This study investigates how product attributes shape user interpretation of unfamiliar products in terms of functions and context of use. This was made possible through an experiment involving 71 participants who were administered three unfamiliar end-use products without any additional cue. Findings reveal that visual cues, material semantics, and contextual imagination shape understanding, with misinterpretations often arising from analogical reasoning and partial cue activation. Designers should harmonize cues and leverage material symbolism to guide user perception and acceptance.

Additive manufacturing (AM) eases conventional manufacturing (CM) constraints allowing new design freedom. Yet designers still rely on experience and face AM high energy demand and variable waste benefits. This paper introduces an ecodesign approach to AM workflow through a tool comparing AM and CM via lifecycle metrics. The tool anticipates design and sustainability challenges providing environmental insights already at initial stages. This is highlighted with two use scenario: a new design and a redesign task. Future work involves tool development and validation with industrial case studies.

Designers use GenAI tools during bioinspired design (BID) process to understand biological inspiration. We investigate the influence of using ChatGPT with BID on their creative thinking. We present BID stimuli to 30 designers in three modes: BID only, ChatGPT only, and BID + ChatGPT; and record their EEG data across four design phases. Their creativity is analyzed through convergent and divergent thinking (CT and DT), measured by average β and α TRP, respectively. Results show that BID stimuli’s influence on CT and DT is mode and phase dependent, indicating CT and DT as continuous processes.

In a circular economy, repurposing extends product lifecycles and reduces resource use. However, identifying feasible repurposing opportunities remains challenging. This study therefore evaluates the capability of large language models to identify such repurposing scenarios and their relevant properties, using documented repurposing cases from peer-reviewed literature. Three models were tested, revealing potential in identifying repurposing scenarios, but also highlighting the need for structured methods and further research due to systematic limitations in property identification.

This study presents a lean experimental method for investigating Proofs of Concept (PoCs) in early product development. By adapting and extending Design of Experiments (DoE) with complementary frameworks, the method enables efficient identification of minimal functional parameter sets. Based on a cube-oriented model and iterative one-factor-at-a-time (OFAT) testing, the design space is systematically refined. Experimental validation on a smart shaft–hub connection demonstrates the method’s effectiveness in reducing required samples while ensuring feasibility.

Generative AI and additive manufacturing (AM) are shifting orthotic design from generic devices to data-driven, patient-specific solutions. This paper presents a systematic review of Generative AI in Design for AM (DfAM) for orthotic devices. It examines how AI-driven methods generate customised, lightweight orthoses via 3D printing, improving both design efficiency and anatomical fit. The review identifies biomechanical and workflow challenges that hinder adoption and outlines how Generative AI can advance orthotic DfAM, providing a conceptual workflow and suggestions for future research.

Digital platforms for food and mobility offer sustainability and convenience, but their global adoption is context-dependent. This paper analyzes eight platforms in Turkey, contributing to the discourse on sustainable consumption. The analysis reveals diverse platform configurations and identifies key consumer barriers to widespread adoption, including trust issues, platform misuse, power imbalances, and limited service. The paper concludes with recommendations for motivating Turkish users, managing stakeholder trust dynamics, and leveraging existing consumption habits in new platform design.

The following article explores an emerging theoretical framework for ‘sound-based material theatre’, a form of post-instrumental, post-puppetry performance practice. Whitehead’s ‘philosophy of the organism’ is employed as a foundational vocabulary to describe the modes of interaction and synthesis of entities in an intermedial ecology. Drawing on instrument design theory, the discussion connects processes of embodied learning, mediation and distributed agency with a collaborative ‘animist’ approach to composition and performance. Challenges of mediation in intermedial practice are examined in relation to the perceived separation of senses and disciplines. Foucault’s analysis of the pre-classical epistemology of resemblance is used as a model for describing different types of cross-modal relations between materials, applied as a ‘nexus’ method of orchestration towards an ideal of a ‘living’ organism of intermedia.

This paper presents a pedagogical tool for the co-design of systemic and resilient Nature-based Solutions for carbon removal. Tested with nine participants, it significantly improved understanding of NbS dynamics. Wilcoxon tests showed higher scores for all thinking skills derived from Bloom’s taxonomy (p < 0.05), with notable gains for higher-order skills. Participants reported that the tool was intuitive and engaging, fostering collaborative learning. Results confirm its educational value and potential to engage creative and resilient NbS design.

The market for transcranial direct current stimulation (tDCS) expands. tDCS is a non-invasive technique that delivers a weak direct electrical current to the scalp via electrodes. It is used for enhancing cognitive functions and mood. Existing research addresses technical aspects; yet, understanding users’ perceptions and broader design issues are crucial for acceptance and usability. This paper investigates the perceptions of tDCS practitioners, volunteers, and designers on commonly used electrode fixation methods. It presents design dimensions and recommendations for novel tDCS designs.

Children with Developmental Language Disorder (DLD) often experience difficulties with morphosyntax and discourse processing, which hinder their ability to establish referential coherence. While pronoun resolution has been extensively studied in typically developing (TD) children, little is known about how children with DLD process pronouns in real time, especially in Spanish—a language with rich morphology and flexible word order. This study investigated how Spanish-speaking children with DLD interpret third-person subject pronouns during sentence comprehension, examining their use of semantic and syntactic cues in reference resolution. Across three eye-tracking experiments, we tested children’s reliance on semantic gender cues in overt pronouns (Experiment 1), on syntactic cues such as grammatical role and order of mention in overt pronouns (Experiment 2), and on these same cues in null pronouns (Experiment 3). Participants were 48 Spanish–Catalan bilingual children: 16 with DLD, 16 age-matched TD peers, and 16 younger TD children matched by mean length of utterance. Eye movements during a visual world task were analyzed using growth curve and mixed-effects models. Results showed that children with DLD used semantic gender cues when available and relied on first-mentioned or subject referents when such cues were absent, suggesting compensatory use of structural heuristics in pronoun resolution.

Manufacturing companies engineering Cyber-Physical Systems of Systems face growing challenges in maintaining consistent product-related data and processes. This paper proposes a PLM Architecture Framework that integrates concepts from Product Lifecycle Management (PLM) and Enterprise Architecture (EA) along an adapted DevOps lifecycle. The framework enables consistency and transparency across engineering and business domains. A case study in machine tool engineering illustrates its potential to enhance data traceability, stakeholder alignment, and cross-generational digital continuity.

Trifludimoxazin + saflufenacil is a premix protoporphyrinogen oxidase-inhibiting herbicide with both preemergence and postemergence activity pending registration in the United States for burndown application ahead of planting corn, soybean, grain sorghum, and wheat. With early-season corn growth often occurring simultaneously with preplant burndown of fields in later-planted soybean and grain sorghum, the odds of negative impacts associated with off-target herbicide movement to the crop increase. Field studies were conducted in 2024 at seven different sites to evaluate the effect of reduced rates of trifludimoxazin + saflufenacil (12.5% to 0.4% of the lowest proposed labeled application rate of 38.3 g ai ha-1) applied to 2- or 4-leaf (lf) corn. Corn injury 7 days after treatment (DAT) for the early and later application timings ranged from 21 to 3% and 19 to 5%, respectively, while at 14 DAT these respective ranges were 12 to % and 8 to 1%. At 28 DAT, the highest rate (1/8x) applied to 2-lf corn resulted in 6% visible injury while no other treatment surpassed 3% injury. Early season plant height was negatively impacted, more so at the three highest rates applied and at the earlier timing, but this reduction did not impact yield, therefore application of trifludimoxazin + saflufenacil adjacent to corn in the early vegetative stages of growth should be avoided. However, corn affected by off-target movement should be expected to fully recover following 2-lf exposure, with no impact on yield and minimal yield impact at later timing (<3% reduction), given adequate growing conditions and agronomic/pest management practices.

Current generative artificial intelligence for Computer-Aided Design (CAD) optimizes for geometric similarity, neglecting engineering criteria like functionality, manufacturability, and sustainability. This paper addresses this gap and proposes a conceptual framework to reorient generative CAD from replicating shapes to achieving function. We introduce two hybrid training strategies: a pre-learning approach using synthetically labeled datasets (evaluated via FEA, CAM, LCA) and a self-learning approach where GenAI uses these knowledge-based tools as a reinforcement feedback loop.

In iterative product development, teams encounter various issues, such as difficulty communicating easily with stakeholders during reviews or internally during sprint planning. The present paper proposes a product-oriented visualization method that highlights engineering changes and deviations, enhances communication, anchors review feedback directly to components, and supports deriving actionable planning steps. Its implementation in development settings has demonstrated the enhancement of transparency, shared understanding, and traceability.

Perpetual innovative products (PIPs) enable the reuse of components from previous generations to create new products with improved functionality and performance, supporting a circular economy. However, the concept entails uncertainties in design due to degradation and functional integration. This paper examines how testing can reveal and reduce these uncertainties through the analysis of testing activities. A four-step process is proposed that integrates testing in PIP development. The process strengthens decision-making by translating heterogeneous testing into actionable design knowledge.

Long-COVID is a complex, multi-system condition with variable care across the UK. Using a systems and design engineering approach underpinned by Model-Based Systems Engineering (MBSE), this study examined Long-COVID clinic pathways through semi-structured interviews with 15 clinicians and patients. Thematic analysis identified five domains—attitudes, relationships, service integration, technology adoption, and safety netting. The final synthesised swimlane diagram revealed opportunities to improve coordination, operational efficiency, and patient safety within evolving care models.

This study explores the alignment of production system development processes (PSDP) with agile principles in the automotive industry. A multiple case study of eight companies reveals low overall alignment, with OEMs and subcontractors constrained by sequential, stage-gate structures, while engineering consultants show higher agility in early phases. Findings suggest gradual adoption of hybrid models, culural changes and iterative planning to enhance flexibility and responsiveness.