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To improve the classification accuracy of foreign language mobile learning (m-learning) strategies applied by college students, an evaluation model based on principal component analysis (PCA) and support vector machine (SVM) is proposed. PCA was first employed to reduce the dimensionality of an evaluation system of foreign language m-learning strategies and the correlation between the indices in the system was eliminated. The first 5 principal components were extracted and a classification model based on SVM was established by taking the extracted principal components as its inputs. Gaussian radial basis function was adopted as the kernel function and the optimal SVM model was realized by adjusting the parameters C and g. The classification result was compared with those produced by a BP neural network model and a single SVM model. The simulation results prove that the PCA-SVM model has a simpler algorithm, faster calculating speed, higher classification accuracy and better generalization ability.

In view of the present situations that the traditional college English teaching is mainly based on teachers' teaching, students are in a passive learning state, the classroom atmosphere is boring and students' positive initiative can't play, this paper researches collaboration interactive teaching mode based on multimedia network technology. Based on the advantages of multimedia network English teaching mode, the multimedia network English teaching platform is constructed, college English collaborative interactive teaching strategies are put forward. Specific strategies include: find the best combining point of multimedia and teaching materials, exert students' subjectivity, combine with conventional teaching methods, create appropriate interactive environment and change teachers' role and so on. The research results of this paper have important significance in promoting college English teaching reform and improving the quality of English teaching and so on.

To increase classification accuracy of college students' mobile learning (m-learning) strategies in foreign language learning, a classification model based on principal component analysis (PCA) and probabilistic neural network (PNN) is proposed. First, an index system of college student m-learning strategy evaluation was established. Second, PCA was employed to reduce the dimensions of the original data of students' m-learning strategies obtained through questionnaire. Five principal components were extracted to be the input variables of PNN to create a PCA-PNN classification model. Third, a simulation experiment was done to compare the classification effectiveness of the established PCA-PNN model with a PNN model and a BPNN model. The experiment result shows that the PCA-PNN model has simpler network architecture, faster convergence speed, higher accuracy and better generalization ability, which proves the effectiveness of the proposed model.

Phytophthora root and stem rot of soybean [Glycine max (L.) Merr.], caused by Phytophthora sojae Kaufmann and Gerdemann, is a destructive disease throughout the soybean planting regions in the world. Here, we report insights into the function and underlying mechanisms of a novel ethylene response factor (ERF) in soybean, namely GmERF5, in host responses to P. sojae. GmERF5-overexpressing transgenic soybean exhibited significantly enhanced resistance to P. sojae and positively regulated the expression of the PR10, PR1-1, and PR10-1 genes. Sequence analysis suggested that GmERF5 contains an AP2/ERF domain of 58 aa and a conserved ERF-associated amphiphilic repression (EAR) motif in its C-terminal region. Following stress treatments, GmERF5 was significantly induced by P. sojae, ethylene (ET), abscisic acid (ABA), and salicylic acid (SA). The activity of the GmERF5 promoter (GmERF5P) was upregulated in tobacco leaves with ET, ABA, Phytophthora nicotianae, salt, and drought treatments, suggesting that GmERF5 could be involved not only in the induced defence response but also in the ABA-mediated pathway of salt and drought tolerance. GmERF5 could bind to the GCC-box element and act as a repressor of gene transcription. It was targeted to the nucleus when transiently expressed in Arabidopsis protoplasts. GmERF5 interacted with a basic helix-loop-helix transcription factor (GmbHLH) and eukaryotic translation initiation factor (GmEIF) both in yeast cells and in planta. To the best of our knowledge, GmERF5 is the first soybean EAR motif-containing ERF transcription factor demonstrated to be involved in the response to pathogen infection.

A design of LD-pumped Nd:YVO 4 laser that generates simultaneous laser action at wavelengths 1064 and 1342 nm by optimizing film design is presented. An optimized continuous-wave (cw) yellow laser at 593.5 nm in room temperature is obtained for the first time. Using type-I critical phase-matching (CPM)LBO crystal, a yellow laser at 593.5 nm is obtained by 1064 and 1342 nm intracavity sum-frequency mixing. The maximum laser output power of 85 mW is obtained when an incident pump laser of 1.8 W is used. The optical-to-optical conversion is up to 4.7%, and the power stability in 24 h is better than ±2.8%.