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After clarifying the development characteristics of Sino-foreign cooperative education in a depth perspective, this paper designs a framework of online learning activities for Sino-foreign cooperative education in combination with the components of online learning activities. Correlation analysis and cluster analysis are used to explore the learners’ knowledge preparation of online learning activities and learners’ behaviors are mined to explore the correlation between behavioral characteristics and learning effects. Regression analysis is conducted with the interactive learning dimension as the independent variable and the learning outcome as the dependent variable to construct a multiple regression model of students’ interactive learning on the learning outcome of online learning activities, and to analyze the influence of online learning autonomy and interactivity on the learning outcome. It also explores the relationship between online learning activity design, teaching quality, faculty, and student satisfaction during internship practice and overall satisfaction. The data show that personal background variables and online learning autonomy explain 0.408 of the variance in learning outcomes, which is an increase of 0.390 in the strength of explanation over that of only personal background factors. The substantial increase in the R-square of the model suggests that the level of autonomy of students in the online environment is an important factor influencing the variance in learning outcomes. The more autonomy the students have in their online learning, the more competent they become in the online learning activities of Sino-foreign cooperative universities.

Small-pixel is one of the development directions of InSb infrared focal plane devices, but it will have a greater impact on the photocurrent response and crosstalk of the device. Therefore, the mesa junction device model, the planar junction device model and the epitaxial device model of the small-pixel InSb infrared focal plane array device are established using Sentaurus TCAD software. The back-incident three-pixel InSb array devices are used to simulate the photocurrent response and crosstalk of the three device models. The results show that the photocurrent response can be increased and the crosstalk can be reduced for the mesa junction device and the planar junction device by reducing device thickness and increasing junction depth, and the photocurrent response and crosstalk can be improved for the epitaxial InSb device by increasing the thickness of absorption layer and selecting an appropriate doping concentration. Considering the influence of process difficulty, it is re-commended to adopt a deep ion implanted planar junction structure or an epitaxial structure with controllable thickness and doping concentration prepared by molecular beam epitaxy.

The classification of voltage sag sources is essential for the establishment of controlling scheme and reasonable division of responsibilities in voltage sag-associated accidents. Existing methods for classifying voltage sag sources usually ignore the interpretability of the classification model, and are only dedicated to improving the accuracy of voltage classification, which cannot provide a reliable classification basis for users and power enterprises. Therefore, this article proposes an effective and interpretable voltage sag sources classification method based on sequential trajectory feature learning and Random Forest algorithm. Firstly, to fully consider the interpretable sequential trajectory features of voltage sag signals so as to improve the quality and interpretability of calculation process, the fused lasso generalized eigenvector (FLAG) algorithm is adopted to quickly search for interpretable shapelets sub-sequences from the labeled voltage sag data. After that, the labeled data and samples to-be-classified are subjected to shapelet transformation through the shapelet sub-sequences to obtain the sequential trajectory features. Finally, the random forest is trained on the labeled sequential trajectory feature data to achieve supervised sample classification, which inherits the interpretability of shapelet. To test the feasibility and validity of the proposed voltage sag sources classification method, the simulation cases based on the simulated voltage sag signals were studied. The simulation results show that the proposed method has significant advantages in terms of accuracy and interpretability of voltage sag sources classification.

We have investigated the magnetoelectric effect of a series of Y-type hexaferrite Ba0.8Sr1.2Co2Fe11.1−xAl0.9CrxO22 (x ≤ 0.4) at room temperature. Although the magnetoelectric voltage coefficient αE of these multiferroic hexaferrites is not high, it changes rapidly with an applied DC magnetic field, a phenomenon known as the giant magnetotranstance (GMT) effect. It is found that the GMT effect is enhanced with increasing content of Cr and reaches the maximum for x = 0.2. Specifically, the magnetotranstance ratio in the x = 0.2 sample is as high as −96% at 2000 Oe and −84% at 500 Oe. Further addition of Cr content causes a decay of the GMT effect. This kind of room-temperature GMT effect in Y-type hexaferrites opens up a route toward practical applications for the single-phase multiferroics. [ABSTRACT FROM AUTHOR]