Search results

To predict the price of battery-grade lithium carbonate accurately and provide proper guidance to investors, a method called MFTBGAM is proposed in this study. This method integrates textual and numerical information using TCN-BiGRU–Attention.

The Word2Vec model is initially employed to process the gathered textual data concerning battery-grade lithium carbonate. Subsequently, a dual-channel text-numerical extraction model, integrating TCN and BiGRU, is constructed to extract textual and numerical features separately. Following this, the attention mechanism is applied to extract fusion features from the textual and numerical data. Finally, the market price prediction results for battery-grade lithium carbonate are calculated and outputted using the fully connected layer.

Experiments in this study are carried out using datasets consisting of news and investor commentary. The findings reveal that the MFTBGAM model exhibits superior performance compared to alternative models, showing its efficacy in precisely forecasting the future market price of battery-grade lithium carbonate.

The dataset analyzed in this study spans from 2020 to 2023, and thus, the forecast results are specifically relevant to this timeframe. Altering the sample data would necessitate repetition of the experimental process, resulting in different outcomes. Furthermore, recognizing that raw data might include noise and irrelevant information, future endeavors will explore efficient data preprocessing techniques to mitigate such issues, thereby enhancing the model’s predictive capabilities in long-term forecasting tasks.

The price prediction model serves as a valuable tool for investors in the battery-grade lithium carbonate industry, facilitating informed investment decisions. By using the results of price prediction, investors can discern opportune moments for investment. Moreover, this study utilizes two distinct types of text information – news and investor comments – as independent sources of textual data input. This approach provides investors with a more precise and comprehensive understanding of market dynamics.

We propose a novel price prediction method based on TCN-BiGRU Attention for “text-numerical” information fusion. We separately use two types of textual information, news and investor comments, for prediction to enhance the model's effectiveness and generalization ability. Additionally, we utilize news datasets including both titles and content to improve the accuracy of battery-grade lithium carbonate market price predictions.

The developed plasto-elastohydrodynamic lubrication (PEHL) model is used to demonstrate the permanent change of macro morphology by critical high local stress at micro asperities in contact, which may further affect the fluid-film characteristics.

Geometric morphology is integrated into the PEHL model to elucidate the fluid-film properties governed by both macro- and micromorphologies.

Results show the model, accounting for combination of elastic and plastic deformations, realistically reveals fluid film distribution affected by the significant pressure highly concentrated within surface micro roughness interaction. The designed macroscopic textured surface mitigates the fluid film rupture phenomenon and prevents accumulated wear degradation from plastic deformation.

The PEHL model takes into account both elastic and plastic deformations and realistically reveals the fluid film distribution affected by large pressures that are highly concentrated in surface micro-roughness interactions. The macro-textured surfaces are designed to mitigate fluid film rupture phenomena and prevent cumulative wear caused by plastic deformation.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0170/