This study examines the integration of values into design methodologies, essential for guiding value-driven design processes. Values, spanning ethical, economic, and functional dimensions, influence decision-making and project outcomes. Through Principal Component Analysis (PCA), five clusters of design methodologies were identified, each addressing distinct aspects of value integration. Interviews with designers highlighted challenges in defining, formalizing, and adapting values due to their inherent subjectivity and volatility. This study, by adopting a values-centered perspective, enriches our understanding of design methodologies and paves the way for more informed methodological choices across various contexts.

Can observing opposing partisans engage in dialogue depolarize Americans at scale? Partisan animosity poses a challenge to democracy in the United States. Direct intergroup contact interventions have shown promise in reducing partisan polarization, but are costly, time-consuming, and sensitive to subtle changes in implementation. Vicarious intergroup contact—observing co-partisans engage with outparty members—offers a possible solution to the drawbacks of direct contact, and could potentially depolarize Americans quickly and at scale. We test this proposition using a pre-registered, placebo-controlled trial with a nationally representative sample of Americans. Using both attitudinal and behavioral measures, we find that a 50-minute documentary showing an intergroup contact workshop reduces polarization and increases interest but not investment in depolarization activities. While we find no evidence that the film mitigates anti-democratic attitudes, it does increase optimism about the survival of democratic institutions. Our findings suggest that vicarious intergroup contact delivered via mass media can be an effective, inexpensive, and scalable way to promote depolarization among Americans.

Various methods, such as LCA, LCC, or circularity indicators, are used to integrate sustainability into product development. However, these approaches often require extensive expertise in both processes and sustainability, which is not always available in combination. This paper introduces a framework based on the double diamond model, structured into (1) a preliminary assessment, (2) a collaborative workshop, and (3) a prioritization process. It aims to help engineers identify sustainability improvements without requiring prior expertise. A case study on an industrial digital printing system identified five opportunities for enhancing sustainability. Three measures were validated using LCA and the RPR metric. The study resulted in seven principles for sustainable printer design, with a lightweight door design, reduced number of rivets, and logistical improvements as key outcomes.

The ability to modify designs, personalize nutrition, and improve food sustainability makes 3D food printing (3DFP) an exciting emerging technology. Food materials’ complex chemistry and mechanics make it difficult to consistently print designs of different shapes. This research uses two methods to assess printed food fidelity: Manual and automated image analysis with custom-developed algorithm. Fidelity based on printed area was measured for three overhang designs (0°, 30°, and 60°) and three food ink mixtures. The manual method provided a baseline for analysis by comparing printed images with CAD images. Both methods showed consistent results with only ±3% differences in analyzing printed design areas. While the computational method offers advantages for efficiency and bias reduction, making it well-suited for fidelity assessment to assess designs.

Natural Language Processing (NLP) has been widely applied in design, particularly for analyzing technical documents like patents and scientific papers to extract engineering design knowledge. This work aims to enhance this process by integrating the Axiomatic Design methodology with NLP techniques applied to patent texts. The objectives are to (1) extract Functional requirements (FRs) and Design parameters (DPs), and (2) identify how FRs and DPs are related in text (Axiomatic relations). The second objective is particularly challenging due to limited focus on understanding semantic relations in literature, and previous studies often extract Axiomatic relations in an unstructured way. The approach achieves 60% precision for the first objective and 30-50% for the second. Moreover, a case study shows the practical application of this methodology to assist the work of designers.

Assumption-making is a critical cognitive process in design, where incomplete information is ever-present. Understanding how assumptions are formed, maintained, and adapted can offer key insights into decision-making. While theoretical explorations of assumptions exist, empirical research remains limited. This pilot study investigates how varying temporal constraints influence assumption-making while solving ill-structured problems. The challenge lies in isolating the temporal and cognitive factors at play. The early insights reveal that task ambiguity, contextual framing, and time constraints play significant roles in shaping responses, highlighting the dual nature of assumption-making as both adaptable and resistant to change. The insights highlight the importance of strategic task design that balances ambiguity and structure to deepen our understanding of assumption-making.

Product development is critical for sustainable development, yet sustainable design practices remain under-implemented in the industry. This paper explores the aerospace sector, addressing its specific barriers and enablers to sustainable design. Through a comprehensive literature review, group discussions, and expert group interviews, this study introduces an impact model with essential elements for enabling sustainable product development in aerospace and explains their causal relations. Five key elements were identified: business drive, sustainability implementation, knowledge, ownership, and collaboration. In addition to the impact model, the paper discusses aerospace-specific challenges and opportunities for sustainable product development. Findings from this study offer a practical framework for practitioners and researchers to plan and implement interventions in organizations.

Virtual Reality (VR) has garnered significant attention as a potential ‘empathy machine’ for its ability to simulate firsthand experiences of others’ perspectives. However, recent research reveals conflicting evidence regarding VR’s effectiveness in fostering empathy, with outcomes ranging from strong positive effects to complete ineffectiveness. By analyzing both subjective experiences and objective measures, this study aims to elucidate the relationship between VR design and human empathy, addressing three prevalent perspectives on the field’s inconsistencies: flawed mechanisms, ineffective design, and mismatched methodology. The findings contribute to the theoretical understanding of empathic VR and provide practical implications for designing effective VR-based empathy interventions in engineering contexts.

sEMG biofeedback therapy can be used to treat arm paresis after a stroke by using surface mounted EMG electrodes to measure muscle activity in the forearm and provide visual feedback to the patient. Since current sEMG biofeedback systems rely on manual placement of a few large electrodes, they cannot be used to discriminate between individual extrinsic finger muscle activities, which is necessary for training everyday hand movements. In this paper, we present our concept for the development of a device that enables the resolution of individual finger activities. We have developed a method that uses and reduces information from large-scale sEMG scans of a person's forearm to identify suitable locations for the strategic placement of a minimal number of electrodes in a personalised forearm sleeve, which is the key component of an effective biofeedback device for everyday hand movements.

This article explores the use of large language models (LLMs), specifically GPT, for enhancing information extraction from unstructured text in political science research. By automating the retrieval of explicit details from sources including historical documents, meeting minutes, news articles, and unstructured search results, GPT significantly reduces the time and resources required for data collection. The study highlights how GPT complements human research assistants, combining automated efficiency with human oversight to improve the reliability and depth of research. This integration not only makes comprehensive data collection more accessible; it also increases the overall research efficiency and scope of research. The article highlights GPT’s unique capabilities in information extraction and its potential to advance empirical research in the field. Additionally, we discuss ethical concerns related to student employment, privacy, bias, and environmental impact associated with the use of LLMs.

Text-to-Image Generative AI (GenAI) platforms offer designers new opportunities for inspiration-seeking and concept generation, marking a significant shift from traditional visualisation approaches like sketching. This study investigates how designers work with text-to-image GenAI during inspiration-seeking and ideation, aiming to characterise designers’ behaviours through designer-GenAI interaction data. Analysis of 503 prompts by four designers engaging in a GenAI supported design task identifies two distinct behaviours: exploratory, characterised by short, diverse prompts with low similarity; and narrowing, characterised by longer, high-similarity prompts used with detail focused variation functions. The findings highlight the value of GenAI interaction data to reveal patterns in designers’ behaviours, offering insights into how these tools support designers and inform best practices.

Requirements engineering is in the design process, translating stakeholder needs into actionable and well-defined specifications. While existing design enablers and tools provide partial solutions, they often fall short in addressing essential aspects such as real-time feedback, lifecycle management, and the use of controlled vocabularies. To bridge these gaps, the Requirements Authoring Design Enabler (RADE), a macro-enabled Excel tool, is presented to support requirement authoring, tracking, and management. RADE integrates features like automated feedback, a dual-mode interface, robust change tracking, and controlled vocabularies. The tool was tested with pre-service engineers with user feedback informing iterative refinements. RADE addresses key challenges in requirements engineering, demonstrating its potential to enhance design outcomes across various domains.

Companies in the development of cyber-physical systems are responding to the ever faster changing requirements of their own products by implementing agile methods. Until now, however, there has been a lack of ways to determine the true effects of agile transformation on their own processes to operate them in a targeted manner. This paper presents an impact model that defines factors that can be used to describe process changes and outlines the interdependencies between the individual factors and describes the influence of known agile methods. This allows the benefits of agile methods to be presented transparently and objectively.

Dynamics of a spherical particle and the suspending low-Reynolds-number fluid confined between two concentric spherical walls were studied numerically. We calculated the particle’s hydrodynamic mobilities at various locations in the confined space. It was observed that the mobility is largest near the middle of confined space along the radial direction, and decays as the particle becomes closer to no-slip walls. At a certain confinement level, the maximal mobility occurs near the inner wall. We also calculated the drift velocity of the particle perpendicular to the external force. The magnitude of the drift velocity normalised by the velocity along the external force was found to depend on particle location and the confinement level; it is observed that the maximal drift velocity occurs near the wall. Fluid vortices in the confined space induced by particle motion were observed and analysed. In addition, we studied particle trajectories in the flow when the walls rotate at constant angular velocities. The externally applied force, rotation-induced flow and centrifugal/centripetal force, and particle–wall interaction lead to various modes of particle motion. This work lays the foundation to understand and manipulate particulate transport in microfluidic applications such as intracellular transport and encapsulation technologies.

Interdisciplinary work environments, such as in the engineering of Cyber-Physical Systems (CPS), face significant communication challenges due to the need for collaboration among different engineering domains. This study examines communication comprehensibility within a CPS research project involving 30 researchers from multiple universities. We conducted two surveys to assess the status quo of communication comprehensibility. While most research descriptions are generally understandable, significant barriers exist due to technical terminology and differing epistemic foundations. The study presents a systematic approach to assess communication comprehensibility in interdisciplinary projects and highlights the need for support in enhancing communication. Further data from multiple projects is needed to develop effective communication models for interdisciplinary teams.

Conformal prediction (CP) is a framework that provides uncertainty quantification output as valid marginal coverage for predictive models. At present, the main methods used are divided into Bayesian methods and statistical inference method. Among the statistical inference methods, split, full and adaptive conformal prediction are the basic methods. Although there are numerous variations of these methods, a clear comparison is lacking. In this paper, three basic conformal prediction methods are compared on low-dimensional and high-dimensional dataset to illustrate the advantages and disadvantages of each method. The experiment shows that split conformal prediction performs stable coverage but holds data partition as key issue to solve; Expected coverage could not be achieved by Full conformal though it can decrease the prediction interval; Adaptive conformal prediction faces the quantile distribution deviation of complex model. This paper also illustrate the direction of future research.

This study investigates user engagement and its relationship with the visual aspects of design using a newly designed 3D Tic-Tac-Toe. The research examines user experience factors like cognitive engagement, fun, stress relief, etc., and to analyze their correlation with the design principles found in literature, such as Contrast, Framing, and Balance. 15 teams, comprising 2 players each, from design academic backgrounds, were provided with the game board to play. Researchers observed interactions and challenges, while subsequent surveys captured experience, aesthetics, emotional response, and design principles. The findings reveal the strong and weak correlations amongst the factors and the principles, highlights further prototype refinement. The insights integrate cognitive and emotional dimensions with core principles of design to create engaging and visually satisfying products.

Cyber-physical production systems (CPPS) are responsible for a significant portion of manufacturers’ carbon emissions. Since 80% of product-related environmental impacts are determined at the design stage, there is a need for CPPS manufacturers to focus on decarbonization at the design stage. To date, there is a lack of design-for-decarbonization guidance for CPPS. This paper proposes a procedural framework for the effective selection of decarbonization measures for the design of CPPS. A Decarbonization Wheel is developed to establish a product-specific decarbonization strategy. This tool is linked to a catalogue of decarbonization measures. A measure prioritization logic provides a structure for systematizing selected measures. The framework is validated in the case of an intelligent industrial control valve.

This study investigates the integration of Large Language Models with the TRIZ to improve problem solving and innovation in industrial product development. By combining the structured problem-solving framework of TRIZ with LLMs to process large amounts of data and generate ideas, this hybrid approach seeks to overcome the limitations of traditional TRIZ and optimize solution generation. In a case study conducted in an industrial setting, the effectiveness of this integration was investigated by comparing team-generated solutions with those derived using LLMs and TRIZ-enhanced LLMs. The results show that while LLMs accelerate idea generation and provide practical solutions, the additional structure of TRIZ can provide unique insights, however depending on the application context.