The influence of nano-silica (nSiO₂) and micro-silica (mSiO₂) in the shell and wood fiber filler in the core on the thermal expansion behavior of co-extruded wood/polyethylene composites (Co-WPCs) was investigated to optimize the thermal expansion resistance. The cut Co-WPCs samples showed anisotropic thermal expansion, and the thermal expansion strain and linear coefficient of thermal expansion (LCTE) decreased by filling the shell layer with rigid silica, especially nSiO₂. Finite element analysis indicated that the polymer-filled shell was mainly responsible for the thermal expansion. The entire Co-WPCs samples exhibited a lower thermal expansion strain than the cut Co-WPCs samples due to protection by the shell. Increasing the wood fiber content in the core significantly decreased the thermal expansion strain and LCTE of the Co-WPCs. The Co-WPCs whose core layer was filled with 70% wood fiber exhibited the greatest anisotropic thermal expansion.

The current derivation and application of traditional culture lack a scientific procedural method, and the existing processes are overly cumbersome and complex. The transformation of flat patterns requires manual modeling by professional product designers, resulting in low efficiency, especially when dealing with intricate patterns. To address these issues, this paper takes Xiamen cement encaustic tiles as an example and establishes a standardized process from the extraction of cultural elements to the 3D printing of physical products. Additionally, a one-click transformation plugin from flat patterns to three-dimensional models is developed based on the QT framework and Grasshopper platform. This plugin utilizes an edge recognition algorithm to identify the transformed patterns as editable NURBS curves. The curves are then converted into surfaces using the plugin’s modeling functionality before being encapsulated. With the encapsulated plugin, users only need to input a PNG image of the desired pattern and the basic model of the desired product to obtain a product with the structural features of the specified pattern. This research aids product designers in quickly extracting the most distinctive features, significantly accelerating the development speed of cultural and creative product transformations, improving production efficiency. The simplified operational process enables more people to participate in the design of cultural and creative products, promoting the dissemination and integration of traditional culture with modern technology.

Age-friendly interaction design remains challenging in intelligent furniture applications, where complex interfaces and unreliable control may hinder middle-to-older adults’ safe operation and long-term acceptance. This study proposes a data-driven framework to support the design and evaluation of an age-friendly smart bed application by linking large-scale requirement mining, requirement-to-design translation, and controlled usability validation. First, large-scale e-commerce reviews (n = 20,174) were analyzed using LDA to identify dominant unmet needs. Key pain points clustered around interaction usability, information interpretability, and voice control reliability. Based on these findings, we developed an age-friendly smart bed app prototype. A within-subject usability study with 27 middle-to-older adults (55–65 years) compared the proposed system against a representative commercial reference application under standardized task protocols. The results showed significantly improved perceived usability, with a higher System Usability Scale (SUS) score (94.91 vs. 79.63, p < 0.001) and higher recommendation intention measured by overall NPS (59.52% vs. 34.39%). Beyond system-level usability improvement, this study makes three key contributions: (1) a reproducible ‘demand mining → design translation → empirical validation’ framework that bridges the methodological gaps in current intelligent furniture research; (2) operationalized mapping from LDA-derived user pain points to concrete multimodal interaction modules; and (3) empirical evidence from comparative usability testing with explicitly profiled middle-to-older adults (55–65 years) against a commercial reference system.