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The purpose of the research is to assess the risk of the financial market in the digital economy through the quantitative analysis model in the big data era. It is a big challenge for the government to carry out financial market risk management in the big data era.

In this study, a generalized autoregressive conditional heteroskedasticity-vector autoregression (GARCH-VaR) model is constructed to analyze the big data financial market in the digital economy. Additionally, the correlation test and stationarity test are carried out to construct the best fit model and get the corresponding VaR value.

Owing to the conditional heteroscedasticity, the index return series shows the leptokurtic and fat tail phenomenon. According to the AIC (Akaike information criterion), the fitting degree of the GARCH model is measured. The AIC value difference of the models under the three distributions is not obvious, and the differences between them can be ignored.

Using the GARCH-VaR model can better measure and predict the risk of the big data finance market and provide a reliable and quantitative basis for the current technology-driven regulation in the digital economy.

The purpose of this paper is to numerically study the variations of oil film pressure, thickness and temperature rise in the contact zone of plate-pin pair in silent chains.

A steady-state thermal elastohydrodynamic lubrication (EHL) model is built using a Ree–Eyring fluid. The contact between the plate and the pin is simplified as a narrow finite line contact, and the lubrication state is examined by varying the geometry and the plate speed.

With increase in the equivalent radius of curvature, the pressure peak and the central film thickness increase. Because the plate is very thin, the temperature rise can be neglected. Even when the influence of the rounded corner region is less, a proper design can beneficially increase the minimum film thickness at both edges of the plate. Under a low entraining speed, strong stress concentration results in close-zero film thickness at both edges of the plate.

This study reveals the EHL feature of the narrow finite line contact in plate-pin pairs for silent chains and will support the future works considering transient effect, surface features and wear.

This paper aims to propose a measurement principle and a calibration method of measurement system integrated with serial robot and 3D camera to identify its parameters conveniently and achieve high measurement accuracy.

A stiffness and kinematic measurement principle of the integrated system is proposed, which considers the influence of robot weight and load weight on measurement accuracy. Then an error model is derived based on the principle that the coordinate of sphere center is invariant, which can simultaneously identify the parameters of joint stiffness, kinematic and hand-eye relationship. Further, considering the errors of the parameters to be calibrated and the measurement error of 3D camera, a method to generate calibration observation data is proposed to validate both calibration accuracy and parameter identification accuracy of calibration method.

Comparative simulations and experiments of conventional kinematic calibration method and the stiffness and kinematic calibration method proposed in this paper are conducted. The results of the simulations show that the proposed method is more accurate, and the identified values of angle parameters in modified Denavit and Hartenberg model are closer to their real values. Compared with the conventional calibration method in experiments, the proposed method decreases the maximum and mean errors by 19.9% and 13.4%, respectively.

A new measurement principle and a novel calibration method are proposed. The proposed method can simultaneously identify joint stiffness, kinematic and hand-eye parameters and obtain not only higher measurement accuracy but also higher parameter identification accuracy, which is suitable for on-site calibration.

The first indication that traditional lecture-style teaching is not very effective was provided by Dr Donald Bligh in the 1980s and 1990s. As empirical evidence about this fact has continued to accumulate, science, technology, engineering, and math (STEM) education in the USA has undergone a significant change in emphasis away from lecture-based approaches in favor of systems emphasizing more interactive learning. The paper aims to discuss this issue.

A wide range of experimental research has employed the principles of scientific teaching to investigate the efficacy of an ever widening range of pedagogical methods. For STEM education, the most successful of these has been active learning.

At its core, active learning is a redesign of in-class activities to maximize interactivity and feedback through facilitated problem-solving environments. Although the efficacies of both scientific teaching and active learning have been verified in a wide range of empirical works, the dissemination of these platforms, in general, teaching has been slow, even in the USA.

The first significant impediment has been an overall lack of awareness coupled with general skepticism about alternative learning methods.

This paper first reviews the education literature behind scientific teaching and active learning before reviewing some of the challenges to their implementation on an institutional level.

These challenges and known solutions are then applied to the European and East Asian contexts to examine why scientific teaching and active learning remain predominantly an American phenomenon.

For East Asian countries, the authors offer a commentary on how certain aspects of Confucian classroom culture may interact negatively with efforts to install scientific teaching and active learning systems.