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Because the dynamic characteristics of the stock market are nonlinear, it is unclear whether stock prices can be predicted. This paper aims to explore the predictability of the stock price index from a long-memory perspective. The authors propose hybrid models to predict the next-day closing price index and explore the policy effects behind stock prices. The paper aims to discuss the aforementioned ideas.

The authors found a long memory in the stock price index series using modified R/S and GPH tests, and propose an improved bi-directional gated recurrent units (BiGRU) hybrid network framework to predict the next-day stock price index. The proposed framework integrates (1) A de-noising module—Singular Spectrum Analysis (SSA) algorithm, (2) a predictive module—BiGRU model, and (3) an optimization module—Grid Search Cross-validation (GSCV) algorithm.

Three critical findings are long memory, fit effectiveness and model optimization. There is long memory (predictability) in the stock price index series. The proposed framework yields predictions of optimum fit. Data de-noising and parameter optimization can improve the model fit.

The empirical data are obtained from the financial data of listed companies in the Wind Financial Terminal. The model can accurately predict stock price index series, guide investors to make reasonable investment decisions, and provide a basis for establishing individual industry stock investment strategies.

If the index series in the stock market exhibits long-memory characteristics, the policy implication is that fractal markets, even in the nonlinear case, allow for a corresponding distribution pattern in the value of portfolio assets. The risk of stock price volatility in various sectors has expanded due to the effects of the COVID-19 pandemic and the R-U conflict on the stock market. Predicting future trends by forecasting stock prices is critical for minimizing financial risk. The ability to mitigate the epidemic’s impact and stop losses promptly is relevant to market regulators, companies and other relevant stakeholders.

Although long memory exists, the stock price index series can be predicted. However, price fluctuations are unstable and chaotic, and traditional mathematical and statistical methods cannot provide precise predictions. The network framework proposed in this paper has robust horizontal connections between units, strong memory capability and stronger generalization ability than traditional network structures. The authors demonstrate significant performance improvements of SSA-BiGRU-GSCV over comparison models on Chinese stocks.

The purpose of this study is to investigate the sealing performance of S-CO₂ dry gas seals (DGSs) by considering the effects of pressure-induced deformation, thermal deformation and coupling deformation.

A hydrodynamic lubrication flow model of S-CO₂ DGS was established, and the model was solved using the finite difference and finite element methods. The pressure-induced deformation and thermal deformation of the sealing ring, as well as the sealing performance under the effects of pressure-induced deformation, thermal deformation and coupling deformation, were obtained.

The deformation of the sealing ring is mainly thermal deformation. The influence of pressure-induced deformation on leakage and gas film stiffness is greater than that of thermal deformation and coupling deformation. However, thermal deformation has a greater impact on friction torque and minimum film thickness than pressure-induced deformation and coupling deformation. The influence of deformations on sealing performance is important.

The sealing performance of S-CO₂ DGSs was analyzed considering the effect of pressure-induced deformation, thermal deformation and coupling deformation, which can provide a theoretical basis for S-CO₂ DGS optimization design.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2023-0120/

Shear stresses have a considerable influence on the characteristics of lubricants and become significant at high rotating speeds. This study aims to investigate the influences of shear cavitation (SC) on loading capacity of journal bearings.

A principal normal stress cavitation criterion based on the stress applied to flowing lubricant in journal bearings is developed and used to investigate SC in journal bearings. A computational fluid dynamic (CFD) model for calculating the loading capacity is established using this criterion. After validation with experimental results, the loading capacity is calculated under different conditions.

The calculation results indicate that SC intensifies when viscosity, speed and eccentricity increase. Angle of loading capacity with SC is larger than that without SC. The magnitude of loading capacity with SC is smaller than that without SC due to the decrease in the ultimate pressure. In addition, the magnitude difference between the loading capacity with and without SC increases when viscosity, speed and eccentricity increases.

Present research can provide some guidance for calculating the loading capacity when a journal bearing is operating at high speed or with a high viscosity lubricant.

Frequent itemset mining (FIM) is a basic topic in data mining. Most FIM methods build itemset database containing all possible itemsets, and use predefined thresholds to determine whether an itemset is frequent. However, the algorithm has some deficiencies. It is more fit for discrete data rather than ordinal/continuous data, which may result in computational redundancy, and some of the results are difficult to be interpreted. The purpose of this paper is to shed light on this gap by proposing a new data mining method.

Regression pattern (RP) model will be introduced, in which the regression model and FIM method will be combined to solve the existing problems. Using a survey data of computer technology and software professional qualification examination, the multiple linear regression model is selected to mine associations between items.

Some interesting associations mined by the proposed algorithm and the results show that the proposed method can be applied in ordinal/continuous data mining area. The experiment of RP model shows that, compared to FIM, the computational redundancy decreased and the results contain more information.

The proposed algorithm is designed for ordinal/continuous data and is expected to provide inspiration for data stream mining and unstructured data mining.

Compared to FIM, which mines associations between discrete items, RP model could mine associations between ordinal/continuous data sets. Importantly, RP model performs well in saving computational resource and mining meaningful associations.

The proposed algorithms provide a novelty view to define and mine association.