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Structural design theory and method for the polyurea coating system have rarely been developed. The normally used experience-based design method may lead to unsafe, unreasonable and uneconomic results. The purpose of this paper is to investigate the deformation and failure behaviour of the polyurea coating so as to propose a rational design method.

In this study, the fundamental theories concerning the nonlinear analysis of polyurea coating were discussed. Nonlinear Finite Element Method (FEM) models were built to investigate the mechanical behaviour, peeling behaviour and failure mode of the polyurea coating under various conditions, such as surface fracturing, surface hydraulic pressure and reverse hydraulic peeling.

The main conclusions are as follows. The peeled coating at the crack location exhibits a tensile-bending stress state. Coating material damage may occur at the stress concentration point. The maximum stress increases with the increase in the hydraulic pressure and cohesive strength. The peeling length of the coating decreases with the increase in cohesive strength and increases with the increase in fracture width. The reverse hydraulic pressure-induced blister edge stress concentration is the main trigger of the polyurea coating blistering damage. The blistering height decreases with increasing coating thickness and increases with increasing blistering radius and hydraulic load. The maximum stress increases with increasing blistering radius and hydraulic load. Finally, the frame of design and simple design programme were proposed.

Currently, a design method for polyurea anti-seepage coating can hardly be found. The reason is that the anti-seepage coating has only been recognized as a water-proofing layer, the mechanical behaviour of which has been ignored. This study focused on the mechanical behaviour of the anti-seepage coating and investigated the deformation and failure behaviour of the coating structure under various loading conditions through nonlinear FEM analysis. These research results offer a better understanding concerning the mechanical behaviour and design method of the polyurea anti-seepage coating.

This study aims to investigate the causes of leakage in radial oil seals under dynamic eccentricity, elucidate the influence of operating parameters on leakage failure and develop methods for predicting and preventing such leakage.

Based on the principle of cam motion and considering viscoelasticity, develops a motion model of the compression and release of the shaft seal and proposes a method to determine its failure. In addition, this study quantifies the leakage gap and formulates a quantitative calculation model to accurately determine the location and shape parameters of the leakage gap.

Leakage gaps predominantly occur during the release phase of the shaft seal. Their presence can be identified by comparing the descending times of the seal and the shaft during this phase. An increase in rotation speed and eccentricity heightens the likelihood of gap formation, with both the dimensions and leakage rate of the gap increasing as these factors escalate. Eccentricity, in particular, has a more pronounced effect on gap formation.

This study clarifies the failure mechanisms of radial oil seals under dynamic eccentricity and introduces a criterion for identifying leakage gaps, providing valuable theoretical guidance for the design and optimization of radial oil seals.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0192/.

The discrepancy between the content of an article and its title is a key characteristic of fake news. Current methods for detecting fake news often ignore the significant difference in length between the content and its title. In addition, relying solely on textual discrepancies between the title and content to distinguish between real and fake news has proven ineffective. The purpose of this paper is to develop a new approach called semantic enhancement network with content–title discrepancy (SEN–CTD), which enhances the accuracy of fake news detection.

The SEN–CTD framework is composed of two primary modules: the SEN and the content–title comparison network (CTCN). The SEN is designed to enrich the representation of news titles by integrating external information and position information to capture the context. Meanwhile, the CTCN focuses on assessing the consistency between the content of news articles and their corresponding titles examining both emotional tones and semantic attributes.

The SEN–CTD model performs well on the GossipCop, PolitiFact and RealNews data sets, achieving accuracies of 80.28%, 86.88% and 84.96%, respectively. These results highlight its effectiveness in accurately detecting fake news across different types of content.

The SEN is specifically designed to improve the representation of extremely short texts, enhancing the depth and accuracy of analyses for brief content. The CTCN is tailored to examine the consistency between news titles and their corresponding content, ensuring a thorough comparative evaluation of both emotional and semantic discrepancies.