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Flipped classroom is an instructional model in which basic subject matter knowledge is learned prior to in-class sessions, and active learning experiences happen in class. To address a gap in recent research regarding students' pre-class learning design and its effect in flipped classroom, an experimental study was conducted to investigate in a flipped classroom course, how pre-service teachers' pre-class learning effect differed according to different pre-class learning materials by using t-test. The control group completed their pre-class learning by viewing ordinary videos, while the treatment group completed their pre-class learning by viewing interactive videos designed based upon the Knowledge Integration theory. Both groups have the same instructor, classroom learning activities, and learning evaluation methods. This study found that the learning achievement, self-efficacy, the application of cognitive and the metacognitive skills of the learners in the treatment group were significantly higher than those in the control group. In addition, the interactive pre-class videos were generally well received by the learners in flipped classroom.

Based on the calculation method summed up by Sokolov with theory and experience, this paper designs the relevant structural parameters of the steam ejector and establishes a two-dimensional CFD(Computational Fluid Dynamics) model of the steam ejector. Then, the numerical simulation software FLUENT is used to study the influence of NXP (Nozzle Exit Position) on the performance of the injector and the optimum NXP is obtained. The results show that the remaining structural parameters are kept unchanged, and only Nozzle Exit Position is changed, that has an optimal value. At this time, the injection coefficient of the injector is optimal, and the injection coefficient can be improved accordingly.

The current rapid prototyping method is a cylindroid approach, the obvious disadvantage of the render program is using fixed step length to make slices, which can not handle the manufacturing accuracy and the speed flexibly. The paper provides the idea of a cone approach and some related work for variable step length slicing, this could be useful for a new type of rapid prototyping.

Rapid prototyping technology enables rapid production of complex objects directly from a computer-aided design model without involving any tooling or conventional part programming. This has created a new set of problems associated with part design, process planning, support design and value engineering analysis of rapid prototyping parts. In the paper, a framework for resolving these problems is given, which use concurrent engineering, distributed blackboard, value engineering, knowledge-based and feature-based technologies.

With the development of computer vision and machine learning, smart farming is becoming more popular and more important in agricultural industries. In this paper, we design and develop a snail detection and tracking system for real-world application. In this approach, deep learning is adopted to detect snails in the real-world. This can make full use of the computer's computing power to analyze big data and reduce researchers' workload. We have set up a snail dataset according to the video collected in the field environment, and we use a Faster R-CNN based algorithm to detect snails. Experiments show that this method can achieve good detection results. On this basis, by analyzing snail data sets, we optimized Faster R-CNN based algorithm according to the characteristics of snail's smaller size. These two methods are used by setting different anchor scale sizes and combining shallower features for detection. As a result, we improve the performance of snail detection in field conditions. We also adopt a linear Kalman filter as tracker to link objects into each trajectories Refereed/Peer-reviewed