The spread of enterprise credit risk in the supply chain may lead to large‐scale bankruptcy and credit crises, which are related to national economic and social stability and financial system security. Therefore, enterprise credit risk in the supply chain context is not only a concern for banking financial institutions, credit rating agencies and enterprise managers but also the focus of governments. This article develops a DTE‐DSA (decision tree [DT] ensemble model using the differential sampling rate, Synthetic Minority Oversampling Technique [SMOTE] and AdaBoost) prediction framework integrating supply chain information to predict enterprise credit risk. The empirical test shows that using supply chain information can significantly improve the prediction score. The DTE‐DSA model has the best prediction effect in dealing with class imbalance problems. Compared with single classifier models—such as logistic regression, k‐nearest neighbours, support vector machine, DT and DT using the SMOTE—as well as ensemble models—such as extremely randomized trees, random forest, rotation forest, extreme gradient boosting, gradient boosting DT and DT ensemble model using AdaBoost—the DTE‐DSA model not only has the best prediction score but also has a more stable performance. The comprehensive use of supply chain information and the DTE‐DSA model can result in the highest prediction score, with an area under the curve of 0.9016 and a Kolmogorov–Smirnov statistic of 0.7369. Further analysis of the variables of importance enhances the interpretability of the model and obtains relevant management insights.

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We exploit stable conjugated linkages, phenyl imine conjugated N–N bonds, for the γ-ray-induced controllable cleavage of polymer chains as a new methodology for the fabrication of γ-ray-degradable epoxy thermosets.

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The paper presents an approach to applying a classifier ensemble to identify human body gestures, so as to control a robot to write Chinese characters. Robotic handwriting ability requires complicated robotic control algorithms. In particular, the Chinese handwriting needs to consider the relative positions of a character's strokes. This approach derives the font information from human gestures by using a motion sensing input device. Five elementary strokes are used to form Chinese characters, and each elementary stroke is assigned to a type of human gestures. Then, a classifier ensemble is applied to identify each gesture so as to recognize the characters that gestured by the human demonstrator. The classier ensemble's size is reduced by feature selection techniques and harmony search algorithm, thereby achieving higher accuracy and smaller ensemble size. The inverse kinematics algorithm converts each stroke's trajectory to the robot's motor values that are executed by a robotic arm to draw the entire character. Experimental analysis shows that the proposed approach can allow a human to naturally and conveniently control the robot in order to write many Chinese characters.