This study examines how product development activities influence the environmental sustainability of complex mechatronic systems using a 2D-flatbed laser cutting system as a case study. Three levels are identified, the machine, operation and part level, at which design changes can affect environmental sustainability during machine operation. Utilizing operational machine data, nine design changes are derived indicating that ∼36% of the environmental impact in the use phase can be reduced through technical design solutions, enabling EcoDesign principles supported by data-driven approaches.

Functional decomposition shapes early design decisions but is largely qualitative, leaving units and measures implicit. This work introduces the Quantitative Functional Decomposition Problem, which formalizes functions and interfaces with measurable quantities, making decomposition solvable as a quantified planning problem. Two case studies show that the approach gives immediate feedback on the admissibility of functions and their connections. Design engineers get consistent quantified structures, which speed up iteration, reduce work and set targets for subsequent steps in the design process.

The pilot project initiatives using Knowledge-Based Engineering (KBE), Design Automation (DA), and visual modelling techniques, at Saab Aeronautics, are presented in this work. The aim is to evaluate their practical applicability and outline how organisations can implement accelerated product development methodologies. By integrating organisational knowledge, parametric models, standardized workflows and automation tools, design lead times are significantly reduced, allowing design expertise to focus on innovation, quality, and strategic problem-solving.

Systems engineering continues to face challenges such as high manual modelling costs and insufficient tool support. With the rising importance of AI methods, SE assistants, software systems that support engineers in typical SE tasks, are gaining attention. However, there is currently no systematic classification of such assistants. At the same time, their usefulness depends heavily on the quality of the human AI interaction. This paper addresses these gaps by systematically categorising SE assistants and analysing the role of interaction design in their development and application.

This exploratory study examines what forms of expert support DfAM novices need and how they perceive AI-based expert systems. The results show that cognitive orientation, transparent communication and reliable information are most valued, while social or emotional expert attributes play a minor role. The study derives requirements for explainable, trustworthy AI support tailored to the early needs of DfAM.

This paper presents a deep learning-based approach to automatically classify the rust level of screws using ResNet-18 and MobileNetV3 convolutional neural networks. A controlled salt-spray chamber was used to simulate corrosion on metal screws over 0h, 48h, 96h, and 168h of exposure. Images were processed with a circle-detection algorithm to extract individual screws, followed by data augmentation and training. The final models achieved a classification accuracy greater than 94% on the validation set.

This paper presents a design support framework that focuses on linking product design with risk management within the pharmaceutical packaging industry. The framework is intended for use by packaging designers and adopts a multi-user perspective to identify design requirements and integrate them into risk mitigation activities. It promotes safe and effective packaging through proactive design, reducing costly redesign measures. A preliminary version is presented which has been developed through studies with key industry stakeholders, including pharmaceutical packaging designers.

This study compares solid and shell generatively designed PLA components for material extrusion (MEX) at matched mass targets (100, 150, 200 g). Geometries were generated Generative Design (GD) and manufactured by MEX, then tested in a 30° quasi-static compression rig representing prosthetic heel strike. Solid designs achieved up to 92% higher peak load, but failed abruptly, whereas shells exhibited lower strength but progressive, energy-dissipating failure. Results show that simple shelling of GD outcomes cannot replace iterative GD refinement for weight-critical, load-bearing parts.

The product life cycle (PLC) is the basis for every development task. Its modeling is especially important in the context of the circular economy, as recirculation within the PLC forms its basic concept. To derive requirements for circular products, it must be known which phases are to be passed through within which circular strategy. This paper links R-strategies and life cycle phases by analyzing 56 life cycle models in regard to the number of phases, sequence, and other characteristics. A dependency matrix consolidated from the life cycles allows the findings to be utilized further.

This paper conceptualizes data-driven product management (DDPM) as an organisational capability requiring clearly defined roles for effective coordination across technical, analytical, and managerial domains. Based on a systematic literature review and industry workshops, twelve interdependent roles were identified and specified through standardized role profiles. The resulting role architecture provides a structured foundation for organisational design, enabling companies to assign responsibilities, align competences, and operationalise DDPM in practice.

The rapid diffusion of generative AI is pushing creative work toward human–AI co-creation (HAIC). This paper designs a conceptual HAIC model that specifies several indispensable elements of effective co-creation: Human, AI, Artifact, Instruction, and Interaction. We demonstrated through a case study of a large-scale management information system development project how the HAIC model helps organizations implement HAIC. The proposed framework offers both an analytical lens for researchers and prescriptive guidance for practitioners seeking to engineer reliable human–AI collaboration.

We examine how the social condition of work influences design cognition. By applying cognitive load theory, we explore that individual work fosters internal self-regulation and user-centered pragmatism, whereas group work creates the collaborative substitution paradox, in which digital resources supplant interaction, thus encouraging external regulation and experiential narratives. The findings suggest that social conditions act as a moderator of cognitive load, indicating that individual work is beneficial for deep learning, while structured group work help mitigate substitution effects.

This paper presents the Designable Inclusive Design Methodology, a modular framework that was tested across two consecutive semesters in a company-sponsored design studio within academic settings. The toolkit provides practical and repeatable methods that embed inclusion throughout the design process. Developed through research through design, it offers a fast and flexible alternative to large-scale curriculum reform. Initial findings indicate that the use of the methodology enhances student confidence, fosters deeper reflection, and promotes inclusive thinking in everyday studio work.

Iptriazopyrid is a novel 4-hydroxyphenylpyruvate dioxygenase inhibitor that, upon registration for rice production, will be the first azole carboxamide herbicide labeled in the United States. Because herbicides are commonly mixed to broaden the weed control spectrum and efficacy, herbicide interactions with iptriazopyrid must be evaluated. Field experiments were conducted near Colt, AR, in 2024 and 2025 to evaluate interactions between iptriazopyrid and either synthetic auxins or residual herbicides labeled for rice. The experiments were designed as a two-factor randomized complete block design, with the presence of iptriazopyrid at 50 g ai ha-1 as factor one and the presence of labeled synthetic auxins or residual herbicides as the second factor. Applications targeted 3- to 4-leaf barnyardgrass, and Colby’s method was utilized to determine herbicide interactions. 2,4-D was the only herbicide found to be antagonistic to iptriazopyrid throughout the experiments. Applied alone, iptriazopyrid provided 75%, 83%, and 79% barnyardgrass control at 1, 2, and 4 WAT, respectively, compared to 12%, 18%, and 8% control when mixed with 2,4-D. The iptriazopyrid plus triclopyr combination was antagonistic for barnyardgrass control at 1 and 2 weeks after treatment (WAT), with 61% and 73% control, respectively; however, the relationship was no longer significant at 4 WAT. Barnyardgrass density supported an antagonistic interaction between 2,4-D and iptriazopyrid, as combinations of the two led to higher densities than iptriazopyrid applied alone. No labeled residual herbicide antagonized iptriazopyrid for barnyardgrass control; nor did barnyardgrass densities differ from iptriazopyrid applied alone. Overall, these findings show that iptriazopyrid offers reasonable flexibility when mixed with some, but not all, synthetic auxins labeled for rice, and that there is no reduction in barnyardgrass efficacy when mixed with residual herbicides.

An old improvisational semiotic practice is gesturing by hand. Hand gestures have often been regarded as spontaneous embodiments of psychic processes, and also as a primal and universal mode of human expression. The view not only characterizes some psychological and lay theories, but also schools of modern art, most explicitly Abstract Expressionism. This article is a study of hand gestures by an art historian discussing two Abstract Expressionist painters. It shows how an ideology of gestures and brushstrokes as spontaneous emotion-driven expressions is articulated by the expert’s hand gestures, but also shown to be a calculated effect of the images, a figuration. Following the analysis of the videotaped episode, the construal of gesture as primal and emotional is shown to match conservative ideologies seeking to suppress the alleged wildness of gesture. A review of the art-historian’s own improvisational, kinesthesia-driven gesture practices concludes the article.

LLMs change the game for child language acquisition research, because they constitute an existence proof that – counter to most prevailing theoretical accounts – it is possible for learners to acquire a productive system of grammar solely from exposure to individual utterances, without the need for abstract grammatical rules or categories. Conversely, LLM development can benefit from insights from child language research.

This article is a study of people with dwarfism employed as court dwarfs during the reigns of Henri IV, Louis XIII and Louis XIV of France. Through an analysis of the varying positions held by people with dwarfism at court and the roles they performed there, it challenges existing historiographies that emphasise the wonder their physical difference elicited and their function as the monarch’s alter ego to explain their presence at court. Instead of focusing on symbolic meanings attached to dwarfism, this study centres on the activities and experiences of people with dwarfism. This approach reveals that the invisibility of people with dwarfism in the archive is partly due to their reticence to be identified with the office of court dwarf. Indeed, they struggled to power the institutional development of the office because it was a constant reminder of their marginal status at court. The office’s eventual disappearance under Louis XIV thus reveals how marginalisation combined with processes of institutionalisation to destabilise the lives and careers of people with dwarfism at court. The article’s analysis rests on a granular approach to primary sources, re-emphasising the value of archival research for the study of marginalised groups, even when surviving material is relatively lacking.

Clinical and translational investigators increasingly rely on complex institutional and national data resources, yet barriers related to data discovery, governance, and access pathways remain common. To address fragmentation in data access, we piloted a Data Navigation Program within the Clinical and Translational Science Institute (CTSI) that established a trained Data Navigator as a centralized first point of contact for investigator data inquiries who provided individualized consultations, facilitated connections to data domain experts and honest broker services, and increased awareness of institutional data assets and regulatory requirements. To better characterize investigator needs, a CTSI-wide survey assessing data sources, governance, and training priorities was conducted in collaboration with the Clinical Translational Data Science (CTDS) Workgroup. Results demonstrated strong demand for structured guidance in data discovery and governance navigation. These findings informed refinement of the program, including development of the Research Data Source Match, a self-service decision-support tool implemented in REDCap that generates customized data access roadmaps based on investigator characteristics and data needs. During the pilot year, the Data Navigator conducted consultations addressing electronic health record (EHR), PCORnet resources, and government datasets. Integrating personalized navigation with scalable self-service tools may reduce barriers and support responsible data use in translational research.