a detailed description of this species was provided.
A prediction model of the sum of container based on combined BP neural network and SVM
Min-jie Ding,Shao-zhong Zhang,Haidong Zhong,Yao-hui Wu,Liang-bin Zhang 한국정보처리학회 2019 Journal of information processing systems Vol.15 No.2
The prediction of the sum of container is very important in the field of container transport. Many influencingfactors can affect the prediction results. These factors are usually composed of many variables, whosecomposition is often very complex. In this paper, we use gray relational analysis to set up a proper forecastindex system for the prediction of the sum of containers in foreign trade. To address the issue of the lowaccuracy of the traditional prediction models and the problem of the difficulty of fully considering all the factorsand other issues, this paper puts forward a prediction model which is combined with a back-propagation (BP)neural networks and the support vector machine (SVM). First, it gives the prediction with the data normalizedby the BP neural network and generates a preliminary forecast data. Second, it employs SVM for the residualcorrection calculation for the results based on the preliminary data. The results of practical examples show thatthe overall relative error of the combined prediction model is no more than 1.5%, which is less than the relativeerror of the single prediction models. It is hoped that the research can provide a useful reference for theprediction of the sum of container and related studies.