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The effect of spinning has been studied and analysed for different projectile shapes such as ogive, blunt, cylindrical and conical by using numerical simulations.

Projectile shape is one of the important parameters in the penetration mechanism. The present study deals with the failure mechanisms and ballistic evaluation for different nose-shaped projectiles undergoing normal impact with spinning. Materials characterization has been made by Johnson–Cook strength and failure models, and LS-DYNA simulations are used to analyse the impact of steel projectiles on an Al 7075-T651 target at different impact velocities under normal impact conditions. The experimental results from the literature are used to validate the model. Based on the residual velocity values, the Recht-Ipson model has been curve-fitted and approximate ballistic limit velocity has been evaluated. The approximated ballistic limit velocity is found to be 3.4% higher than the experimental results and compared well with the experimental results. Subsequently, the validated model conditions are used to study and analyse the effect of spinning for different nose-shaped projectiles undergoing normal impact conditions.

The ductile hole failure is observed for the ogive nose projectile, petals are formed and fragmented for the conical projectile, and plugging is observed for cylindrical projectiles. A Recht-Ipson curve is presented for each spinning condition for each projectile shape and the ballistic limit has been evaluated for each condition.

The proposed research outputs are original and innovative and, have a lot of importance in defence applications, particularly in arms and ammunition.

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.