The influence of Chinese university students' entrepreneurial experience, alertness, and prior knowledge on opportunity recognition was examined using the novice–experienced entrepreneurs contrast paradigm. After viewing a self-made opportunity situation, 94 entrepreneurial university students and 114 nonentrepreneurial university students were instructed to complete via email or paper and pencil tests measures of opportunity recognition, entrepreneurial alertness, and prior knowledge. The results showed that entrepreneurial alertness significantly and directly predicted opportunity recognition, whereas prior knowledge significantly and indirectly affected opportunity recognition through its impact on entrepreneurial alertness. The entrepreneurial alertness of nonentrepreneurial university students significantly influenced their opportunity recognition; in contrast, the prior knowledge of entrepreneurial university students greatly influenced their opportunity recognition. Practical implications for entrepreneurial training and future directions for research on opportunity recognition are discussed.

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Tree skeleton could describe the shape and topological structure of a tree, which are useful to forest researchers. Terrestrial laser scanner (TLS) can scan trees with high accuracy and speed to acquire the point cloud data, which could be used to extract tree skeletons. An adaptive extracting method of tree skeleton based on the point cloud data of TLS was proposed in this paper. The point cloud data were segmented by artificial filtration and -means clustering, and the point cloud data of trunk and branches remained to extract skeleton. Then the skeleton nodes were calculated by using breadth first search (BFS) method, quantifying method, and clustering method. Based on their connectivity, the skeleton nodes were connected to generate the tree skeleton, which would be smoothed by using Laplace smoothing method. In this paper, the point cloud data of a toona tree and peach tree were used to test the proposed method and for comparing the proposed method with the shortest path method to illustrate the robustness and superiority of the method. The experimental results showed that the shape of tree skeleton extracted was consistent with the real tree, which showed the method proposed in the paper is effective and feasible.

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