Exoskeleton robots enable individuals with impaired physical functions to perform daily activities and maintain independence. However, the discomfort experienced by users when using these devices may limit the application scope of exoskeleton robots. Therefore, this paper systematically defines and analyzes the key design factors affecting the wearing comfort of rigid exoskeleton robots by differentiating and discussing the characteristics of traditional exoskeleton robots and exoskeleton robots equipped with the self-alignment mechanism based on addressing misalignment issues. Furthermore, the various structural configurations of the Physical Human-Robot Attachment Interface (PHRAI) and related quantitative evaluation indicators are explored in depth, and the advantages and limitations of structural customized design methods combining parametric design, Three-Dimensional (3D) scanning, and 3D printing technology are evaluated. Finally, the current concerns in the research field and potential solution strategies are proposed, aiming to provide directional guidance to optimize future exoskeleton robots. The research findings are of significant value for enhancing the comfort of wearing exoskeleton robots and provide valuable theoretical and practical references for future research.

The role ofdesignin the exploration of integratedScience,Technology,Engineering, andMathematics(STEM) education has rapidly expanded in recent years.Designhas made an important contribution by providing instructional scaffolds in synthesising knowledge from multiple disciplines to solve real-world problems. Despite the potential ofDesign-based Pedagogy(DBP), there is a paucity of applicable DBP process models for constructing integrated STEM tasks and formulating implementation approaches. To address this issue, we enquire into theSolution-based Design Process(SBDP) that reconciles curriculum content knowledge and teaching through authentic problem finding and solving. Through an exploratory study of a self-critical reflection in autoethnographic design practice, we explore how the optimised SBDP can inform (i) Thedesign skillsandmindsetsof DBP forintegrated STEM education, and (ii) Theinstructional processesfor constructing and implementing integrated STEM tasks involving design models. As found in this study, a unique feature of the Solution-based DBP is that it relates to the processes of thinking and reasoning—highly cognitive and logical; its application requires the skilled operation of 3D printing-based digital fabrication. Additionally, we demonstrated a Solution-based DBP, combined with 3D printing-based digital fabrication, and providing teachers with a structural blueprint for developing integrated STEM programs. Overall, this research contributes to the corpus of methodologically rigorous and evidence-based studies on design-led integrated STEM curriculums in the context of a specific country’s education system.

This research explores the pivotal role of Building Information Modeling (BIM) technology in revolutionizing the design and construction of prefabricated buildings. It highlights the benefits of BIM-enabled design processes, including improved coordination and efficiency, while addressing challenges in integrating BIM with prefabricated elements. The study proposes artificial intelligence (AI) solutions to enhance architectural detailing, optimize workflows, and facilitate human–robot collaboration. Using autoethnographic methods, the research refines BIM-forward design through thematic analysis and technology gap (TG) assessments. It advocates for a comprehensive technology roadmap that emphasizes BIM libraries, external data sources, and the transition to a multi-dimensional BIM environment. This research contributes valuable insights to advance BIM practices in prefabricated building construction and offers a strategic framework to bridge existing TGs, enabling seamless integration of BIM and AI in future practices.

This paper proposes a method for 3D reconstruction from Freehand Design Sketching (FDS) in architecture and industrial design. The implementation begins by extracting features from the FDS using the self-supervised learning model DINO, followed by the continuous Signed Distance Function (SDF) regression as an implicit representation through a Multi-Layer Perceptron network. Taking eyeglass frames as an example, the 2D contour and freehand sketch optimize the alignment by their geometrical similarity while exploiting symmetry to improve reconstruction accuracy. Experiments demonstrate that this method can effectively reconstruct high-quality 3D models of eyeglass frames from 2D freehand sketches, outperforming existing deep learning-based 3D reconstruction methods. This research offers practical information for understanding 3D modeling methodology for FDS, triggering multiple modes of design creativity and efficient scheme adjustments in industrial or architectural conceptual design. In conclusion, this novel approach integrates self-supervised learning and geometric optimization to achieve unprecedented fidelity in 3D reconstruction from FDS, setting a new benchmark for AI-driven design processes in industrial and architectural applications.