Symmetry

This study aimed to assess the association between four depression subtypes and health-related symptoms. Although it was expected that there would be asymmetry across the associations between depression subtypes and health, that relationship had not been previously reported. Data on the core diagnostic symptoms of depression and four depression subtypes, plus health-related outcomes, were collected from a sample of 301 community participants who completed standardised self-report questionnaires. Results indicated that Somatic Depression and Depressed Mood were most consistently associated with poorer outcomes across physical, emotional, and social domains, while Cognitive Depression and Anhedonia subtypes showed weaker associations. An exploratory network analysis identified Social Functioning and Depressed Mood as central nodes, indicating that affective disturbance and social wellbeing are key pathways through which depression relates to wider health outcomes. In conclusion, these results confirmed the heterogeneity of depression, as well as indicating that certain symptom clusters carry disproportionate weight in predicting health outcomes, thus exhibiting an asymmetrical pattern of associations between depression and health.

The symmetrical relationship between affective semantics and form bionics creates new possibilities for tea packaging. This study proposes a biologically inspired workflow for tea packaging design, effectively integrating natural forms, affective semantics, and sustainability assessment. First, ten natural forms suitable for bionic design were collected. The Affinity Diagram (AD) method was adopted based on evaluations from 20 consumers and tea merchants, yielding nine effective semantic and sustainability evaluation systems. Then, 10 domain experts scored the affective semantics, and the indicator weights were determined via the Precedence Chart (PC) method. The Quality Function Deployment (QFD) method was used to construct a relationship matrix between natural forms and affective semantics, identifying prioritized natural forms. Three biomimetic tea packaging designs were developed based on the three selected priority forms. Subsequently, the Criteria Importance Through Intercriteria Correlation (CRITIC) method calculated the objective weights of sustainability indicators. These weights were combined with Grey Relational Analysis (GRA) for comprehensive ranking to determine the optimal packaging scheme. The results show that stylish design (P1) has the highest weight among affective semantics, while low resource consumption (Q1) ranks first in sustainability evaluation indicators. Bamboo joint packaging was selected as the optimal design solution in the comprehensive ranking. This design process provides a methodological framework for tea packaging design, integrates biological bionics with affective semantics, and demonstrates potential for cross-category applications.

The transportation safety of aviation structural components directly impacts equipment performance and mission success rates, constituting a critical link in modern aviation industry that cannot be overlooked. Traditional methods relying on numerical analysis or structural health monitoring techniques analyze structural stress during transportation to ensure safety. However, they suffer from low computational efficiency, inability to perform real-time online monitoring, or limited coverage to a few measurement points. To address these challenges, this study proposes a metabolic deep learning model based on Gated Recurrent Units (GRU) for predicting stresses in symmetrical vertical tail (SVT) structures during transportation. First, a refined finite element model of the symmetrical vertical tail structure is established using a model ensemble analysis strategy. By integrating modal analysis with transient analysis, the maximum stress loads for different transportation processes are calculated, generating maximum stress data samples under multiple acceleration history conditions. Second, by establishing a mathematical description and deep learning framework, the GRU establishes a mapping relationship between acceleration history and maximum stress, enabling prediction of maximum stress loads associated with different acceleration histories. This method effectively resolves the challenge of exponential mesh growth in complex assembly simulations. This research enables real-time structural stress warnings for drivers during highway transportation, triggering early alerts when stress approaches allowable limits to ensure structural safety and reliability.