Challenges of increasing system complexity and the need for interdisciplinary collaboration are prompting companies to reorganize towards Systems Engineering (SE). As part of the implementation of large-scale transformation programs, transformation progress is of great interest to management and employees involved. Existing maturity models lack measurable variables and reliable forecast. For this reason, a maturity model for evaluating SE Transformation is developed, that builds on quantitative metrics and enables an overarching view on transformation considering cultural aspects. Literature-based criteria for evaluating SE Transformation lay the foundation for measures and referenced metrics and indicators. Due to its data-centricity, the model presented enables a more comprehensive, fact-based decision-making basis for the design and steering of SE Transformation programs.

The properties of the external environment such as colour, light, sound and scent, have been shown to influence the emotional responses of the people in those spaces. However, these findings are typically drawn from studies using stimuli designed by researchers. It remains unclear whether workspace designers can intentionally elicit specific emotional responses in the occupants of those spaces. To address this, we evaluate two workspaces designed by students to ‘activate’ and ‘relax’ their occupants. The spaces were used as stimuli in a controlled experiment conducted during a design exhibition. Self-report measures of emotions showed that the activating room energised its occupants and the relaxing room both calmed and reduced the tension perceived by its occupants. Future analyses will determine whether physiological and behavioural measures are consistent with these findings

We prove two-sided bounds on the expected values of several geometric functionals of the convex hull of Brownian motion in $\mathbb {R}^n$ and their inverse processes. This extends some recent results of McRedmond and Xu (2017), Jovalekić (2021), and Cygan, Šebek, and the first author (2023) from the plane to higher dimensions. Our main result shows that the average time required for the convex hull in $\mathbb {R}^n$ to attain unit volume is at most $n\sqrt [n]{n!}$. The proof relies on a novel procedure that embeds an n-simplex of prescribed volume within the convex hull of the Brownian path run up to a certain stopping time. All of our bounds capture the correct order of asymptotic growth or decay in the dimension n.

The World Café method is an explorative research method that includes elements of participatory and qualitative research and is suitable for design science. The iterative approach and usage of the wisdom of the crowd enable researchers to collect data in small and large groups in a cost- and time-efficient way. However, researchers lack guidance on how to scientifically conduct the method from a process perspective and what they can do to improve the quality of the data collected. Regarding that last perspective, the table hosts play a crucial role. To solve this, we designed a Three-Phase Blueprint of the World Café method, which includes a three-step instruction procedure for preparing the table hosts. This instruction is an artifact that we tested and evaluated for its effectiveness.

Over the last decade, engineering institutions have implemented changes in engineering education curriculum to address evolving industry needs. This includes the integration of design-focused curricula at various instances throughout engineering programs. This study investigates the relationship between design problem modality and neural activity. Participants in this study were engineering students enrolled in cornerstone design. Neural activity was measured using electroencephalography (EEG) during a single session where students were presented with two design problem modalities. This data was compared with motivational factors and learning preferences. The findings reveal correlations between neural activation, student motivation, and learning preferences. This suggests that problem modality influences cognition and motivation, offering valuable insight into individual student needs.

3D food printing is transforming the food industry by enabling the production of customized, on-demand foods with intricate designs. However, achieving high shape fidelity remains a challenge for optimized food ink formulations. This study investigates 3D-printed foods with overhang designs using extrusion-based 3D printing. Mashed potato and pea protein were selected as base ingredients with varied water content to assess their differences in moisture content (70–87%), pH (5.66–7.06), firmness (0.52–8.12 N), and adhesiveness (0.29–2.73 N·s). Shape fidelity was evaluated by printing geometries with overhang angles of 0° and 60°. Results showed the best printability at a 1:4 ratio (81% moisture) for mashed potato and 1:3.5 ratio (78% moisture) for pea protein. These insights provide guidelines for engineering high-fidelity food inks, that advances additive manufacturing in food design.

This paper explores the role of concept maps in investigating and highlighting project alignment through shared understanding within a multidisciplinary, long-term, creative, practice-based project called “Fish Project.” By combining surveys and concept maps, the case study investigates team dynamics, skill diversity, and evolving project comprehension. The project aims to integrate augmented reality and geo-locative technologies to cultivate care, community, and collaborative design through a digital fish ecosystem. Analysing concept map: metadata, topology, and vocabulary, the research highlights gaps in team alignment and provides areas for cohesive project visioning and execution. The findings underscore the importance of iterative communication tools to bridge interdisciplinary boundaries and strengthen team coherence.

This paper presents a motion-based taxonomy for classifying lattice structures in additive manufacturing (AM) based on their geometric suitability for linear, oscillating, reciprocating, and rotary motions. While existing classification frameworks primarily focus on static load-bearing performance, this study develops a geometry-driven taxonomy, classifying 51 lattice variations based on how tessellation patterns and wall thickness influence motion-driven deformation. The taxonomy provides a framework independent of materials, aiding the selection of lattices for compliant structures, and energy-absorbing applications, by isolating geometric tessellations to assess their role in dynamic deformation and motion suitability. This approach links lattice geometry to motion-driven behaviour, offering a predictive framework for AM design while emphasising its role in motion applications.

One focus of creativity research is the question of how creative potential can be effectively unleashed in relation to certain cognitive styles such as convergent and divergent thinking. Neurobiological findings show that different brain structures need to be activated in order to specifically stimulate these cognitive styles. By integrating tools that help understand and optimize the neurochemistry of creativity into the design process, we enable a comprehensive application of creativity and improve the ability to develop innovative solutions. In this contribution, we therefore examine which neurobiological structures undaerlay creativity and how they can be activated in a natural way. We present practical tools for fostering creativity from litertaur, make the scientific mechanisms underlying creativity accessible to designers and propose an approach for implementing the tools.

We derive optimal road fuel taxes for gasoline, diesel and ethanol for Brazil. Fuel-related externalities, including carbon emissions and air pollution, and distance-related externalities, such as accidents, congestion and road damages, are added as Brazil has today no other way to effectively tax these components. A value-added tax (VAT) of 26.5 per cent is added to the tax for gasoline and ethanol, but not for diesel which is mostly an input and not final consumption. On this basis, we find that the optimal gasoline, diesel and ethanol taxes are US$1.03, 0.85 and 0.58 per litre, of which the respective carbon taxes, excluding VAT, are about US$0.14, 0.16 and 0.04 per litre given a carbon price of US$60 per ton of CO2. The largest externality component for gasoline and ethanol is accidents followed by congestion and carbon emissions. For diesel, air pollution is most significant, followed by accidents and congestion.

ChAx is a chatbot designed to support in technical drawing lectures by leveraging Retrieval-Augmented Generation. Addressing challenges such as the complexity of rules and dependencies in technical drawing, the system accesses the specific lecture materials to provide students with accurate and context-aware answers. The architecture combines modular components, including a RAG pipeline and a frontend with an interactive PDF viewer, ensuring transparency and user-friendliness. Optimization strategies like semantic chunking, fine-tuning, and cost-effective configurations enable efficient performance within constrained server environments. Evaluation metrics, including factual correctness and answer relevancy, were evaluated by using the LLM-as-a-judge method. The results underline ChAx’s potential to enhance educational outcomes by enabling students utilize materials more effectively.

The manufacturing process selection (MPS) greatly influences possible design decisions regarding the product’s embodiment. However, a gap remains in understanding how design engineers make these selections and what data and resources inform them. Through semi-structured interviews with engineers across various mechanical engineering industries insights into current decision-making processes are gained. The findings reveal that MPS is mostly guided by personal and collective experience, with influencing factors such as functionality and product quantities. The use of support tools remains limited. A systematic integration of data-driven tools and structured knowledge management is mostly absent. It’s concluded that reliance on experiential knowledge risks overlooking alternative processes and integrating systematic tools with existing experience-based practices could enhance MPS.

Until a few years ago, moderate alcohol consumption was thought to have (mild) beneficial effects on health. However, some recent studies have suggested that “there is no safe level” of alcohol intake. Consequently, public health institutions have responded by advising against any level of alcohol use and suggesting governments a number of policies to reduce overall alcohol consumption. Nonetheless, medical studies suffer from a variety of intrinsic limitations that could undermine the reliability of their findings, especially when focusing on low-intake levels. On the one hand, we show that the literature on alcohol consumption may suffer from publication bias; such a problem is known to be present in the scientific literature in general. On the other hand, we discuss other potential sources of bias, which are inevitable due to the infeasibility of randomized controlled trials. We assess a sample of articles for the presence of omitted variable bias, miscalculation of alcohol intake, use of linear in place of non-linear models, lack of validation of Mendelian randomization assumptions, and other possible weaknesses. We conclude that the claim that “there is no safe level” of alcohol intake is not sufficiently supported based on our current scientific knowledge.

This study presents a methodology for leveraging an LLM to generate user-centered recommendations in design for sustainable behavior. A survey of 50 users captured reasonings for evaluating thermostats’ eco-friendliness and sustainable design features. Through in-context learning, GPT-4o learned to take user perspectives for similar evaluations. The model classified 196 thermostats by eco-friendliness and design intervention types—persuasive, decisive, or both. Analysis of user sentiment and ratings of these thermostats’ reviews showed persuasive designs, which offer users behavioral control, received higher satisfaction. GPT-4o extracted features from these classifications to generate design recommendations. This method is a scalable approach for identifying user preferences and informing sustainable design decisions.

How we gather individual data to inform product design is changing. In ergonomics, methodologies are rooted in qualitative approaches, providing a holistic approach but can lack objectivity and precision. In this work, we explore novel quantitative techniques, involving machine vision and muscle sensing, to create personalized data dashboards that enrich qualitative practices in a mixed-method design. We conducted a pilot study (n=10), evaluating participants’ motion in a simple ergonomic task, followed by interviews discussing the dashboards. A thematic analysis showed that all participants agreed the dashboards affirmed their experience. Furthermore, the order of data presentation influenced their language, affecting subjectivity and specificity. This study highlights participants’ roles as stakeholders, underscoring the need for their engagement to achieve meaningful design outcomes.

The healthcare sector is a large contributor to climate change, due to their size, resource use and extensive use of single-use devices (SUDs). Despite the European Medical Device Regulation (MDR) permitting the resetting of SUDs, healthcare professionals are hesitant and seek evidence-based guidelines. This demonstration study investigates how design engineering can contribute to the feasibility of resetting SUDs that are theoretically suitable for reuse, contributing to the broader discussion on medical device sustainability. The research focuses on the quality evalualtion of reset SUDs through a detailed protocol ensuring that reused devices meet safety and performance standards. Results reveal a discrepancy between the theoretical feasibility of resetting SUD and its actual practicability. This finding highlights the necessity for more practically oriented protocols.

In this paper, I stress the need to broaden the scope of diversity in value-laden ideals of science to include geographic diversity. I argue that egalitarian and normic ideals have conceptual limitations when considering this dimension. While egalitarian frameworks advocate for a placeless science, normic frameworks predominantly locate scientific knowledge within the “Global North,” highlighting the importance of including “non-Western” perspectives from the “Global South.” These limitations have negative and unjust epistemic consequences: they risk perpetuating cultural imperialism, reproducing a colonial epistemic norming of space, and committing epistemic exoticization towards scientific communities in subaltern regions.

There is a growing consensus among philosophers that quantifying value-laden concepts can be epistemically successful and politically legitimate if all value-laden choices in the process of quantification are aligned with stakeholder values. I argue that proponents of this view have failed to argue for its basic premise: successful quantification is sufficiently unconstrained to be achievable along multiple, stakeholder-specific pathways. I then challenge this premise by considering a rare example of successful value-laden quantification in seismology. Seismologists quantified earthquake size precisely by excluding stakeholder values from measure design and testing.

This study explores a graph-theoretic approach to assess the alignment of R-imperatives with the integrated product development and supply chain design decisions in the transition toward a circular economy. By modeling interdependencies as a multi-layer graph, our framework quantifies alignment levels, identifies gaps, and provides strategic insights for improving circularity. The methodology employs a hierarchical matrix representation and scenario-based analysis to assess integration performance under different conditions. Numerical results from a case study in the lighting systems industry illustrate the approach’s practical applicability. Findings highlight that repair and remanufacturing exhibit the highest alignment potential, while repurposing shows limited viability. This research offers a structured assessment tool for companies to enhance circularity in supply chain management.