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In this paper, we use mutual information to measure the statistical interdependence between 23 industry sectors of Shanghai stock market and construct corresponding correlation network to analyze the shock of 2008 financial crisis on industry sectors. The obtained meaningful facts are as follows. First, such crisis has only a limited impact on leading industries such as Manufacturing, Commercial trade and Machinery & Equipment, which still play an important role in Chinese economy. Second, the crisis badly attacks China's export industries like Electronics, Wood & Furniture and Textile & Clothing. The damage further hurts other industries, and then export industries' influence becomes larger. Third, the crisis adversely impacts the import industries like Petrochemical, Metal & Nonmetal and Pharmaceutical Biotechnology. While due to the stimulation of macroeconomic policies, the influence of crisis on import industries is limited. Similarly, due to relatively strict capital control and the macroeconomic policies stimulating the domestic demand, those industries like Construction, Real Estate and Financial Services are slightly wounded. All these findings suggest that Chinese government should transform from the external demand to the domestic consumption to sustain economic growth.

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In traditional education, there is not much difference between assessment tasks designed for learners. However, learners' learning performance may vary due to a number of factors, e.g., learning ability, academic emotion, and learners' and teachers' academic expectations. Considering those factors, accurately recommending personalized assessment tasks for each learner is challenging. To overcome the limitations in the current work, this paper proposed an autonomousagent-based approach to recommend personalized assessment tasks considering multiple factors. Contributions of the proposed approach contain three aspects: (1) Considering objective factors, the proposed approach dynamically adjusts the assessment tasks recommended for students. (2) Considering subjective factors, the proposed approach can dynamically predict learners' learning performances by applying autonomous agent-based negotiation. (3) The proposed recommendation algorithm can handle the problems of cold start in the research of typical recommendation algorithms. A set of experiments evaluates the effectiveness of the proposed approach in this paper, and the experimental results show that the proposed approach can accurately and adaptively generate personalized assessment tasks considering objective and subjective factors.