Mixed reality prototypes are used for applications like design, analysis, and training. They combine high-fidelity overlays on low-fidelity tangible prototypes, giving users physical interactions in virtual environments. Suitable virtual environments are crucial in taking full advantage of these prototypes. However, there is a lack of guidance in the literature on choosing environment reconstruction methods for various applications. The rapid advancements in this area necessitate the characterisation of the reconstruction methods. This paper thus presents a novel knowledge framework for mapping the reconstruction methods with the requirements of MR prototype applications. The aim of the proposed framework is to help designers and engineers make informed decisions. The effectiveness of the framework has been illustrated using five reconstruction methods and testing via four case studies.

The effectiveness of robotic grippers is critical for the secure and damage-free manipulation of objects with diverse geometries and material properties. This paper presents the design, analysis, and experimental evaluation of a novel reconfigurable four-finger robotic gripper. The proposed design incorporates two stationary fingers fixed to a circular base and two movable fingers repositioned and reoriented via a face gear mechanism, enabling multiple finger configurations to enhance adaptability. A single geared motor drives the opening and closing motions of all four fingers, simplifying the actuation mechanism. The robotic gripper was fabricated using 3D printing technology, ensuring cost-effective and precise manufacturing. Experimental tests were conducted to evaluate the robotic gripper’s reconfigurability and grasping performance across a range of objects, demonstrating its effectiveness in various configurations. Additionally, a closed-loop force control system was implemented to assess the grasping performance of a soft reconfigurable variant. Grasping force measurements were performed on three distinct objects, yielding a grasping curve that confirmed successful adaptation and secure handling. While the results validate the robotic gripper’s performance, further refinement of the control algorithm is recommended to optimize its capabilities. Compared to conventional three-finger designs, the proposed robotic gripper offers superior reconfigurability and adaptability, making it suitable for a broader range of industrial and research applications. The innovative face gear mechanism and modular design expand the robotic gripper’s functionality, positioning it as a versatile tool for advanced robotic manipulation tasks.

We explore the role of aesthetic interaction in controlling music playback control and its influence on user experience. Three music playback control designs of different aesthetic interactions were developed and prototyped. An experiment was conducted to measure how their experiences varied regarding aesthetic interaction. Participant responses were then gathered through PrEmo that measured the influence on emotions and user experience, Results indicate how each aesthetic interaction evoked particular emotions and experiences. The aesthetic interaction of music playback control was shown to influence the participants’ appraisal of their music-listening experiences significantly. Findings contribute to a better understanding of how aesthetic interaction in the music listening experience implicates the user’s affective response.

Advances in animal sentience research, neural organoids, and artificial intelligence reinforce the relevance of justifying attributions of consciousness to nonstandard systems. Clarifying the argumentative structure behind these attributions is important for evaluating their validity. This article addresses this issue, concluding that analogical abduction—a form of reasoning combining analogical and abductive elements—is the strongest method for extrapolating consciousness from humans to nonstandard systems. We argue that the argument from analogy and inference to the best explanation, individually taken, do not meet the criteria for successful extrapolations, whereas analogical abduction offers a promising approach despite limitations in current consciousness science.

This study evaluates a framework for translating user scenarios into engineering specifications with AI integration. We conducted workshops with AI-assisted and non-AI teams to assess AI’s impact on usability, efficiency, and collaboration. We collected data through surveys, interviews, and observations. Results indicate the framework is moderately to highly usable. While AI improved efficiency, it did not enhance output comprehensiveness or collaboration. Information overload and limited contextual understanding hindered AI integration. The study highlights AI’s potential as a technical consultant and interdisciplinary bridge, emphasizing the need for domain-specific training and enhanced interactivity capabilities to optimize human-AI collaboration. These findings underscore AI’s role in engineering design, contributing to scalable methods for developing user-centered specifications.

Enlargement is seemingly back on the list of EU priorities. It took a war for it to happen! Indeed, it has been over a decade since the last EU enlargement with Croatia. Other Western Balkan countries have been (im)patiently queuing in the EU’s foyer, waiting for Godot, who never comes despite the promises of his arrival. Meanwhile, Türkiye, an EU candidate country since 1999, dropped out of the league, being almost wiped off the enlargement map after the stalemate in its negotiations with the Union. The EU’s progress with the Western Balkan countries – for which the European perspective was unequivocally confirmed over two decades ago – has stalled due to various issues, including unresolved bilateral disputes. Yet, requesting settlement of bilateral disputes that fall outside the scope of EU law and the jurisdiction of EU courts to reach solutions that can be rejected once the candidate country becomes an EU Member State is rather vain. The current situation is disheartening, underscoring the necessity for a revised approach in the pre-accession process that promotes solidarity, peaceful coexistence, and genuine friendship among states.

Political scientists lack a generally accepted definition of bargaining complexity, and attempts to quantify the complexity of political negotiations as such are rare. We argue that bargaining complexity is best defined as the amount of choice facing the negotiating actors, and best operationalized as the entropy of the probability distribution across potential bargaining outcomes. We apply this general approach to 343 government formation processes in advanced democracies, predicting the selection probability of each potential government using a state-of-the-art government formation model that integrates both arithmetic factors based on the number and size of parties and interparty relations, such as ideological dispersion and pre-electoral coalitions. We then demonstrate how to use our measure to disentangle between different determinants of bargaining complexity. Lastly, we show that bargaining complexity is robustly related to how many potential governments and partners were considered but ultimately set aside during negotiations and to the resulting cabinet’s durability.

The engineering design community has increased their focus on sustainable development, which has resulted in design methodologies and optimization techniques for the design of socio-technical systems involving engineered products. An essential part of design for sustainable development is understanding the social impacts that technology has on people. Social impact diffusion can model how these impacts propagate through society. This paper combines social impact diffusion models, graph-based socio-technical representations, and computational optimization techniques to present a social impact diffusion objective function for optimizing social impact in socio-technical systems. The results of the paper indicate that using social impact diffusion objective functions can improve upon random or best guess designs for socio-technical systems.

The environmental impacts generated by manufacturing processes have become a concern, as underlined by regulation controls. Studies tend to focus on optimization of the processes through process parameter refinement to try to reduce energy consumption and raw material consumption. However, a thorough assessment of the building of a component linked to its use should be performed to help decision making. The focus of this paper is to define a methodology that helps the choice of the process parameters since the first design steps, by assessing this choice on the mechanical properties and thus the global environmental impact of the manufactured component. To do so, a case study is applied to a given additive manufacturing technology combining metal injection molding and fused filament fabrication. This combination is part of the additive manufacturing processes involving material extrusion.

This paper provides a design solution to the existing problem of using eye trackers for large screens. Traditional eye trackers are limited to commercial and smaller-sized screens. However, as larger screens become increasingly popular and essential for various tasks, their impact needs further investigation in user performance and behavioral studies. This work introduces a design approach for adjustable guide rail system to make moving an eye tracker along with the user's head position possible. The testing results showcase robust, accurate and functions under varying real-world conditions, making it ideal for Human-Computer Interaction and User Experience Research. The Guide Rail design employed by this system is easy to manufacture and incorporates 3D printed parts making it easily reproducible and open for customization.

The NIH’s Clinical and Translational Science Award (CTSA) program has placed greater emphasis on Continuous Quality Improvement (CQI) in recent years. Our institution’s CTSA-supported Institute for Clinical and Translational Research (ICTR) implemented a novel CQI process in response. This manuscript shares lessons learned from our implementation, reflecting a paradigm shift from managing an “evaluation program” to creating a process whose central goal is CQI. Our objective is to share these reflections to support other CTSA hubs’ efforts to successfully implement CQI programs. Key elements of our implementation included (1) establishing a shared understanding about CQI’s purpose; (2) leveraging a centralized management approach while addressing barriers to implementation; and (3) creating structures that foster collaboration. The CQI framework we chose, FACE (Focus, Analyze, Change, Evaluate), enabled us not only to improve the activities of ICTR modules but also, over time, to refine the CQI process itself. Through regular convenings of module leaders, the ICTR has sought to cultivate a culture of CQI as a dynamic, participatory process that supports mutual learning and collective problem-solving.

eHealth systems, such as digital care applications or remote monitoring devices, can improve health outcomes using user-centered design principles to create medical devices that adapt to users’ needs and contexts. Data-enabled design (DED) builds on these principles by leveraging user-generated data to iteratively refine systems based on real-world use, enabling adaptive and context-sensitive solutions. However, its exploratory and iterative nature conflicts with the rigid protocols required in clinical trials to evaluate safety and effectiveness. This study revises DED in alignment with clinical trial requirements, identifying four key challenges and proposing a four-phase Clinical Data-Enabled Design (C-DED) framework. This framework reconciles exploratory design with trial methodological demands, supporting the development of safe, effective, and user-centered eHealth medical devices.

Complex machines are increasingly expensive to develop and build, which causes many to be maintained in service for longer than initially designed, as they still effectively perform valuable tasks. Longlasting, effective service lives of centuries rather than decades are a valuable characteristic for certain machines in several industries, whether for continual service, extended storage, or extremely remote deployment, such as in military service, agriculture and space exploration. Although there are various archival publications that focus on longevity, we seek to identify product architecture decisions which impact a machine’s longevity and can then be extrapolated out for timescales greater than 100 years. We refer to this as hyper-longevity. This paper seeks to find patterns in the literature that can identify causes linked to longevity effects, their frequency in the literature, and the types of impacts they have in facilitating longevity.

The objective of this research is to compare the requirements generated by human participants and large language models (LLMs). Requirements are statements that capture the needs and desires from stakeholders and organize them into design parameters. These statements are expressed in natural language which may lead to incompleteness and ambiguity. Due to the recent advancements in the natural language model such as ChatGPT and Gemini as a tool for requirement generation, this study investigates the quantity, variety and completeness of requirements generated by 66 pre-service engineers and 4 LLMs. This is because in some design projects, stakeholder access may be limited. The results show that pre-service engineers outperformed LLMs in variety, quantity and completeness. Future work could involve developing and comparing true human personas to LLMs.

Multi-word expressions (MWEs) are fixed, conventional strings of language (e.g. idioms, collocations, binomials, proverbs) which have been found to be widespread in language use. Research has shown that MWEs exhibit an online processing advantage over control phrases by first language (L1) and second language (L2) speakers. While this line of research has helped us better understand the nature of MWEs and factors that may influence their processing in real time, there remain several gaps that future research should focus on. In this piece, we focus on four main topics related to the online processing of MWEs: (1) comprehension of MWEs by L1 and L2 speakers, (2) production of MWEs by L1 and L2 speakers, (3) the processing of modified MWEs by L1 and L2 speakers, and (4) the processing of MWEs by L1 children. Under each topic, we propose nine research tasks that will further advance our understanding of MWE processing in real time. We conclude with relevance of MWE processing research to L2 teaching and learning.

This research examines, during the human-AI interaction process, how generative AI’s depiction of human bodies reflects and perpetuates able-bodied norms, positioning disabled or grotesque bodies as “errors.” Through a feminist and disability studies lens and employing archival research and visual analysis, this research challenges traditional notions of bodily normativity, advocating for inclusivity in AI-generated imagery. It underscores how labeling nonconformity as an error perpetuates able-bodied standards while erasing the visibility and autonomy of disabled bodies. By critiquing generative AI’s role in reinforcing societal norms, this study calls for reimagining human-AI interactions with a shift in perception and advocates for an approach that neither devalues nor excludes disabled bodies.

I use Swampman to illuminate the role of thought experiments in philosophy of science. Against Millikan and others, I argue that even outlandish thought experiments can shed light on science and scientific kinds, so long as we understand them as illustrations of scientific reasoning, not as examples of scientific kinds. The logic of thought experiments, understood as illustrations, is analogous to the logic of common experimental paradigms in science. So, in reviving Swampman and showing how he survives teleosemantic objections, I also provide a framework for understanding how, why, and when thought experiments are informative about science and scientific kinds.

This study explores the impact of new work practices on product development in an Engineering Simulator by comparing traditional practices with new ways of working in a compressed product development process. By introducing flexibility, digital tools, and autonomy, the study highlights improvements in individual productivity and innovation. For example, teams employing new work practices developed their first prototypes 30% faster than control groups. However, challenges in communication and team dynamics emerge, underscoring the need for structured support systems. The findings further suggest that while these modern practices foster creativity and efficiency, successful implementation at the organizational level requires balancing autonomy with clear guidelines and effective management. This study provides actionable insights for adapting new work methods to engineering environments.

Food production systems are shaped by external factors, such as social events and economic shifts, which influence and are influenced by labour dynamics—e.g., workforce availability—and human factors—e.g., worker skills. Using a systems approach, this paper explores how labour shortages impacting worker teams—such as in terms of mixture of availability, skills, and human behaviours—affect production and quality. UK apple harvesting is chosen as a case study due to its reliance on skilled seasonal migrant workers. Findings highlight the need for strategies such as upskilling local workers, enhancing training programmes, and adopting new technologies to mitigate labour shortages and enable high-performance collaborative worker groups.

The rise of mindfulness apps has integrated these tools into daily life, but concerns arise about preserving traditional practices and the ethical use of manipulative dark patterns that undermine user autonomy. This study examines the impact of dark patterns on user perceptions, engagement, and trust in mindfulness apps using expert reviews, surveys, journaling, and interviews. Three apps—Calm, Headspace, and Insight Timer—were analyzed for dark patterns, with participants documenting their experiences and perceptions. The findings underscore the need for ethical design practices to enhance trust and informed decision-making while highlighting the influence of dark patterns on user behavior and experience.