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The purpose of this paper is to investigate the effects of different surface structures, dimensional parameters and cavitation models on the lubrication characteristics of water-lubricated journal bearings.

In this paper, the coupling iteration method of ANSYS and MATLAB is established to calculate the journal orbits of water-lubricated bearing, and the differences between the journal orbits of the smoothed and the textured water-lubricated bearings are compared and analyzed, and the effects of different bearing materials, L/D ratios and clearance ratios on the lubrication performance of water-lubricated bearings are investigated. The effects of different cavitation models on the static equilibrium position and whirling trajectory of water-lubricated bearings are compared.

The results show that when the surface texture is distributed in the upper bearing or the bearing elastic modulus decreases, the bearing stability increases. Considering shear cavitation and noncondensing gas, the rotor journal orbits amplitude decreases at high speed with low clearance ratio. A water film test rig for water-lubricated bearings is built to measure the full-circle water film pressure of water-lubricated journal bearings, and the experimental results are compared with the simulation results, which are in good agreement.

The findings provide a theoretical basis for optimizing the structure of water-lubricated bearings.

The purpose of this paper is to propose a new tool for stock investment risk management through studying stocks with what kind of characteristics can be predicted by individual investor behavior.

Based on comment data of individual stock from the Snowball, a thermal optimal path method is employed to analyze the lead–lag relationship between investor attention (IA) and the stock price. And machine learning algorithms, including SVM and BP neural network, are used to predict the prices of certain kind of stock.

It turns out that the lead–lag relationships between IA and the stock price change dynamically. Forecasting based on investor behavior is more accurate only when the IA of the stock is stably leading its price change most of the time.

One limitation of this paper is that it studies China’s stock market only; however, different conclusions could be drawn for other financial markets or mature stock markets.

As for the implications, the new tool could improve the prediction accuracy of the model, thus have practical significance for stock selection and dynamic portfolio management.

This paper is one of the first few research works that introduce individual investor data into portfolio risk management. The new tool put forward in this study can capture the dynamic interplay between IA and stock price change, which help investors identify and control the risk of their portfolios.