Data-driven design increasingly relies on sensor data, yet these thin measurements often lack the experiential context needed to explain why events occur or what users feel and need. This limits their value for human-centred design. Passive and active contextualisation are introduced to describe how meaning is produced through inference and user participation. A real-world case study using See.Sense cycling data from Melbourne shows how combining thin and thick data produces more situated understanding and actionable design insight.

Early integration of circularity into complex system architectures remains ad hoc and weakly verifiable. Model-based systems engineering (MBSE) can address this issue with precise modeling and early verification. This paper presents an artificial intelligence supported MBSE method to integrate circular strategies based on SysML v2 models, highlighting potential changes and enhancements. Generative suggestions are integrated via a regulated workflow with traceable justifications and context-enhanced queries. A use case modeling the print head of a 3D printer illustrates this approach.

This study proposes a new design for a knowledge audit, focusing on human-AI knowledge augmentation within a consulting firm’s setting. It adopts a mixed-method paradigm, including interviews, quantitative surveys and Social Network Analysis, to identify obstacles, facilitators, and AI-mediated flows within a Community of Practice. Findings show strong motivations and AI readiness coexist with poor documentation and codification. The paper reframes the audit as a design-oriented tool for mapping how human–AI collaboration shapes organisational knowledge maturity.

In north-east Scotland the stereotype ‘White-doctor–non-White patients’ is almost reversed, as international medical graduates join the workforce. Psychiatrists from culturally diverse backgrounds in a psychotherapy supervision group explored the benefits of cultural curiosity between clinicians and patients, and among colleagues. The group acknowledges the barriers, obstacles and hurt posed by cultural difference, and at the same time rejoices in the developmental advances that can be achieved for both patient and clinician when these aspects are examined and addressed with curiosity.

Engineer-to-Order (ETO) companies often grow around ad-hoc know-how, and when they scale, usually they must standardize and choose a BOM setup, yet literature offers options but little guidance on how to decide. We propose a Focus Identification Model, a lightweight, tool-agnostic method that profiles context drivers, asks weighted decision questions, and links answers to a go-to setup with explicit governance. Calibrated on two industry cases, it yields a driver heatmap, decision matrix, and governance card to establish a common language and identify fit-for-purpose BOMs with less friction.

Understanding correlates and potential causes of mental illness stigma in community-based settings in low- and middle-income countries (LMICs) is important for developing effective interventions to reduce stigma and demand-side barriers for treatment. This study analyzed data from structured questionnaires administered to 178 respondents in the Buyende district in Eastern Uganda to investigate sociodemographic and clinical factors correlated with mental illness stigma. Factor analysis of 33 items revealed a single dominant factor reflecting mental health stigma. Bivariate and multivariate analyses of sociodemographic and clinical correlates were used to identify factors associated with stigma in the entire sample and separately within the subgroups with evidence of mental illness. In the entire sample, female gender was the only independent correlate of stigma. Analysis of the mental illness subgroup also showed that women had high levels of mental illness stigma. These findings suggest that female gender appears to be a more important correlate of mental health stigma than clinical factors. Nevertheless, effective destigmatizing interventions are needed for the entire population, with additional approaches specifically tailored to women.

Thermal history is critical to part performance and reliability in material-extrusion additive manufacturing. Using encoders and an infrared camera, we developed a method to generate thermal clouds, where each node has its distinct spatio-thermal data. Filters removed up to 20.68% of the data while preserving relevant thermal features. This study enables in-situ process monitoring that establishes the basis for part certification, particularly for high-performance polymers, and for predicting material strength from thermal clouds.

Providing effective assistive technologies is challenging due to misalignment with users’ needs, often leading to product abandonment. Occupational therapists play a key role in prescribing, adapting, and creating personalized ATs, yet technical and marketplace barriers complicate this work. Computational design tools can support OTs, yet no clear design guidelines currently exist. From four case studies, we identify six considerations: workflow integration, intuitive interfaces, real-time visualization, collaboration, customization, and safety, to guide OT-focused design tool development.

Advances in additive manufacturing (AM) enable the use of AM components in demanding complex applications with high functional requirements. As a result, integrating standardized machine elements such as conventional rolling bearings is gaining growing relevance. However, limitations regarding achievable tolerances or surface qualities in the MEX process stand in contrast to strict specifications for bearing integration. This study introduces a novel interface element and a corresponding integration process that considers both bearing requirements and the layered structure of MEX components.

This contribution analyses key factors for establishing innovation communities within the circular economy. A mixed-method approach combines systematic literature review and a stakeholder survey to identify success conditions, governance requirements, and implementation challenges. The results underline the importance of open, interdisciplinary networks and adaptive, participative governance. Recommendations focus on iterative evaluation, stakeholder inclusion, and scalable models for long-term impact.

This study investigates how large language models (LLMs) support extracting technical requirements from early product pitches. Mechanical engineering students worked under three conditions: manual, LLM-assisted, and LLM combined with a QFD interface. Both AI-assisted conditions improved requirement quality and lowered perceived difficulty. Thematic analysis showed cognitive effort shifted from generating requirements to evaluating and verifying AI outputs, while the LLM-only group reported the most positive attitudes.

This work presents the design and simulation-based validation of a next-generation cell-to-pack battery system for hybrid-electric refrigerated transport. The configuration integrates a welded stainless-steel frame, liquid-cooling system, and power-electronics module within a compact modular structure that improves assembly efficiency and adaptability. Finite-element and thermal analyses confirmed compliance with vibration and thermal requirements, achieving improved volumetric efficiency and scalability. The results provide a validated foundation for prototyping and future optimization.

Turbine blades are high-value components whose replacement is costly and slow, increasing the demand for effective repair strategies. Although PBF-LB/M supports precise additive repair, its application is limited by manual and time-intensive part preparation. This work introduces an automated digital workflow for cutting plane definition, repair geometry reconstruction and part alignment, improving reproducibility and reducing preparation time in PBF-LB/M-based turbine blade repair.

The paper presents an automated decision support application for sustainability metrics in product design by leveraging semantic technologies. The proposed framework utilizes an ontology for structured representation of design characteristics and correlating lifecycle data. A rule-based approach is investigated for enabling automated reasoning. The implementation showcases real-time impact assessments based on CAD models, supporting sustainable product development. This approach demonstrates significant potential for advancing lifecycle design practices in industrial contexts.

Conventional service design methods are valuable for improving healthcare experience, but are limited in scale and information capture. Based on a constructed database of 2,320 stories from patients and carers with multiple long-term conditions (MLTC), this paper shows how real-life experiences can be used to inform healthcare service redesign. By combining the richness of qualitative insight with the breadth and representativeness of large-scale data, it identifies “Continuity of care”, “Care coordination”, and “Temporal – Access to services” as the priority redesign opportunities for MLTC.

Trade-off studies often use the design of experiments approach, while simulation models enable data-based product optimization by AI. This paper presents a comparison of evolutionary algorithms, reinforcement learning as well as active learning for design space exploration. Based on a real-world case study and hypervolume analysis, the performance of selected algorithms is assessed. The results highlight their ability to identify pareto fronts and provide insights to deepen the understanding of AI-driven design space exploration.

As concerns about climate change and biodiversity loss intensify, circular economy strategies are crucial for decoupling economic growth from resource depletion. Yet, the consumer behavioural dimension including returning, repairing, and accepting refurbished products remains underexplored, in particular in the bicycle industry. By conducting a survey of bicycle users, this study finds a strong willingness to engage in these slow-the-loop practices, driven by cost savings, convenience, and trust, but hindered by knowledge gaps and quality concerns, implying recommendations for manufacturers.

This paper evaluates the criticality assessment within the Criticality-Based Planning of Prototype Sequences method. Two independent application studies investigate whether the existing scale definitions for novelty, technical difficulty and importance are sufficient for systematic and context-independent use. The results show that operationalised criteria and clearer evaluator guidance significantly improve consistency, reproducibility and applicability across different development projects.