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A novel analytical model is proposed to improve the accuracy of predicting the mechanical response of a strand with asymmetrically broken wires. This model incorporates a new approach to compute the contact force between the broken wire and the duct. When there is more than one asymmetrically broken wire, the previous method cannot handle this situation due to the large deflection of the center wire near the breaking end. This deflection results in a significant contact force between the center and broken wires. The omission of this force in the previous method leads to unrealistic solutions for two or three asymmetrically broken wires. This paper addresses the issue appropriately. The calculated results have been compared with those from a previous experiment, demonstrating satisfactory agreement. Furthermore, the proposed analytical model has been adopted to predict the mechanical behavior of the broken wire in multi-strand scenarios relevant to a prestressed concrete bridge. The contact force between strands is accurately calculated. The findings indicate that the axial force of the asymmetrically broken wire in multi-strands can recover after a short distance from the broken end. This recovery is attributed to the confinement effect of the other strands. [ABSTRACT FROM AUTHOR]

The social impact of cultural tourism remains controversial even though tourism development seeks a socially and economically balanced outcome. This article explores the potential of liminality as a means of explaining how cultural tourism development affects social cohesion at the neighbourhood level, particularly through open spaces. A quantitative survey of 349 respondents from two national-level tourism districts in Shanghai, Yuyuan and Xinhua, was conducted using the structural equation modeling technique. Three attributes of cultural tourism destinations (CTDs) that affect social cohesion – cultural expressions, spatial features, and management – as measured by 12 items were identified and validated in the study. The results emphasized that CTDs' cultural expressions, such as the form and appearance of buildings, had a strong positive impact on social cohesion. However, the connotation of social cohesion in CTDs is more about latent cohesion than manifest cohesion, which means people share place attachment and a sense of belonging without necessarily having closer interactions. The findings also indicate that liminality, which provides a sense of reviving and freedom, mediates CTDs' contribution to social cohesion. The study provides a reference for tourism planning and management to enhance social cohesion and further achieve consolidated tourism development by facilitating liminality. [ABSTRACT FROM AUTHOR]

The task of food image recognition, a nuanced subset of fine-grained image recognition, grapples with substantial intra-class variation and minimal inter-class differences. These challenges are compounded by the irregular and multi-scale nature of food images. Addressing these complexities, our study introduces an advanced model that leverages multiple attention mechanisms and multi-stage local fusion, grounded in the ConvNeXt architecture. Our model employs hybrid attention (HA) mechanisms to pinpoint critical discriminative regions within images, substantially mitigating the influence of background noise. Furthermore, it introduces a multi-stage local fusion (MSLF) module, fostering long-distance dependencies between feature maps at varying stages. This approach facilitates the assimilation of complementary features across scales, significantly bolstering the model's capacity for feature extraction. Furthermore, we constructed a dataset named Roushi60, which consists of 60 different categories of common meat dishes. Empirical evaluation of the ETH Food-101, ChineseFoodNet, and Roushi60 datasets reveals that our model achieves recognition accuracies of 91.12%, 82.86%, and 92.50%, respectively. These figures not only mark an improvement of 1.04%, 3.42%, and 1.36% over the foundational ConvNeXt network but also surpass the performance of most contemporary food image recognition methods. Such advancements underscore the efficacy of our proposed model in navigating the intricate landscape of food image recognition, setting a new benchmark for the field. [ABSTRACT FROM AUTHOR]