The development of interdisciplinary Smart Products involves complex architectures and processes, which results in new challenges like managing heterogeneous and unstructured data causing inefficiencies. Model-Based Systems Engineering (MBSE) addresses these issues through precise system modeling but encounters obstacles like a lack of model reuse and complexity. This paper introduces a novel framework integrating Artificial Intelligence into MBSE to enhance sustainability and circularity by automating model generation and reusing existing system models. Using ontology-based knowledge management and large language models, model creation, interoperability, and decision-making can be enhanced and automated and visualized in real-time. The framework's capabilities and benefits are demonstrated through the instantiation of a wireless charger system example.

Biodesign is an emerging field integrating design and science; its rise necessitates a reassessment of educational paths and working spaces for cross-disciplinary explorations, such as working with living materials and adhering to safety standards. The article examines laboratory environments dedicated to biodesign practice and education, varying from low-tech to high-tech setups and from university to community spaces, aiming to clarify the role of workspaces and infrastructures in supporting transdisciplinary research between design and science.

We surveyed Biodesign Laboratories worldwide, addressing the current status quo of various lab configurations and their unique spatial typologies to accommodate biodesign’s hybrid nature.

The result is an overview of the socio-technical topos of the laboratory as a literal breeding ground for (future) biodesigners. The qualitative data reported in this article aim to enhance the understanding of Biodesign Labs by analysing the potential of various laboratory configurations to accommodate biodesign’s hybrid nature, potentially developing unique spatial typologies.

We present a probabilistic method for assessing design reasoning in design problem settings using soundness and completeness as metrics. Building on how inference mechanisms are employed during latent need elicitation from product reviews, we compare human-led and Large Language Models (LLMs) via protocols, workshops, and surveys. We demonstrate that human reasoning patterns tend to leverage user opinions, achieving deeper coverage of need potential, whereas LLMs often produce narrower, categorically constrained needs. These findings highlight the importance of balancing inference mechanisms to ensure both coherent reasoning steps and comprehensive exploration of the design space. By formally framing reasoning during design problem-solving, we offer a foundation for developing design enabled AI and deepens our understanding of how complex reasoning unfolds in practice.

The overall quality of final Digital Twin (DT) solutions and their ability to produce useful insights are key considerations for researchers and for the industry to readily adopt them. However, validation of DTs is often neglected in existing research dedicated to their development. Further, there is a lack of methodologies for building bi-directional information exchanges between virtual and real spaces, potentially hindering effective decision-making. This work presents a comparative analysis of several quantitative metrics by implementing them on the Digital Twin of a railway braking system as a use case. Their suitability as performance measures for validation and as thresholds to support decision-making is assessed. Their integration into a novel DT structure is shown to contribute to a well-rounded validation procedure and a practical decision-making framework.

Research into the foundational theories and management of software design remains limited. A 2010 workshop was convened to explore professional software development practices. The workshop sought to foster collaboration between the software engineering and design communities by examining foundational aspects of software design. Building on that workshop’s objectives, this paper investigates contemporary professional software design practice and its management within organizational contexts. It is informed by findings from three interviews with experienced software design managers. This work addresses an important gap by examining software design management through the lens of design rather than solely from a project management perspective. Additionally, it contributes to the development of a general theory of software design by integrating diverse theoretical frameworks.

Understanding how Shared Mental Models (SMMs) develop within design teams has sparked interest in the design community of decades. But to date, there is still a lack of understanding of the factors that influence the development of these structures. This review examines the literature related to SMMs and the factors that impact collaborative efforts. Aiming to bring these two research fields together, this review proposes a new framework to help researchers better understand how SMMs develop and provide a foundation for new research and empirical evidence to establish the factors that influence the development of SMMs

Retrofitting aircraft cabins is characterized by a large number of documents created and required, most of which are currently processed manually. Engineers need to identify which documents include the information that is required for a specific task. This paper proposes an approach that builds upon a digital knowledge base and moves towards automatically processing the quantity-on-hand documents to reduce the work required to identify the required documents without the labour-intensive creation of the knowledge base in beforehand. After describing the scenario this work faces, comparable approaches and promising techniques are discussed. A process-chain that builds upon these fundamentals is presented, including a selection of feasible techniques and algorithms. Finally, the steps towards an implementation as part of the transformation towards a data-driven value chain are presented.

The humanitarian sector requires innovation to enhance emergency response efficiency and effectiveness. This paper examines the sector’s unique challenges, including complex emergencies, specialized products, diverse actors, and barriers to innovation. To address these, UNHRD has revamped its approach, blending accelerator initiatives with design-driven activities via its Innovation Lab. A task force of academics and experts is developing a tailored workflow integrating product design and business process management to improve decision-making. Efforts like market scouting, innovation contests, and in-house R&D aim to overcome current limitations, fostering collaboration among stakeholders. This approach offers a more adaptive and inclusive innovation ecosystem.

The importance of the circular economy as an alternative to today’s prevailing linear economy is recognised in both industry and research. Product designers are having a major influence on this transition by adapting the characteristics of physical products in the early phases of the product development process. However, most products follow a linear approach and are far from being circular. This paper aims to identify the challenges that product designers face when designing circular products. Building on a developed understanding of related terms in circular product design, an exploratory literature review is conducted. The results help to gain an overview and understanding of the challenges that need to be addressed. Therefore, further research directions are derived to support the transition from linear to circular products in the long term.

The transition to Industry 5.0 (I5.0) marks a shift toward human-centric, sustainable, and resilient manufacturing, leveraging technologies like collaborative robots (cobots) and AR/VR to enhance inclusivity, empowerment, and safety. This study investigates how Learning Factories (LFs) can effectively convey I5.0 principles to students and professionals. A simulated production line using AR/VR allowed participants to interact with virtual cobots, assessing key pillars of safety, inclusivity, and empowerment. A survey was used to assess the impact of this immersive environment on participants' perceptions and unconscious reactions. The findings demonstrate LFs' potential to prepare a workforce that integrates human creativity with technological innovation.

This paper presents an Integrated VDD Approach, formulated to address the lack of, and limitations associated with, work concerning the application of VDD to product families. The focus of the results obtained from the application of the Integrated VDD Approach, and the subsequent discussion, will be on the identification of break points to aid objective decision making early in the design process. Results include the identification of the most valuable common wingspan across three conventionally powered aircraft and the identification of the additional system mass which would render an aggressive electrification strategy to facilitate earlier electrification of an initially conventionally powered aircraft futile in comparison to a nominal electrification strategy.

In 1959 and 1960, Cameroonian women nationalists visited the People’s Republic of China. These members of the Union démocratique des femmes camerounaises (UDEFEC) practiced what I term a “diplomacy of intimacy,” which highlighted the effects of colonialism on their bodies, fertility, and intimate relationships to create a shared affective experience of anticolonial solidarity with their Chinese counterparts. Expanding the definition of “diplomat” to reflect how diplomacy functioned in the decolonizing world reveals that women played a much larger role than previously understood. These women diplomats remained largely invisible to the Western powers and to the postcolonial Cameroonian government, but Chinese sources provide a valuable vantage point on their diplomacy. By drawing on sources from Cameroon, China, France, and the UK, I demonstrate that during decolonization African nationalist women represented their parties on the world stage, exercising far more diplomatic power than appears in histories of decolonization focused on the West.

This paper examines the criticism of Munīr Lāhorī (1610–44) regarding the early modern literary style of tāza-gū'ī (speaking anew) through his unedited commentaries on the qasidas of ʿUrfī Shīrāzī (1556–90). Munīr is critical of the Iranian poet's overly complex style, ungrounded in the literary tradition as he perceived it, and of developments in Mughal courts that began to favor Iranian literati over their Indian counterparts. His philological criticism of ʿUrfī's qasidas and the promulgation of tāza-gū'ī elucidates the methodologies of Safavid-Mughal literary criticism and illustrates how the prominence of Iranian figures in South Asian courts influenced the discourse on early modern Persian literary developments.

The automotive industry faces many simultaneous challenges like transitioning from combustion engines to electric vehicles. Suppliers must adapt to changing markets and develop new solutions. Existing transformation approaches focus on strategic goals and comprehensive implementation. However, there is no focus on the transition of the product portfolio. This paper presents a design-thinking-based approach to rapidly generate innovative product ideas. First, company assets, product portfolios, and market environments are analysed to define the ideation focus. Next, these are recombined by interdisciplinary teams to generate ideas, which are then evaluated. In a workshop with 15 experts from an exhaust pipe manufacturer, over 400 ideas were generated and refined into 15 actionable concepts in five hours. This approach supports rapid, cost-effective innovation and strategic transformation.

We developed a cloud microphysics parameterization for the icosahedral nonhydrostatic modeling framework (ICON) model based on physics-informed machine learning (ML). By training our ML model on high-resolution simulation data, we enhance the representation of cloud microphysics in Earth system models (ESMs) compared to traditional parameterization schemes, in particular by considering the influence of high-resolution dynamics that are not resolved in coarse ESMs. We run a global, kilometer-scale ICON simulation with a one-moment cloud microphysics scheme, the complex graupel scheme, to generate 12 days of training data. Our ML approach combines a microphysics trigger classifier and a regression model. The microphysics trigger classifier identifies the grid cells where changes due to the cloud microphysical parameterization are expected. In those, the workflow continues by calling the regression model and additionally includes physical constraints for mass positivity and water mass conservation to ensure physical consistency. The microphysics trigger classifier achieves an F1 score of 0.93 on classifying unseen grid cells. The regression model reaches an $ {R}^2 $ score of 0.72 averaged over all seven microphysical tendencies on simulated days used for validation only. This results in a combined offline performance of 0.78. Using explainability techniques, we explored the correlations between input and output features, finding a strong alignment with the graupel scheme and, hence, physical understanding of cloud microphysical processes. This parameterization provides the foundation to advance the representation of cloud microphysical processes in climate models with ML, leading to more accurate climate projections and improved comprehension of the Earth’s climate system.

We show, assuming PD, that every complete finitely axiomatized second-order theory with a countable model is categorical, but that there is, assuming again PD, a complete recursively axiomatized second-order theory with a countable model which is non-categorical. We show that the existence of even very large (e.g., supercompact) cardinals does not imply the categoricity of all finitely axiomatizable complete second-order theories. More exactly, we show that a non-categorical complete finitely axiomatized second-order theory can always be obtained by (set) forcing. We also show that the categoricity of all finite complete second-order theories with a model of a certain singular cardinality $\kappa $ of uncountable cofinality can be forced over any model of set theory. Previously, Solovay had proved, assuming $V=L$, that every complete finitely axiomatized second-order theory (with or without a countable model) is categorical, and that in a generic extension of L there is a complete finitely axiomatized second-order theory with a countable model which is non-categorical.

Disadvantaged minority groups can gain support for their cause by convincing majority members of their experienced adversity. We theorize and empirically test the efficacy of different types of evidence, varying in character (statistical versus personal) and ambiguity (manifest versus ambiguous), vis-à-vis raising majority members’ awareness of ethnic minority discrimination. Reflecting the combination of these two dimensions, we develop four treatments based on real evidence/stories and test several pre-registered hypotheses regarding their efficacy in two survey-experimental studies conducted in Denmark. We find that manifest types of evidence – from an audit study and a personal story exhibiting explicit discrimination – are the most effective in raising majority members’ awareness of ethnic minority discrimination. Further, the effect of the personal story extends to increased support for anti-discrimination policies and higher donations to an immigrant NGO, highlighting how personal stories can increase majorities’ awareness of and willingness to act on the adversity experienced by minorities.

The critical period for weed control (CPWC) has been used to define weed-control threshold triggers in many cropping systems. Using the CPWC to develop a weed-control threshold for broadleaf weeds that emerge later in the season would be valuable to cotton growers to enable them to schedule management of later emerging weeds to occur before crops suffer unacceptable yield losses. Field studies were conducted over two seasons from 2006 to 2008 to determine the CPWC for a broadleaf weed in cotton, using mungbean as a mimic weed. Mungbean was planted into cotton at densities of 1 to 50 plants m−2, at up to 450 growing-degree days (GDD) after crop planting, and removed at successive 200 GDD intervals after introduction, or left to compete full season. The data were fit to logistic and Gompertz curves. More complex models were developed and tested that included the time of planting and removal, weed density, height and biomass in the relationships. The CPWC models were able to predict the yield loss from later emerging weeds and together with an understanding of the expected growth rates of the weeds, the functions could be used predictively to determine the likely impact of delaying a weed-control input. This predictive element will be of value to cotton growers needing to coordinate weed-control inputs with other farm activities.