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This paper aims to amplify the retrieval and utilization of historical newspapers through the application of semantic organization, all from the vantage point of a fine-grained knowledge element perspective. This endeavor seeks to unlock the latent value embedded within newspaper contents while simultaneously furnishing invaluable guidance within methodological paradigms for research in the humanities domain.

According to the semantic organization process and knowledge element concept, this study proposes a holistic framework, including four pivotal stages: knowledge element description, extraction, association and application. Initially, a semantic description model dedicated to knowledge elements is devised. Subsequently, harnessing the advanced deep learning techniques, the study delves into the realm of entity recognition and relationship extraction. These techniques are instrumental in identifying entities within the historical newspaper contents and capturing the interdependencies that exist among them. Finally, an online platform based on Flask is developed to enable the recognition of entities and relationships within historical newspapers.

This article utilized the Shengjing Times·Changchun Compilation as the datasets for describing, extracting, associating and applying newspapers contents. Regarding knowledge element extraction, the BERT + BS consistently outperforms Bi-LSTM, CRF++ and even BERT in terms of Recall and F1 scores, making it a favorable choice for entity recognition in this context. Particularly noteworthy is the Bi-LSTM-Pro model, which stands out with the highest scores across all metrics, notably achieving an exceptional F1 score in knowledge element relationship recognition.

Historical newspapers transcend their status as mere artifacts, evolving into invaluable reservoirs safeguarding the societal and historical memory. Through semantic organization from a fine-grained knowledge element perspective, it can facilitate semantic retrieval, semantic association, information visualization and knowledge discovery services for historical newspapers. In practice, it can empower researchers to unearth profound insights within the historical and cultural context, broadening the landscape of digital humanities research and practical applications.

The purpose of this study is to investigate the sealing performance of different deep groove mechanical seals by considering the changing law of dynamic pressure effect and temperature gradient caused by high speed and high pressure.

A thermohydrodynamic lubrication model (THD) of the mechanical seal was constructed and solved using the commercial software FLUENT. The pressure and temperature distributions of the fluid under different groove types, as well as the sealing performance under different pressures, rotational speeds and sealing gaps, are obtained.

The annular groove (AG) can effectively reduce the temperature, and the T-type spiral groove (STG) can effectively inhibit the leakage. The increase of pressure and rotational speed leads to the enhancement of dynamic pressure effect and the increase of leakage, while the sealing gap increases and the leakage increases while taking away more heat. The choice of groove type is very important to the impact of sealing performance.

In consideration of the beneficial effect of deep grooves on cooling performance, the viscous temperature equation and the impact of the thermodynamic lubrication model are evaluated in conjunction with the sealing performance of four distinct groove types. This approach provides a theoretical basis for the optimal design of mechanical seals.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0184/