Hubei Institute of Fine Arts

Museums are key institutions for cultural communication and public education, and their operating concept is shifting from exhibit-centered to experience-centered. As expectations for exhibition experience rise, museum fatigue has become a major constraint on visitors. Existing studies rely on questionnaires and other subjective measures, which makes it difficult to locate fatigue in specific spaces. At the same time, body pose detection and fatigue recognition techniques remain hard to apply in museums because of complex spatial configurations and dense visitor flows. Effective methods for quantifying and mitigating museum fatigue are still lacking. This study proposes a contact-free sensing scheme based on computer vision and builds a coupled analytical framework with three stages: Human Pose Estimation (HPE) for visitor posture detection, fatigue assessment, and fatigue mitigation. A Fatigue Index (FI) quantifies bodily fatigue. Applying this index to the exhibition space in both the baseline and adjusted configurations guides the formulation of mitigation strategies and shows a consistent reduction in FI, which indicates that the adopted measures are effective. The proposed approach establishes a complete frame from fatigue quantification to fatigue mitigation, supports evaluation of exhibition space design, and provides theoretical and methodological support for future improvements to museum experience.

ABSTRACT Contemporary China’s urban development has been driven by a logic of homogenization, quantification, and exchange value—hallmarks of the globally admired ‘Chinese speed.’ Yet this rapid abstraction of urban space has increasingly disconnected from the lived experiences of residents, fostering widespread disillusionment and eroding the public’s confidence in everyday spatial participation. As a result, the city risks losing its capacity for organic renewal. In stark contrast, a group of working poets—migrant labourers engaged in creative self-expression—inhabits the same spaces while producing alternative, poetic forms of everyday life. This article employs Henri Lefebvre’s spatial dialectics to analyse the divergent spatial practices of urban residents and working poets, revealing the roots of their contrasting experiences. Through interdisciplinary inquiry—combining urban theory, field interviews, and audiovisual experimentation—this research culminates in the creation of an essay film that constructs a representational image-space based on the working poets’ practices. By doing so, it proposes a narrative intervention that not only critiques dominant spatial discourses but also invites publics to reclaim everyday space through creative, poetic acts. Ultimately, the article positions artmaking as a method of urban problem-solving—foregrounding difference, imagination, and self-identification as vital tools for reactivating the right to the city.

This paper examines the role of the Baishou Dance, a collective ritual of the Tujia people in Western Hubei, in fostering community resilience. As a significant intangible cultural heritage (ICH) practice, Baishou Dance transcends its artistic value to function as a vital social institution. Based on ethnographic fieldwork in Laifeng County, this study investigates how the dance, through its ritual performances, collective narratives, and intergenerational transmission, strengthens social cohesion, reinforces cultural identity, and enhances the community's adaptive capacity in the face of modernization and social change. We argue that the safeguarding of Baishou Dance is intrinsically linked to the resilience of the Tujia community. The dance serves as a dynamic reservoir of cultural knowledge and social capital, enabling the community to navigate challenges while maintaining its cultural distinctiveness. The findings suggest that supporting such living heritage practices is not merely an act of cultural preservation but a crucial strategy for promoting sustainable community development. This case study offers broader insights for global ICH safeguarding efforts, highlighting the importance of community-based resilience as a key indicator of cultural sustainability.

The growing problem of antibiotic resistance and associated side effects underscores the need for exploring novel therapeutic strategies. The utilization of insect resources is being investigated as one potential avenue in this context. The effective utilization of insect resources represents a promising pathway to this end. This study focuses on investigating the glycerolysis of black soldier fly (Hermetia illucens) larvae (BSFL) oil, which is rich in lauric acid, to optimize the production of antimicrobial monoglycerides. Response surface optimization yielded the following optimal conditions: 35.5 min, 219 °C, 0.72% sodium methoxide catalyst, and a 1:4 molar ratio of triglyceride to glycerol. Under these conditions, monoglycerides accounted for 55.86% of the product, specifically glycerol monolaurate, accounting for 29.47%; this mixture showed notable antimicrobial activity against Staphylococcus aureus, methicillin-resistant Staphylococcus aureus (MRSA), Staphylococcus epidermidis, Escherichia coli, and Pseudomonas aeruginosa. After purification via the solvent crystallization method, the monoglyceride content rose to 69.64%, while the glycerol monolaurate content increased to 35.24%, resulting in enhanced antimicrobial efficacy. Notably, monoglycerides were more effective against Gram-positive than Gram-negative bacteria, consistent with their known membrane-targeting specificity. Importantly, the potent activity against MRSA highlights the potential of these MAGs to combat antibiotic-resistant strains. These findings indicate that BSFL oil is a sustainable feedstock for producing antimicrobial agents with in vitro efficacy. This work supports the further investigation of MAGs derived from BSFL oil as potential candidates to complement existing antibiotics, particularly against resistant strains such as MRSA.

Emotional responses to visual art involve perceptual and physiological interactions, but existing methods isolate these modalities, limiting individualized affective modeling. This study presents BioArt-Net, a computational framework integrating visual semantic analysis and physiological signal processing for viewer-centric art emotion recognition, focusing on cross-modal fusion methodologies. Key modules: Visual semantics via fine-tuned ViT (16 × 16 patches, 32% dimensionality reduction); EEG (wavelet transform), GSR/PPG (1D CNNs) encoding with 94% data integrity; token-level attention aligning modalities; compound loss (CE + MSE + entropy) reducing redundancy by 27%; optimized via distillation (48% fewer parameters, 89 ms latency). On 520-session BioArt-Emotion Dataset, accuracy reaches 0.87 (vs 0.76 unimodal), F1 = 0.85. Cross-modal attention boosts accuracy by 11%; physiological reweighting stabilizes results. This advances computational neuroaesthetics, fitting the journal’s focus on innovative interdisciplinary frameworks.

Chinese Kung Fu not only emphasizes external techniques and movements but also places great importance on the cultivation of internal states such as Fa Li (force exertion) and Qi (energy control). However, existing motor learning systems predominantly focus on improving movement accuracy, with limited attention to the awareness and guidance of these internal states. Kung Fu films often use visual effects (VFX) to vividly express traditional cultural imagery. Inspired by this, we designed and developed a wearable motor learning system and interactive interface that integrates interactive VFX and haptic feedback to enhance users’ awareness of internal states such as force exertion and Qi, thereby improving both learning effectiveness and user experience. Through prototyping and an exploratory study, we found that the system significantly improved users’ awareness of Qi and force exertion, as well as their overall training experience. In addition, we identified several usability issues and proposed corresponding design improvements. This study introduces a novel visualization framework for internal state cues in Kung Fu training, offering new perspectives and practical approaches for the design of motor learning systems.

Global change threatens mutualistic networks, altering their structure and species richness. The influence of such changes on dynamical stability, which is the ability to return to pre-disturbance conditions after a disturbance, is still debated. Generalized Modeling (GM) combines the advantages of previous theoretical approaches by being highly efficient, while also allowing to incorporate high biological realism such as competition for limited mutualistic resources. We develop a GM for mutualistic networks and find increasing dynamical stability with increasing complexity, which we relate to a weakening of the mutualistic dependencies in the Jacobian matrices. We find a negligible role of nestedness for the stability of random and empirical networks. Our findings emphasize the need to increase conservation efforts that preserve biodiversity of mutualistic networks, as loss of species and interactions makes them more vulnerable to disturbances.

In the context of China's active promotion of the integration between artificial intelligence and cultural innovation, generative AI models are increasingly applied in the field of art and design. This study presents an image generation and interaction system that merges surrealist art concepts, constructed using Stable Diffusion and TouchDesigner. The system generates imaginative visual outcomes in real time through the collaboration of facial capture and text prompts, operating within a latent space. By analyzing the alignment between the diffusion mechanism and surrealist concepts such as “automatism” and the “unconscious,” this paper explores the creative potential of AI-generated imagery in artistic expression. Unlike previous experiments that aimed solely at image output, the system developed here emphasizes human-machine collaboration, visual interaction, and nonlinear generative processes. It offers practical technical solutions and creative paradigms for real-world applications in education, exhibitions, and cultural product design. The study demonstrates that the system not only ensures stable image output but also serves as an interactive platform that stimulates creativity and reconfigures aesthetic experience, thereby opening new ways for the practical application of AI in visual art.

ABSTRACT Visual blind spots caused by occlusion from lead vehicles represent a significant latent risk factor contributing to traffic accidents. Although Level 2 (L2) autonomous driving systems can partially mitigate this issue, human drivers are still required to actively perceive potential hazards and understand the behaviour of the autonomous vehicle to ensure driving safety. Therefore, designing interactive interfaces that reduce the risks associated with visual blind spots is critical in autonomous driving scenarios. However, current research on this typical scenario remains relatively limited. This study proposes an innovative cooperative driving warning strategy, focussing on driving situations where the driver's line of sight is blocked by a lead vehicle. The strategy integrates environmental cues and behavioural information from the autonomous driving system through an augmented reality (AR) interface, aiming to facilitate efficient cooperation between human drivers and autonomous systems in perceiving visual blind spots. We systematically evaluated the proposed interface prototype using a driving simulator under L2 autonomous driving conditions. The results indicate that the interface significantly enhances drivers' situation awareness and reduces their reaction time to potential hazards. Additionally, the design improves the quality of human-machine cooperation by decreasing conflicts with the autonomous system, increasing trust in the system, and significantly boosting user satisfaction. This study provides new insights into the design of human-machine cooperative perception interfaces for blind spot scenarios and offers both theoretical foundations and practical implications for the future development of cooperative driving systems.