In this paper, based on the dynamical system method, we obtain the exact parametric expressions of the travelling wave solutions of the Wu–Zhang system. Our approach is much different from the existing literature studies on the Wu–Zhang system. Moreover, we also study the fractional derivative of the Wu–Zhang system. Finally, by comparison between the integer-order Wu–Zhang system and the fractional-order Wu–Zhang system, we see that the phase portrait, nonzero equilibrium points, and the corresponding exact travelling wave solutions all depend on the derivative order α. Phase portraits and simulations are given to show the validity of the obtained solutions.

Abstract-Unlike doctors, teachers are not able to carry out personality analysis and teaching diagnosis via using various tools and data. So how can we build the exact teaching environment, collect the data of study process, and perform the evidence-based teaching? To answer the question, computers are introduced to observe study activities, which will surely become an important trend just as in other fields. By designing the cognition process oriented novel study observation software system based on Computer-Human Interaction (CHI) to realize the online observation of study process, massive amount of information that can hardly be obtained by traditional education and psychology is able to be efficiently collected. According to dynamics, data of study process was analyzed to obtain the variables of cognitive dynamics such as study speed, acceleration, average speed, key points, learning kinetic energy and so on. All these parameters reflected the personalities of the learners, which will provide useful information for teaching, training and diagnosis.

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SummaryLabel distribution learning (LDL) is an emerging learning paradigm, which can be used to solve the label ambiguity problem. In spite of the recent great progress in LDL algorithms considering label correlations, the majority of existing methods only measure pairwise label correlations through the commonly used similarity metric, which is incapable of accurately reflecting the complex relationship between labels. To solve this problem, a novel label distribution learning method—based on high‐order label correlations (LDL‐HLC) is proposed. By virtue of the ‐regularization sparse reconstruction of the label space, the high‐order label correlations matrix is firstly obtained. Then, a new regular term can be constructed to fit the final prediction label distribution via the correction matrix. Furthermore, efficient classification performance and complete feature selection are guaranteed by common features learning via ‐regularization. Finally, the performance and effectiveness of the proposed algorithm are well illustrated through extensive experiments on 14 label distribution datasets and comparisons with some existing algorithms.

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