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Nowadays, China is faced with increasing downward pressure on its economy, along with an expanding business risk on listed companies in China. Listed companies, as the solid foundation of the national economy, once they face a financial crisis, will experience hazards from multiple perspectives. Therefore, the construction of an effective financial crisis early warning model can help listed companies predict, control and resolve their risks. Based on textual data, this paper proposes a web crawler and textual analysis, to assess the sentiment and tone of financial news texts and that of the management discussion and analysis (MD&A) section in annual financial reports of listed companies. The emotional tones of the two texts are used as external and internal information sources for listed companies, respectively, to measure whether they can improve the prediction accuracy of a financial crisis early warning model based on traditional financial indicators. By comparing the early warning effects of thirteen machine learning models, this paper finds that financial news, as external texts, can provide more incremental information for prediction models. In contrast, the emotional tone of MD&A, which can be easily modified by the management, will distort predictions. Comparing the early warning effect of machine learning models with different input feature variables, this paper also finds that DBGT, AdaBoost, random forest and Bagging models maintain stable and accurate sample recognition ability. This paper quantifies financial news texts, unraveling implied information hiding behind the surface, to further improve the accuracy of the financial crisis early warning model. Thus, it provides a new research perspective for related research in the field of financial crisis warnings for listed companies. [ABSTRACT FROM AUTHOR]

Due to the small size and harsh transmission conditions of small-module gears, it is very difficult to measure gear characteristics with a modulus smaller than 1 mm. We proposed an optical coherence tomography (OCT) method for measuring small-module gears. Testing of a 30-tooth copper gear with a small modulus of 0.5 mm was carried out for the measurement of its modulus, tooth parameter, tooth number, pressure angle, modification coefficient, and tooth thickness by using OCT. In addition, the influencing factors on the measurement were discussed. The whole teeth profile of a 0.2 mm modulus gear was imaged by processing of the data collected from different clamping angles. Compared with the visual imaging without depth information and small-scale microscopic imaging, the OCT method has shown its superiority, and it has potential in the application of the measurement of micro gears with a small modulus of less than 0.2 mm. [ABSTRACT FROM AUTHOR]