Digital engineering transformation in industrial companies requires addressing diverse needs and their impact on every impacted engineering aspect. This paper analyses Changes initiated by transformation drivers and presents a systematic approach to integrate sustainability into engineering processes and artifacts. As a currently important topic the integration of sustainability data in engineering is used as an example of application. Based on identified use cases, sustainability parameters are derived and linked to engineering data objects to pinpoint their placement within the early product development. The results demonstrate how data-driven approaches enable effective sustainability integration and provide a foundation for future digital engineering transformations due to diverse divers.

This study explores the influence of perceived similarity on pro-environmental behavior, focusing on plastic reduction. Participants’ daily plastic use and reduction were tracked over 30 days via online chat software, with controlled nudges from an agent. Each group included two examinees and one agent. Behavioral data were analyzed to evaluate predictability from various perspectives and its relationship with behavioral change. Results showed significant differences in predictability based on perceived similarity, particularly during the first 10 days. Furthermore, nudges, consumption levels, and behavioral changes significantly affected predictability within the first 20 days. These findings contribute to understanding how perceived similarity can enhance nudging strategies to promote sustainable behavior and reduce plastic consumption.

Lightweight design is critical for improving the efficiency and sustainability of engineering applications. Laminated composites, with their high strength-to-weight ratio and tailored material properties, play a key role but introduce interlaminar stresses, particularly near free edges where delamination failures often occur. Addressing these stresses typically requires computationally expensive 3D finite element simulations, limiting their use in early design stages. This study presents a machine learning approach using Gaussian process regression and artificial neural networks to efficiently predict interlaminar stresses based on in-plane stress data from shell FE simulations. Achieving high predictive accuracy, this method enables cost-effective, early-stage composite design optimization under complex loading scenarios.

This paper presents the MBSE-Graph-RAG framework to address key challenges in Model-Based Systems Engineering (MBSE). Traditional MBSE tools suffer from usability barriers, limited accessibility, and integration challenges. By combining knowledge graphs with Retrieval-Augmented Generation (RAG), the proposed framework enables AI-Augmented engineering through natural language interactions and automated system architecture generation. A systematic literature review establishes a solid research foundation, identifying gaps in AI-assisted MBSE. Key contributions include a structured MBSE-Graph interface, improved usability via Large Language Models (LLMs), and automated graph construction aligned with SysML. A proof-of-concept demonstrates the potential of this approach to enhance MBSE by reducing complexity, improving data accessibility, and supporting engineering collaboration.

The article examines informal carers’ experiences of co-producing care, combining notions of carer roles with the strategies used by carers in their interactions and negotiations with the health and social services. The aim is to contribute to the theoretical understanding of carers’ role in co-production. On the basis of interviews with carers with a wide range of experiences, we find that they wish to be treated as co-producers, but their roles and impact depend on whether they are tasked with co-producing knowledge or co-producing care. In knowledge production, informal carers are encouraged to take active part and use their voice to further the interests and values of the person in need of support. However, their impact is conditional on their initiatives being recognised by formal caregivers and, to some extent, the person in need of support. In providing care, their efforts largely go unnoticed, and they are less likely to make their voices heard, but their room to manoeuvre appears to be greater. However, when the work of carers is not recognised, formal carers forego resources that are important to the quality and effectiveness of care. The findings, we argue, have important implications for the theory and practice of co-production.

The southern early-type, young, eccentric-orbit eclipsing binary NO Puppis forms the A component of the multiple star Gaia DR3 5528147999779517568. The B component is an astrometric binary now at a separation of about 8.1 arcsec. There may be other fainter stars in this interesting but complex stellar system. We have combined several lines of evidence, including TESS data from four sectors, new ground-based BVR photometry, HARPS (ESO) and HERCULES (UCMJO) high-resolution spectra and astrometry of NO Pup. We derive a revised set of absolute parameters with increased precision. Alternative optimal curve-fitting programs were used in the analysis, allowing a wider view of modelling and parameter uncertainties. The main parameters are as follows: $M_{Aa} = 3.58 \pm 0.11$, $M_{Ab} = 1.68 \pm 0.09$ (M$_\odot$); $R_{Aa} = 2.17 \pm 0.03$, $R_{Ab} = 1.51 \pm 0.06$ (R$_\odot$), and $T_{\mathrm{e Aa}} = 13\,300 \pm 500$, $T_{\mathrm{e Ab}} = 7\,400 \pm 500$ (K). We estimate approximate masses of the wide companions, Ba and Bb, as $M_{Ba} = 2.0$ and $M_{Bb} = 1.8$ (M$_\odot$). The close binary’s orbital separation is $a= 8.51 \pm 0.05$ (R$_\odot$); its age is approximately 20 Myr and distance $172 \pm 1$ pc. The close binary’s secondary (Ab) appears to be the source of low amplitude $ {\delta}$ Scuti-type oscillations, although the form of these oscillations is irregular and unrepetitive. Analysis of the $ \lambda$ 6678 He I profile of the primary show synchronism of the mean bodily and orbital rotations. The retention of significant orbital eccentricity, in view of the closeness of the A-system components, is unexpected and poses challenges for the explanation that we discuss.

Digital products and applications are rapidly evolving, offering immense potential to drive social change and encourage sustainable behaviors. This raises a critical question: how can we effectively design these products to support and inspire sustainable practices? This paper presents a literature review of design for sustainable behavior (DfSB) strategies across various digital and physical product-service systems in engineering design and human-computer interaction. The review examines DfSB intervention trends over the last decade, highlighting the increasing diversity of technological interventions, and categorizes the design methods employed in these technologies. These categories identify opportunities where future DfSB interventions can be applied and illustrate how the unique affordances of digital vs physical technologies can be effectively used to support sustainable practices.

Thousands of federal policies have been produced by coalitions of executive agencies over the last few decades. Despite this, little attention has been paid to why agencies collaborate. The decision among relatively autonomous agencies to collaborate and therefore cede some of their power demands theoretical attention. I argue that agencies form coalitions to overcome legislative oversight attempts by activating veto points and exploiting collective action problems in Congress. Using data on dozens of agencies over twenty-four years, I find that agencies form policy-making coalitions when it helps them activate veto points and exploit collective action problems among their overseers in Congress: namely, committee freeriding in oversight and legislative gridlock in lawmaking. These collective action problems, in turn, inhibit Congressional attempts to overturn bureaucratically led policies and therefore allow agency policies to stick. Agencies form coalitions actively in order to insulate their policies against congressional oversight.

Customer engagement is crucial for success and innovation in digital businesses, but its impact on digital startups, particularly on business performance, is underexplored. This study investigates the relationship between customer-related digitalization factors, engagement, and business performance. Using a cross-sectional survey and Partial Least Squares Structural Equation Modeling, data from 125 startups were analyzed. The findings reveal that digitalization factors, encompassing Data Security, Transparency, Consumer Reviews, and Effective Communication, significantly impact customer engagement and digital business performance. Additionally, customer engagement mediates the relationship between digitalization factors and digital business performance, highlighting its critical role in fostering customer loyalty, communication, and co-creation.

This paper presents a systematic method and coding scheme to convert concept maps into bi-partite graphs that can be computationally evaluated for topological complexity measurements. The coding scheme is focused on splitting concepts with multiple elements embedded and linking these objectively. The guidance for this is established and the method presented with examples. The motivation for this work is to establish a means to objectively compare concept maps generated by individuals at the beginning and the end of an intervention to measure the impact of the intervention. The reliability of the coding scheme is presented in separate work.

Engineering design has recently undergone a paradigm shift led by generative artificial intelligence (AI). The Generative Design (GD) paradigm utilizes generative AI tools (e.g., large language models) to define the objective space and computationally exploit the design space. This is a drastic shift from the roles of human designers in the Traditional Design (TD) paradigm which consists of manual design-objective space co-evolution, and has created a research gap for Generative Design Thinking (GDT): how a designer thinks and cognitively approaches the design process during GD. To fill this gap, we propose the Paradigmatic Design Thinking Model which uniquely defines design thinking as situated within three factors (Design Cognition, Design Tools, and Design Methodology) and use it to explain design thinking in two paradigms: Traditional Design Thinking and Generative Design Thinking.

Creativity is a fundamental aspect of design that can bring us novel and useful products. However, measuring creativity in design can always be challenging as there is a lack of standardized quantification methods and the inherent limitations of mathematical modelling. Previous approaches often rely on human experts to assess design creativity. Still, humans can be subjective and biased in their evaluation procedures. Recent advancements in AI have inspired us to integrate LLMs as evaluators in engineering design. In this study, we utilize LLMs to assess the novelty and usefulness of design ideas. We developed an evaluation procedure and tested it using design samples. Experimental results demonstrate that the proposed method enhances creativity evaluation capabilities across various LLMs and improves the alignment between LLM and human expert assessments.

This paper investigates the role of generative Artificial Intelligence (AI) in academic settings, focusing on its effectiveness in providing feedback during the brainstorming phase of the design process. A controlled study with 25 students (n=25) compared feedback from Generative AI (GPT-4) to that from six human educators. Findings reveal that AI-generated feedback enhances student motivation during ideation and facilitates iterative idea refinement. Generative AI’s ability to deliver rapid, scalable feedback proves advantageous in resource-constrained contexts, supporting more effective design processes. This research highlights the potential for AI-driven feedback mechanisms to transform human-AI collaboration in design education, addressing key challenges in personalized and scalable feedback delivery.

The increasing number of accidents involving electric vehicles (EVs) and pedestrians underscores the need of enhancing pedestrian safety. Autonomous vehicles (AVs), which have been introduced to mitigate traffic injuries caused by human error, still miss pedestrian trust due to the absence of a human driver. To improve pedestrian perceptions, EVs and AVs must integrate communication interfaces. This study administers two questionnaires to assess pedestrians’ emotional responses when crossing in front of EVs and AVs, and their preferences of modes of interaction. Vehicles’ ability to communicate their intentions through visual signals results crucial for pedestrians. Finally, findings regarding signals effectiveness when interacting with EVs and AVs allow for guidelines to emerge for the design of EAVs interfaces, offering valuable insights for the development of such vehicles.

Spatial voting models are widely used in political science to analyze legislators’ preferences and voting behavior. Traditional models assume that legislators’ ideal points are static across different types of votes. This article extends the Bayesian spatial voting model to incorporate hierarchical Bayesian methods, allowing for the identification of covariates that explain differences in legislators’ ideal points across voting domains. We apply this model to procedural and final passage votes in the U.S. House of Representatives from the 93rd through 113th Congresses. Our findings indicate that legislators in the minority party and those representing moderate constituencies are more likely to exhibit different ideal points between procedural and final passage votes. This research advances the methodology of ideal point estimation by simultaneously scaling ideal points and explaining variation in these points, providing a more nuanced understanding of legislative voting behavior.

GenAI has significant potential to transform the design process, driving efficiency and innovation from ideation to testing. However, its integration into professional design workflows faces a gap: designers often lack control over outcomes due to inconsistent results, limited transparency, and unpredictability. This paper introduces a framework to foster human ownership in GenAI-assisted design. Developed through a mixed- methods approach—including a survey of 21 designers and a workshop with 12 experts from product design and architecture—the framework identifies strategies to enhance ownership. It organizes these strategies into source, interaction, and outcome, and maps them across four design phases: define, ideate, deliver, and test. This framework offers actionable insights for responsibly integrating GenAI tools in design practices.

We identify a rise in educational polarization among members of the US Congress mirroring the educational polarization in the American mass public. Over the past half-century, the percentage of Republican representatives who attended elite educational institutions declined from 40% to 15%, and the percentage of similarly educated Republican senators declined from 55% to 35%, while the ranks of elite-educated Democrats rose in both chambers. These changes across the parties have mapped into observable differences in behavior and approaches toward lawmaking. We find that elite-educated legislators are much more liberal in their voting patterns, suggesting a link between the decline in elite-educated Republicans and ideological polarization in Congress. We also demonstrate that, in the House, elite-educated Democrats are especially effective lawmakers, but not so for elite-educated Republicans. In the Senate, we establish a link between the decline of elite-educated Republicans and the rise of partisan warrior “Gingrich Senators.” Overall, these patterns offer initial glimpses into how political elites are being drawn from different educational cohorts, representing an important transition in American governance.

This paper investigates the integration of player profiles and gamification elements into knowledge management practices within communities of practice engaged in engineering design. The study proposes a framework combining the MEREX method with gamification, tailored to Marczewski’s player types. The research aims to personalize knowledge sharing, promote user engagement, and structure engineering design knowledge effectively. The framework leverages MEREX sheets with a narrative format structured around phases of the engineering design process. Additionally, it features personalized knowledge maps and contributor profiles to foster collaboration, facilitate knowledge formalization, and encourage knowledge reuse. This integrated approach seeks to improve both community animation and overall knowledge management within engineering design contexts.