Communication University of Zhejiang

Sealing reliability is essential to the safe and efficient operation of proton exchange membrane fuel cell (PEMFC) stacks. In practical stack assembly, however, increasing the seal compression to improve gas tightness may simultaneously raise the mechanical stress in the membrane electrode assembly (MEA), especially in the membrane and gas diffusion layer region. This trade-off makes conventional seal design based solely on leakage prevention insufficient for high-performance stack packaging. In this study, a finite-element-based pressure allocation strategy is developed to coordinate the mechanical requirements of the sealant and the MEA. A two-level modeling framework is adopted. First, a local metal bipolar plate-sealant-MEA frame model is established to verify sealing feasibility and evaluate the force requirement of the seal line under different compression ratios. Then, a refined full model including the flow-field ribs, seal groove, sealant, proton exchange membrane, frame region, and gas diffusion layer is built to investigate the coupling between seal compression, bipolar-plate displacement, and membrane stress. The results show that although a semicircular sealant can satisfy the sealing criterion in the local model, it becomes unsuitable in the full structure because the compression required to ensure gas tightness leads to excessive membrane stress. Under a 20% compression ratio, the maximum equivalent stress in the proton exchange membrane reaches approximately 4.8 MPa, which is far above the desired level of about 1.4 MPa. An improved pressure allocation strategy is therefore proposed by controlling bipolar-plate displacement rather than directly preserving the seal compression state. Based on this strategy, an optimized rectangular sealant section at a compression ratio of 17% achieves both adequate gas tightness and acceptable membrane stress. The study provides a practical finite-element-guided route for coordinated seal and MEA design in PEMFC stacks.

This work examines strategies for enhancing immersion, interactivity, and creative expression in artificial intelligence-assisted new media art exhibitions. An immersive interactive system was developed that integrates tactile feedback enhancement technology with multimodal interaction, including touch control, gesture recognition, voice control, and dynamic spatial layout. Using an immersive interaction evaluation model, interaction data were collected from 120 participants aged 18 to 60 within an experimental environment equipped with a virtual reality head-mounted display, a Leap Motion gesture sensor, surround sound, and multiple touchscreens. Key system parameters included a touch sensitivity of 0.5, a resolution of 1920×1080, a gesture recognition range of 180°, and a voice response delay of 0.3s with a recognition accuracy of 95%. Results indicate that the tactile feedback enhancement technology-enhanced system substantially outperforms the traditional touch-control system across all measured dimensions: immersion (4.0 versus 3.5), interactivity (4.2 versus 3.8), technical stability (4.5 versus 4.0), creative expression (3.9 versus 3.2), and user satisfaction (4.1 versus 3.6). These findings suggest that artificial intelligence-enabled multimodal interaction can significantly improve the exhibition experience while offering practical design guidance for curators and exhibition designers.

The integration and development of computer vision and art design in the media field is an important area of ongoing research and exploration in the humanities and society. This paper takes Generative Adversarial Network(GAN) as the research entry point, proposes its specific application in AIGC, and constructs an image art stylization model based on GAN to realize the migration of image art style for content creation in the media field. On this basis, corresponding to the needs of content generation and creation in the media field, the digital media content generation and creation technology based on computer vision technology is proposed to ensure the quality of the generated content. The content generation and creation technology in the media field proposed in this paper is applied to design public art in the public resting area of Guanfeng Village in Ganbei, Jiangxi Province, China, designing a public art gallery and evaluating it in comparison with neighboring villages such as Wangkou Village, Xiaoqi Village, Likeng Village and Huangling Village. Among the 25 adjective pairs in the public semantic evaluation, except for the adjective pair “abstract - figurative”, the value of positive adjective pairs in the remaining adjective pairs of Guanfeng Village is closest to 1, which brings the public a good artistic emotional experience. The value of all the remaining adjective pairs in favor of positive is close to 1.

The co-creation practices between traditional Chinese indigenous artisans and contemporary designers present a unique opportunity to integrate ecological principles with traditional building techniques. This study explores how equity interactions in these collaborations can bridge the gap between historical craftsmanship and modern ecological design. Using narrative inquiry, data from nine participants reveal six key practices: familiarity, mapping, ideation, prototyping, production, and promotion. Additionally, three core elements—belonging, openness, and normativity—are identified as critical for achieving effective collaboration. The findings offer theoretical insights into the revival and sustainable development of traditional building techniques and practical implications for fostering ecological innovation through co-creation. This research contributes to the preservation and evolution of traditional crafts in the context of ecological design and sustainable development.

We propose and experimentally realize a parity-time ( )-symmetric bimorphic topological insulator, characterized by the coexistence of first- and second-order topological phases. Our platform utilizes an anomalous Floquet model implemented in a 2D optical waveguide array, where symmetry is maintained via a four-step driving protocol with spatiotemporally engineered loss. Despite the presence of dissipation, we demonstrate that the symmetry ensures a purely real quasienergy spectrum across the topological regime, effectively stabilizing boundary modes against the dynamical instabilities and mode competition typical of non-Hermitian systems. Experimentally, we provide unambiguous evidence of this non-Hermitian bimorphic phase by observing unidirectional, clockwise edge transport in the zero gap and robust corner localization in the gap. Our findings pave the way for further exploration of -symmetric and non-Hermitian topological phases in nonlinear and many-body regimes and provide novel insights into the realization of topological insulators in open quantumsystems.

Abstract This study presents an ultra-broadband epsilon-near-zero (ENZ) modulator constructed from graphene–black phosphorus hybrid structures. The key mechanism enabling broadband modulation with high modulation depth (MD) is the spectrally complementary ENZ responses of graphene and black phosphorus, where graphene provides a tunable ENZ condition in the visible (VIS) band, while black phosphorus exhibits an anisotropic ENZ response in the near- to mid-infrared (NIR-MIR) regime. Simulation results demonstrate that the proposed modulator functions efficiently over a continuous ENZ-enhanced field-confinement band from 400 nm to 18.63 μ m, spanning the VIS to MIR regions. The MD is significantly enhanced, particularly in the MIR regime due to the induced ENZ effect. In addition, the modulation bandwidth of up to 710 GHz is achieved. Our work highlights the potential to enhance MD across an ultra-broadband spectrum, facilitating future applications of optoelectronic devices that utilize hybridized two-dimensional materials.

Educational inequity in arts learning disproportionately marginalizes students with Autism Spectrum Disorder (ASD), who require structured, predictable environments for aesthetic development and sensory regulation that traditional pedagogies fail to provide. This study introduces an AI-powered e-learning platform that addresses these systematic barriers through intelligent cross-modal integration, democratizing access to personalized art education for neurodivergent learners. Our DeepSeek-based system transforms visual art features into structured musical accompaniments that accommodate individual sensory processing patterns, cultural backgrounds, and neurodevelopmental profiles while maintaining the predictability essential for ASD learning success. The platform employs enhanced ResNet-50 architecture, high-dimensional manifold mapping, and conditional generation models specifically optimized for sensory regulation principles. Comprehensive evaluation with 203 participants (including 53 neurodivergent learners) and 19 autism education specialists demonstrates substantial improvements: sensory comfort ratings of 4.6/5, learning satisfaction of 4.3/5, and educational outcomes showing 20.5% NAEP score improvements compared to 8.2% for traditional methods (p < 0.008). Technical performance achieved superior cross-modal consistency (MSE 0.05, PCC 0.92) with 89% accommodation success across diverse sensory profiles. This research offers a promising model for inclusive digital education by demonstrating how AI can contribute to mitigating educational inequities for neurodivergent populations. It provides a scalable framework that advances accessible arts education, embracing neurodiversity while maintaining academic rigor.