This paper introduces a novel approach to analysing design protocols using a combination of methods. It describes an approach using a synthesis of concept extraction (using an LLM), semantic analysis (using word vectors and conceptual clustering), and network analysis (following graph construction). It suggests that the resulting measures are useful for studying design framing and for aiding qualitative analysis. It demonstrates this technique with data from a study of 17 designers addressing two design problems. The method enables the comparison of designers working on the same problem as well as the study of individual designers’ use of concepts over time during a think-aloud study.

Sensor-integrating, “gentelligent” components allow to “inherit” operational loads-data for design optimisations from one generation to the next. For area-wide acquisition and reliable transmission of this data, wireless sensor networks (WSN) are often used, but small sensor nodes for reconstructing deformations and loads, so-called shape sensing, are rarely considered as well as a methodical development of such sensor nodes. This paper presents the development of a small sensor node in accordance to the VDI 2206 for shape sensing with a prototype with strain gauges, HX711 A/D converters and an Arduino Nano 33 IoT microprocessor. An infrastructured WSN is built and tested on an aluminium part at a test rig. The shape sensing is carried out with three sensor nodes and the deformed shape is displayed on a server-website to demonstrate the functionality of the methodically developed WSN.

Radiotherapy involves applying radiation doses to tumor cells and healthy tissue. To protect healthy tissue, an accessory called a bolus is used. Traditional boluses face issues such as limited adaptability and inconsistencies in radiodensity. This study proposes a low-cost process that uses 3D scans and additive manufacturing (AM) to design and produce custom boluses. The method uses a 3D scanner as an alternative to standard medical image acquisition, processes the images with CAD and mesh optimization, and then manufactures the pieces through additive manufacturing using polylactic acid (PLA) as the printing material. By optimizing the fill percentage, radiodensity was controlled, resulting in boluses that achieved a 65% cost reduction in material and an 81% savings in imaging compared to the traditional method.

Among state-of-the-art research, thermoelectric modules using the Peltier effect are used for general personalized climatization. However, none of the personalized climatization approaches found in literature reviewed the usability for the wearer, let alone in the context of motorcycle driving. This work was aimed at integrating Peltier technology into a motorcycling protective item in such a way that it is functional, perceived as safe, and usable for motorcyclists. Multiple integration options observing the requirements for motorcyclist’s safety equipment were generated. The functionality and usability of the integration approaches, as well as their impacts on perceived safety of the driver were evaluated. This work could serve as a base for future studies addressing user-oriented methodologies for the validation of technical products in the context of motorcycle protective clothing.

Substantial engineering efforts are dedicated to reducing injury risks in crash scenarios during the development of new vehicles. This is achieved by performing crash simulations to optimize the nonlinear behavior of systems. However, the complexity makes their behavior difficult and time-consuming for engineers to understand. To reduce the analysis time, this study introduces a modular framework combining Explainable Artificial Intelligence and Large Language Models (LLM). Shapley Additive Explanation values allow for simulation-wise feature importance attribution and generate a data-driven understanding. An LLM assists by making result data interactively accessible and supports technical report generation. Validated through a real-world vehicle side crash optimization use case, the framework demonstrates enhanced and accessible insights into system behavior within virtual engineering.

How can local governments in developing countries, constrained by limited resources, identify and respond to the most pressing public demands? This paper posits that public deliberative platforms, even those with controlled agendas, can be instrumental in this regard by facilitating communication between local elites and ordinary citizens, thereby leading to an observable uptick in political trust over time. Public deliberation serves two functions: firstly, it highlights shifting societal issues, incentivizing bureaucrats to respond more promptly; and secondly, it generates narratives that temporarily improve the public perception of local governments, even among individuals not directly benefiting from government actions. This study provides evidence consistent with these theoretical implications by examining Chinese topical debate programs, during which local officials engage with citizens and respond to their concerns. Empirical results based on a staggered difference-in-differences design suggest that broadcasting such programs in China produces a prompt increase in citizens’ trust in local officials. Our results demonstrate that public deliberation can yield noticeable outcomes in developing countries, even with controlled agendas and constrained resources.

This study explores the integration of network analysis and CAD/PDM log data to analyze collaboration and activity patterns in a multi-year engineering project. Using logs from a collaborative CAD platform with PDM features, the research examines team interactions and network evolution over time. Key findings reveal that early project stages featured smaller, denser networks, while later stages saw larger, less interconnected structures. Subteam formations were dynamic, with variations in size and number. Individual-level analysis showed that user influence, measured through eigenvector centrality, did not always align with activity volume. This work highlights the potential of CAD/PDM data for understanding collaboration dynamics and lays the groundwork for further studies on team interactions in design processes.

Design activities rely on external representations to offload cognitive effort and communicate ideas. These representations, ranging from sketches to virtual reality (VR), influence cognitive processes and perceptual outcomes. This study investigates the impact of different media representations on brain activity by comparing neural responses to design representations in VR and desktop monitor conditions. Utilizing brain network analyses derived from EEG signals in alpha, beta, gamma, and theta bands, results demonstrate that VR elicits greater cognitive integration and sensory engagement. These patterns suggest that VR facilitates holistic evaluations, while desktop representations support precision-focused tasks. These findings provide actionable guidance for optimizing design media selection based on cognitive objectives and contribute to the emerging design neurocognition field.

People rely on daily interactions with artifacts, greatly influencing their physical, mental, and social well-being. Despite this, current design practices often overlook well-being as a core consideration. Affordance theory, which explains how an artifact’s features enable specific user actions and experiences, offers a promising lens for addressing this gap. This study focuses on assessing affordance mechanisms as a potential tool to support design practices to design for positive well-being outcomes. Using transportation modes as a case study, we interviewed college students to explore how specific mechanisms can contribute to positive or negative well-being outcomes. Findings resulted in 233 examples, which showed trends in mechanisms, modes, and well-being outcomes. Ultimately, this work presents an initial framework for embedding well-being considerations into design.

This paper explores the multifaceted concept of design theories value, challenging traditional views of science and philosophy and proposing a novel framework for evaluation. Through critical analysis, considering design theories like C-K theory, PSI, GDT, and CDP, and insight from the history of science, we establish the need for a new value model of design theories that includes design-related and other general properties such as generativity, robustness, and impact on practice. We adapt a recently developed system value model (SVM) to consider the diverse perspectives of design theory stakeholders. Our framework is tested on the PSI theory, demonstrating its applicability. This paper redefines how we perceive and measure the value of design theories, offering insights that could influence future research and practice in design science.

This systematic literature review comprehensively assesses the risks associated with implementing Industry 4.0/5.0 technologies. It clusters these risks into six groups (strategic, financial, operational, technological, environmental, and sociocultural). Using a PRISMA-guided approach, the analysis of 83 peer-reviewed papers identified 36 unique risks out of a total of 811. The findings reveal critical challenges, including in cybersecurity threats, financial burdens, technological obsolescence, and workforce adaptation. These results provide a structured risk categorization that can assist enterprises, in effectively mitigating risks and aligning their strategies with Industry 4.0/5.0 transitions. This framework closes knowledge gaps and offers actionable insights for a robust and sustainable implementation.

Products need to be developed faster and more efficiently, which is why companies are seeking to leverage the benefits of digitalization. A current trend is the digital twin (DT), which offers many advantages but also involves high development efforts. Research has addressed the use of the DT along the product life cycle (PLC) to compensate for the development effort, but these approaches are often imprecise and not directly applicable in industry. This paper therefore describes how the individual components of the DT can be utilized along the PLC beyond the manufacturing and use phase with a focus on product design. The resulting framework is then illustrated using a case study of a product service system. This article aims to facilitate the use of the DT in industry to improve product design across product generations.

To specify the solution principles of a design, a material selection should be performed already in the concept phase. Based on the design constraints, inappropriate materials are removed using an attribute filter. Brittle materials are often removed using fracture toughness attribute limits, but this does not take into account the strength specific stress level and incorrectly excludes entire classes of materials. We propose a novel filtering method to account for brittle failure in material selection. Based on linear elastic fracture mechanics, we establish a relationship that correctly describes the transition between brittle and ductile materials. Representing the proposed filter on an Ashby plot, we evaluate its effect on the further material selection process. Additionally, we show how different defect sizes in the materials can be incorporated into the filtering process.

This paper presents a case study within a small manufacturing company, engaging in the early phases of co-designing Mixed Reality assembly instructions. Using generative tools through two co-design workshops, we engaged the participants in reflecting, visualising and defining their processes, needs, challenges and future ways of working in relation to new technological applications. We gained insights into the participants’ current practices and identified areas where new technologies could improve these practices. We co-designed a lock assembly instruction paper prototype to use as support for future MR development, focused on their apprenticeship training. We also uncovered other areas of technological implementation, setting the framework for co-designing a customised production system.

Facing increasingly dynamic market environments and global challenges such as climate change and resource scarcity, companies are under constant pressure to innovate and remain competitive. As technology is a key enabler, companies need to understand the drivers of technological change. Technology Foresight systematically identifies and analyzes emerging technologies to support engineering design decisions. However, the growing volume of data is outpacing manual processing capabilities. This research explores the integration of Generative AI to enhance Technology Foresight by automating technology analysis and information synthesis. This paper presents a comprehensive problem analysis, reviews existing solutions, and proposes a framework that demonstrates the potential of Large Language Models combined with a Retrieval Augmented Generation architecture to transform Technology Foresight.

This introduction to a special issue of BJHS concerned with intermedial approaches to the history of the public culture of science (those that pay attention to the forms of different science media and how they relate to each other) also stands as an argument for such approaches. It amplifies a trend within humanities and social-science approaches to its subject of studying the interactions between science, media and publics as complex historical phenomena – in comparison with evaluative research approaches that seek to make science communication more effective. It argues for the virtues of going beyond most existing scholarship in the field by considering many media together. Drawing on the work of media studies scholars Irina Rajewsky and Klaus Bruhn Jensen, it introduces working definitions of intermediality. It then explores historically the genealogies of intermediality, which emerges as an entanglement of changing disciplines, technological change and media practice. Two brief sections take the example of museum display in this intermedial context with the aim of showing first that museum practice was already intermedial before it was considered to be ‘one of the media’. It then concludes by showing how, and in what circumstances, the mediatization of museums came to seem necessary.

This article presents a print history of the International African Service Bureau journal International African Opinion and its little-known editor Ras T. Makonnen. In doing so, it makes the case for a reassessment of how we think about anti-colonial movements in interwar Britain. It argues that Pan-Africanism can be viewed as a loose network of anti-colonial activists, where political ideas were fluid and often in competition with one another, yet still operated harmoniously under the wider banner of Pan-Africanism. By analysing the place of print in this competition it demonstrates the role of the history of print within wider histories of empire and anti-colonialism, as well as functions as an engagement with Black British history and histories of Black internationalism.

How and why do armed groups that become known as “rebels” initially use violence? New datasets show that such violence is often small in scale. Numerous empirical examples indicate that it is also often ambiguous—not easily identified as a precursor to anti-state rebellion. This paper seeks to explain these patterns. We argue that a variety of fledgling nonstate armed groups find small-scale, anonymous anti-state violence useful, despite the risks. Therefore, armed groups that later become distinguishable as “rebels” or “bandits” often initially use this similar repertoire of violence. The resulting ambiguity of this violence—for outsiders from states to scholars—presents an opportunity for aspiring rebels, since states struggle to discern the threat they pose. Ambiguity lessens when aspiring rebels opt to use offensive, larger-scale violence. We illustrate our claims with three historical case studies that enable close examination of early armed group violence, as well as 12 brief case vignettes. Our analyses show the promise of integrating research on rebel origins, criminality, and state formation.

The intersection of design and narrative plays a crucial role in shaping meaningful experiences. While narrative experience has been explored in product design, its role in service design remains underdeveloped. This study introduces a narrative-driven service design approach, integrating narrative to enhance user experiences. Using a Research through Design methodology, ten digital service prototypes were developed, embedding “stories of moments of joy” as a design foundation. Findings suggest that starting with narratives fosters deeper emotional engagement and enhances service interactions. Participant feedback highlights how this approach provides an alternative to traditional problem-solving models, emphasizing narrative-driven innovation in service design. By positioning narrative as a central design element, this study contributes to advancing service design methodologies.

Recent advancements in machine learning (ML) offer substantial potential for enhancing product development. However, adoption in companies remains limited due to challenges in framing domain-specific problems as ML tasks and selecting suitable ML algorithms, requiring expertise often lacking. This study investigates the use of large language models (LLMs) as recommender systems for facilitating ML implementation. Using a dataset derived from peer-reviewed publications, the LLMs were evaluated for their ability to recommend ML algorithms for product development-related problems. The results indicate moderate success, with GPT-4o achieving the highest accuracy by recommending suitable ML algorithms in 61% of cases. Key limitations include inaccurate recommendations and challenges in identifying multiple sub-problems. Future research will explore prompt engineering to improve performance.