Results
- Showing results for:
- Reset all filters
Search results
-
Conference paperHerron MT, Kohler M, Nieri T, et al., 2026,
Tear-able to Wearable: Exploring End-of-Life Pathways for E-Textiles
Electronic textiles (e-textiles) represent a growing area in HCI, yet their end-of-life remains largely underexplored, leaving no established pathways for addressing this emerging waste stream. This study represents the first step in an ongoing research program exploring recycling possibilities for e-textiles. This work examines the post-disassembly potential of conductive textile substrates to explore whether these materials retain functional value and if they can be reintegrated into new interactive systems. Using commercially available conductive woven fabrics, we apply mechanical recycling techniques adapted from traditional textile processing to produce new nonwoven materials suitable for medium-pressure, low-resolution piezoresistive sensing. Through electromechanical characterization, we identify both the opportunities and limitations of this approach.
-
Conference paperLissillour O, Deterding S, Evans A, 2026,
What’s the point? How users functionalise points in gamified systems
, New York, 2026 CHI Conference on Human Factors in Computing Systems (CHI ’26), Publisher: ACM, Pages: 1-17Points are widely used design elements in gamified systems. Yet how they motivate is still unclear: what motivational meaning or functional significance do users ascribe to points and when? To answer this question, we conducted a semi-structured interview study with 27 users of two popular gamified platforms, Duolingo and Habitica. Through reflexive thematic analysis, we constructed six different types of functionalisation variously proposed in prior gamification and personal informatics work but often not empirically supported. We highlight the importance of functional design detail (such as points should proportionally reward effort) and derive design guidelines.
-
Conference paperDavison M, McPherson A, 2026,
Design Explorations of Instruments and Interactions with Bidirectional Haptic Couplings
, CHI Conference on Human Factors in Computing SystemsDirect interaction with digital synthesisers using audio signals can offer opportunities for intimate and nuanced interaction in digital musical instrument designs. Unlike acoustic instruments, these hybrid instruments tend to follow a unidirectional interaction structure: tactile gestures generate audio signals that are fed into a synthesiser, but there is no vibrotactile feedback from the instrument back to the musician. This paper presents the HaptiCoupler system that enables bidirectional tactile interaction with digital musical instruments using a single voice coil transducer. A study is undertaken with experienced digital musical instrument designers to explore the design implications of introducing closely coupled, collocated haptic feedback in musical systems. The potential creative implications for designers are discussed.
-
Conference paperWan E, Yin C, Ito A, et al., 2026,
KNIT: Computational BoundaryObjects for Real-Time Convergence in Interdisciplinary Teams
, chi -
Conference paperZhang Z, Peters D, Xiao L, et al., 2026,
Understanding Workplace Relatedness Support among Healthcare Professionals: A Four-Layer Model and Implications for Technology Design
Healthcare professionals (HCPs) face increasing occupational stress and burnout. Supporting HCPs' need for relatedness is fundamental to their psychological wellbeing and resilience. However, how technologies could support HCPs' relatedness in the workplace remains less explored. This study incorporated semi-structured interviews (n = 15) and co-design workshops (n = 21) with HCPs working in the UK National Health Service (NHS), to explore their current practices and preferences for workplace relatedness support, and how technology could be utilized to benefit relatedness. Qualitative analysis yielded a four-layer model of HCPs' relatedness need, which includes Informal Interactions, Camaraderie and Bond, Community and Organizational Care, and Shared Identity. Workshops generated eight design concepts (e.g., Playful Encounter, Collocated Action, and Memories and Stories) that operationalize the four relatedness need layers. We conclude by highlighting the theoretical relevance, practical design implications, and the necessity to strengthen relatedness support for HCPs in the era of digitalization and artificial intelligence.
-
Journal articleFalconer T, Kazempour J, Pinson P, 2026,
Toward Replication-Robust Analytics Markets
, Informs Journal on Data Science, Vol: 5, Pages: 155-170, ISSN: 2694-4022Despite recent advancements in machine learning, in practice, relevant data sets are often distributed among market competitors who are reluctant to share. To incentivize data sharing, recent works propose analytics markets where multiple agents share features and are rewarded for improving the predictions of others. These rewards can be computed by treating features as players in a coalitional game, with solution concepts that yield desirable market properties. However, this setup incites agents to strategically replicate their data and act under multiple false identities to increase their own revenue and diminish that of others, limiting the viability of such markets in practice. In this work, we develop an analytics market robust to such strategic replication for supervised learning problems. We adopt Pearl’s do-calculus from causal inference to refine the coalitional game by differentiating between observational and interventional conditional probabilities. As a result, we derive rewards that are replication robust by design.
-
Journal articleArranz CFA, Arroyabe MF, Demirel P, et al., 2026,
Reconciling circular economy and net zero: firm capabilities to resolve sustainability tensions
, British Journal of Management, Vol: 37, ISSN: 1045-3172Achieving net zero has become a key concern for firms to address climate change, yet growing evidence suggests that reducing reliance on fossil fuels for energy generation alone is insufficient. As material use is increasingly recognized as a significant source of greenhouse gas emissions, the circular economy has emerged as a potential pathway to support decarbonization. Despite the intuitive appeal of aligning circularity with net zero, their relationship remains conceptually underexplored and empirically ambiguous. To address this complexity, this article develops a framework conceptualizing the interaction between circular economy and net zero as a dynamic interplay of virtuous and vicious cycles. Drawing on paradox and capability perspectives, it explains when circular practices reinforce decarbonization and when they generate capability traps that undermine environmental performance. The framework contributes to corporate sustainability scholarship by identifying the capabilities that enable firms and their ecosystems to transform tensions into synergies, thereby supporting more coherent strategies and policy interventions at the intersection of circularity and net zero.
-
Journal articleCohen AE, Degnan-Morgenstern S, Daubner S, et al., 2026,
Differentiable learning and control of free-energy-driven pattern dynamics
, Physical Review Research, Vol: 8, ISSN: 2643-1564Pattern-forming dynamics govern the behavior of many quantum, electrochemical, and soft-matter systems, yet learning and controlling the corresponding partial differential equation (PDE) models on realistic geometries remains challenging. In this work, we develop a unified, differentiable framework for free-energy-based PDEs that enables end-to-end parameter inference and optimal control directly on image-based domains. Starting from a variational description in terms of an energy or free energy functional, we combine PDE-based smoothing of segmented geometries, the smoothed boundary method, advanced integrators for stiff pattern-forming PDEs, and memory-efficient automatic differentiation implemented in JAX to construct scalable PDE solvers amenable to gradient-based optimization. We demonstrate the capabilities of this framework across four classes of applications: (1) learning Cahn-Hilliard and Allen-Cahn free energies and kinetic laws from noisy spatiotemporal data on complex battery electrode microstructures; (2) designing time-dependent wetting boundary conditions that steer phase boundaries to desired orientations; (3) optimizing spatially varying reaction rates, interpreted as surface coatings, to suppress phase separation in intercalation electrode models; and (4) computing time-dependent trapping potentials that transfer Bose-Einstein condensates between ground states of qualitatively different Gross-Pitaevskii potentials while minimizing excitations. Together, these results show that variational PDE models, when equipped with differentiable solvers on complex domains, provide a versatile substrate for data-driven discovery, design, and control of pattern-forming materials and quantum fluids, and they point toward tighter integration of physics-based PDE modeling with modern optimization and machine learning pipelines.
-
Journal articleDocherty R, Vamvakeros A, Cooper SJ, 2026,
Upsampling DINOv2 Features for Unsupervised Vision Tasks and Weakly Supervised Materials Segmentation
, Advanced Intelligent Systems, Vol: 8The features of self-supervised vision transformers (ViTs) contain strong semantic and positional information relevant to downstream tasks like object localization and segmentation. Recent works combine these features with traditional methods like clustering, graph partitioning or region correlations to achieve impressive baselines without finetuning or training additional networks. Upsampled features are leveraged from ViT networks (e.g., DINOv2) in two workflows: in a clustering-based approach for object localization and segmentation and paired with standard classifiers in weakly supervised materials segmentation. Both show strong performance on benchmarks, especially in weakly supervised segmentation where the ViT features capture complex relationships inaccessible to classical approaches. It is expected that the flexibility and generalizability of these features will both speed up and strengthen materials characterization, from segmentation to property-prediction.
-
Journal articleBevan PA, BanksLeite C, Kovac M, et al., 2026,
Robotics‐assisted acoustic surveys could deliver reliable, landscape‐level biodiversity insights
, Remote Sensing in Ecology and Conservation, Vol: 12, Pages: 260-274, ISSN: 2056-3485Terrestrial remote sensing approaches, such as acoustic monitoring, deliver finely resolved and reliable biodiversity data. However, the scalability of surveys is often limited by the effort, time and cost needed to deploy, maintain and retrieve sensors. Autonomous unmanned aerial vehicles (UAVs, or drones) are emerging as a promising tool for fully autonomous data collection, but there is considerable scope for their further use in ecology. In this study, we explored whether a novel approach to UAV-based acoustic monitoring could detect biodiversity patterns across a varied tropical landscape in Costa Rica. We simulated surveys of UAVs employing intermittent locomotion-based sampling strategies on an existing dataset of 26,411 h of audio recorded from 341 static sites, with automated detections of 19 bird species (n = 1819) and spider monkey (n = 2977) vocalizations. We varied the number of UAVs deployed in a single survey (sampling intensity) and whether the UAVs move between sites randomly, in a pre-determined route to minimize travel time, or by adaptively responding to real-time detections (sampling strategy), and measured the impact on downstream ecological analyses. We found that avian species detections and spider monkey occupancy were not impacted by sampling strategy, but that sampling intensity had a strong influence on downstream metrics. Whilst our simulated UAV surveys were effective in capturing broad biodiversity trends, such as spider monkey occupancy and avian habitat associations, they were less suited for exhaustive species inventories, with rare species often missed at low sampling intensities. As autonomous UAV systems and acoustic AI analyses become more reliable and accessible, our study shows that combining these technologies could deliver valuable biodiversity data at scale.
-
Journal articleMoreschini A, Scandella M, Astolfi A, et al., 2026,
Moment matching by kernel-based learning
, IEEE Transactions on Automatic Control, Vol: 71, Pages: 2123-2138, ISSN: 0018-9286In this article, we introduce a kernel-based moment matching theory that relies upon a novel data-driven model reduction method, employing the estimation of moments within a reproducing kernel Hilbert space. We demonstrate that moment estimation can be enhanced by appropriately tuning the regularization term, regardless of the kernel choice. In addition, we present conditions to ensure that the reproducing kernel Hilbert space contains only functions, which are bona fide moments. While exact moment matching with finite data is impractical in this scenario, we introduce the concepts of weak moment matching and moment matching almost everywhere onto the L2-space. In addition, we address scenarios in which the dataset contains noisy measurements of outputs that are not yet in a steady state, which typically biases the estimation due to the effect of the output transients. We further prove that estimating over a reproducing kernel Hilbert space can ensure weak moment matching asymptotically and, with additional assumptions, also moment matching almost everywhere despite these transients. Finally, we provide a probabilistic bound that guarantees weak moment matching for an arbitrarily finite amount of data.
-
Journal articleVillejoubert L, Picinali L, Faulkner K, et al., 2026,
Effect of frequency-to-place mismatch on speech and music sound quality in acoustic cochlear implant simulation.
, J Acoust Soc Am, Vol: 159, Pages: 3358-3371Sound quality perception for cochlear implant (CI) users has become increasingly important. Although many CI users achieve near-normal speech recognition in quiet, they often report poor sound quality, particularly for music. One factor contributing to this degradation is frequency-to-place mismatch (FTPM), which occurs when electrode positions do not align with the cochlea's characteristic frequency map. This study aimed to better understand the impact of FTPM on sound quality in CI simulations across different signal types and configurations. Twenty-three normal-hearing participants were tested (online or onsite) using an adapted MUlti Stimulus test with Hidden Reference and Anchor (MUSHRA) paradigm. Different FTPM configurations were simulated with a noise vocoder to assess their influence on speech and music sound quality. Results showed that greater FTPM caused noticeable degradation, especially when lower frequencies were affected. Variability in FTPM across electrodes also significantly reduced perceived quality. Furthermore, the impact of FTPM depended on the type of stimulus, with speech and music showing distinct sensitivity patterns. Online assessments closely matched onsite results, confirming the reliability of remote testing. Together, these findings clarify why sound quality perception differs between CI users and contexts and highlight new opportunities to develop strategies for alleviating mismatch effects in CIs.
-
Journal articleBatcup C, Almukhtar A, Menon A, et al., 2026,
Barriers and enablers to sustainable anaesthetic practice: a mixed-methods study
, British Journal of Anaesthesia, Vol: 136, Pages: 1190-1201, ISSN: 0007-0912BackgroundAnaesthetic practices contribute significantly to the environmental impact of healthcare. Using local or regional anaesthesia instead of general anaesthesia, and TIVA instead of inhalation anaesthesia, can reduce this impact. This study investigated why general anaesthesia is sometimes used over local and regional anaesthesia, and why inhalation agents are often chosen over TIVA.MethodsWe conducted a mixed-methods study in the UK (June 2023–April 2024), underpinned by the Theoretical Domains Framework. Semi-structured interviews (n=19) with anaesthetists, surgeons, and nurses of differing seniority were analysed using Framework Analysis. A national survey (n=347), distributed via posters and professional networks, was developed from early interview findings. Quantitative data were analysed descriptively and open-text responses were coded using the qualitative framework.ResultsFour key themes were identified: (1) contextual factors affecting anaesthesia decision making; (2) patient differences and preferences; (3) influence of key decision makers on anaesthesia choice; and (4) default practices and lack of confidence in alternatives. These encompassed 17 subthemes and mapped to 9 of 14 Theoretical Domains Framework domains.ConclusionsThis study provides new insights into behavioural influences underlying anaesthetic practice, which can inform the design of interventions to improve the sustainability of anaesthesia, without compromising patient safety and comfort. Addressing systemic and behavioural barriers through dedicated local anaesthesia operating lists, improved patient communication, targeted training, and supportive technologies may enhance efficiency while promoting safe, sustainable, patient-centred practice. Future interventions should be co-designed with surgeons, anaesthetists, and patients to ensure clinical acceptability, feasibility, and sustainability.
-
Journal articleJin X, Gao G, Wang W, et al., 2026,
Human-in-the-Loop Capacitive Microphone Sensors-Based Muscle Sensing System for Predictive and Adaptive Exoskeleton Assistance
, IEEE Robotics and Automation Letters, Vol: 11, Pages: 4657-4664Mobility impairments among older adults and individuals with neuromuscular weakness motivate the need for timely and adaptive exoskeleton assistance. This paper presents a human-in-the-loop muscle sensing and control system based on capacitive microphone sensors (CMS) that capture subtle mechanical muscle vibrations preceding observable motion. CMS signals were shown to occur 20-30 ms earlier than IMU-based kinematics, enabling anticipatory intent detection and feedforward assistive control. A five-sensor CMS array positioned over major thigh muscles is combined with a two-stage control strategy that integrates threshold-based pre-assist triggering and machine-learning-based torque refinement. Experiments across walking, stair ascent, sitting, and standing achieved over 90% classification accuracy under both non-fatigued and fatigued conditions with low latency. Robustness evaluations demonstrate stable CMS performance under realistic wearable perturbations, including perspiration and attachment variation. Extended experimental sessions (1-2 h) and preliminary feedback from five participants indicate comfortable wear and natural interaction. These results highlight the potential of CMS-based anticipatory sensing for practical wearable exoskeleton deployment in daily scenarios.
-
Journal articlePoole KC, With S, Martin V, et al., 2026,
Spatial auditory change detection in listeners with hearing loss.
, Hear Res, Vol: 474Everyday listening relies on the auditory system's ability to automatically monitor background ("non-target") sounds that lie outside the focus of attention to detect new or changing sources. Although change detection is a fundamental aspect of this situational awareness, little is known about how hearing impairment affects this ability. This study examined how variability in sensorineural hearing loss influences spatial auditory change detection. Older hearing-impaired listeners (N = 30) completed a spatial change detection task requiring them to identify the appearance of a new sound source within a complex spatialised acoustic scene. Hearing loss was characterised by three factors measured with standard clinical tests: audiometric hearing thresholds, sensitivity to small level changes, and sensitivity to spectrotemporal modulation. These factors were used to predict reaction time, hit rate, and false alarm rate. Listeners with poorer spectrotemporal sensitivity, higher audiometric hearing thresholds, and older age showed slower and less accurate detection, whereas sensitivity to small level changes did not predict outcomes. Detection also varied with spatial location, where appearing sources from behind were detected more slowly and less accurately than those from the front or sides. Numerical analysis using HRTFs suggested that these rear-field effects are not fully explained by acoustic level differences alone, indicating that attentional factors may play a role. These results reveal that hearing loss, age, and spatial factors jointly shape listeners' ability to monitor dynamic auditory scenes. Additionally, testing spectrotemporal sensitivity offers a promising clinical measure of non-speech auditory processing with relevance for hearing-aid fitting and situational awareness.
-
Journal articleDaubner S, Cohen AE, Dörich B, et al., 2026,
evoxels: A differentiable physics framework for voxel-based microstructure simulations
, Journal of Open Source Software, Vol: 11, Pages: 9733-9733 -
Conference paperWang M, Li Y, Nissen B, et al., 2026,
MenstaRay: A Knitted Soft Wearable Robotic Interface for Somatosensory Communication of Menstrual Experience
, Pages: 902-906MenstaRay is a soft knit robotic interface designed to explore how tactile actuation can support somatosensory communication of menstrual experiences. The prototype was created using a fabrication method for knit-integrated soft wearable robotics with two core structural elements: (1) an extensible EcoFlex 00-10 silicone cavity containing internal air chambers and (2) a strain-limiting textile layer knitted with Spandex Super Stretch Yarn (81% nylon, 19% elastane). This configuration enables regulated inflation patterns that preserve the softness of textiles while providing targeted haptic feedback that is suitable for intimate, safe, and therapeutically appropriate interactions. Through a series of workshops, we investigated and evaluated how these dynamic tactile behaviours shaped participants’ embodied reflections on menstrual sensations. This work contributes to human robotic interaction by introducing MenstaRay, a novel artifact coupled with textile-integrated actuation that can externalize intimate bodily sensations and foster new modes of communicating, reflecting on and representing menstrual experiences through wearable interfaces.
-
Journal articleChilds P, Garvey B, Dieckmann E, et al., 2026,
Futures – scenarios, options and agency – preliminary results
, Design for Augmented Humanity, ISSN: 2977-6481A wide range of methodologies are available for predicting the future such as foresight. Such approaches have been widely deployed by organisations and governments to explore potential developments for purposes of planning, resilience, mitigation and adaptation. The differing methods employ a range of qualitative, quantitative and mixed methodology research tools. The future is subject to dynamic intervention as embodied in innovation and the phrase that ‘if you wish to know the future, design it’. The advent of widespread use of artificial intelligence, robotics, neurotechnology and continuous advance in each of the domains is impacting many if not all aspects of society. This review uses diverse methodologies to explore developments within a defined time horizon, a generation taken as approximately 25 years, focussed on 2050, across a range of domains and topics subject to multi, cross, inter and transdisciplinary practice. Although all domains are considered along with major influences on society, a focus is given to eight domains, medicine, robotics, photonics, materials, AI, space, physics and behavioural science, in particular, as representative examples of changes expected. Major societal and behavioural drivers identified in this presentation of preliminary data from the study include well-being, authenticity and sustainability, the steady influence of established philosophy and religion, emerging social media influences, thinking and developments arising from transcending our planetary boundaries, and the impact of disciplinary boundary morphing approaches on innovation in both established and emerging domains.
-
Journal articleMoradbakhti L, Peters D, Quint JK, et al., 2026,
AI-Enhanced Conversational Agents for Personalized Asthma Support in People With Asthma: Factors for Engagement, Value, and Efficacy in a Cross-Sectional Survey Study.
, JMIR Hum Factors, Vol: 13BACKGROUND: Asthma-related deaths in the United Kingdom are the highest in Europe, and only 30% of patients access basic care. There is a need for alternative approaches to reaching people with asthma to provide health education, self-management support, and better bridges to care. OBJECTIVE: This study aimed to examine patients' interest in using a chatbot for asthma and to identify factors that influence engagement. Automated conversational agents (specifically, mobile chatbots) present opportunities for providing alternative and individually tailored access to health education, self-management support, and risk self-assessment. But would patients engage with a chatbot, and what factors influence engagement? METHODS: We present results from a patient survey (N=1257) developed by a team of asthma clinicians, patients, and technology developers, conducted to identify optimal factors for efficacy, value, and engagement with an asthma chatbot. RESULTS: Results indicate that most adults with asthma (53%) are interested in using a chatbot. The patients most likely to do so are those who believe their asthma is more serious and are less confident in their self-management. Results also indicate enthusiasm for 24/7 access, personalization, and for WhatsApp (Meta) as the preferred access method (compared to app, voice assistant, SMS text messaging, or website). CONCLUSIONS: Obstacles to uptake include security and privacy concerns and skepticism of technological capabilities. We present detailed findings and consolidate these into 7 recommendations for developers to optimize the efficacy of chatbot-based health support.
-
Journal articleAngeliki M, Picinali L, Vicente T, 2026,
A pilot study to assess the challenges and efficacy of two hearing loss simulations
, npj Acoustics, Vol: 2, ISSN: 3005-141XDeveloping accurate and customisable hearing loss (HL) simulations is crucial for understanding and raising awareness of the challenges faced by individuals with HL. This pilot study assesses challenges in perceptually validating two real-time audio effects plugin HL simulations: the 3D Tune-In (3DTI) Toolkit and the Queen Mary University of London (QMUL) plugin. Both simulate common HL deficits, with 3DTI offering greater customization. A pilot listening study was conducted with normal-hearing listeners using simulated HL and listeners with real HL, focusing on mild-to-moderate high-frequency HL. Audiometric tests and psychoacoustic tasks were employed, including gap and tone detection in noise, perceived sound intensity, and intelligibility tests. Results from two real listeners with HL informed adjustments to simulations for normal-hearing participants. Initial findings suggest reasonable accuracy in replicating perceived sound intensity, but variability in spectral resolution, temporal resolution, and intelligibility indicates room for improvement in both implementations. This study highlights the need for enhanced customization to improve accuracy and applicability, offering insight into development challenges. The methodology proved effective, revealing challenges and biases that can occur during testing and emphasising the need for further research, including additional HL listeners, to refine and develop more precise tools for understanding and addressing HL.
-
Journal articleLiu T, Chen Y-Y, Chen K, et al., 2026,
Hierarchical adaptive formation tracking control with uncertain time-varying exosystem
, IEEE Transactions on Automatic Control, ISSN: 0018-9286This paper investigates the formation tracking problem with uncertain time-varying exosystem over a general directed graph. The exosystem describes both the moving target and the disturbances affecting each agent. The dynamics of each agent is described by a parametric strict-feedback form subject to orbit constraints. The so-called congelation of variables method is employed in a hierarchical design to yield an adaptive formation estimator and an adaptive tracking controller. An adaptive coupling gain is integrated into the estimator design, which utilizes the local estimated states and is independent of the communication graph. Boundedness and convergence properties of the resulting adaptive systems are proven. Two simulation results are provided to show the effectiveness of the proposed estimator-controller scheme.
-
Journal articleDaugintis R, Geronazzo M, Poole KC, et al., 2026,
Perceptual evaluation of an auditory model–based similarity metric for head-related transfer functions
, Journal of the Acoustical Society of America, Vol: 159, Pages: 2822-2843, ISSN: 0001-4966A key challenge in binaural spatial audio personalisation is defining perceptual similarity metrics that meaningfully rate non-individual head-related transfer function (HRTF) fit. A metric using Bayesian auditory modelling has recently been proposed to address this. It predicts human localisation performance with non-individual HRTFs by matching their auditory cues to individual cues and selects the best and worst non-individual HRTFs based on predicted localisation errors. We present a perceptual evaluation of this selection with 17 participants using static localisation and dynamic spatial audio quality assessments. Localisation performance was significantly poorer with the model-selected worst HRTF, while the best HRTF did not differ significantly from the individual HRTF for most error metrics. Qualitatively, while participants found the best HRTF to be different from the individual HRTF in terms of overall quality and tone colour, the perceived dissimilarity with the worst HRTF was significantly greater. Cross-experiment analysis revealed a moderate correlation between degradation in localisation performance and perceived differences in these qualities. However, no significant differences in perceived naturalness or externalisation were found between HRTF conditions in an anechoic test environment. Overall, these results support the use of the auditory model-based metric for evaluating non-individual HRTFs.
-
Journal articleAlmukhtar A, Batcup C, Jagannath S, et al., 2026,
Understanding sustainability in operating theatres: an ethnographic study to determine drivers of unsustainable behaviours
, Annals of Surgery Open, Vol: 7, ISSN: 2691-3593Background: Climate change is the biggest threat to human health. Paradoxically, the healthcare sector is a major contributor to climate change, and operating theaters are among the highest sources of emissions. Unsustainable practices are actions that compromise environmental, social, and financial sustainability, leading to unnecessary resource use, avoidable harm to the wider population, and reduced ability to provide effective healthcare in the future. Drivers of unsustainable practices and barriers to sustainability in practice (a top priority identified by the James Lind Alliance Priority Setting Partnership) are unexplored, hindering interventions that can help meet net-zero targets within healthcare. We conducted the first known ethnographic study to investigate behaviors related to sustainability in operating theaters, and their influences on those behaviors to inform the design of effective behavior change interventions.Methods: Nonparticipant ethnographic observations with opportunistic discussions in elective general surgical operating theaters were conducted between June and December 2023 at 2 university hospitals in Central London. Data were collected until saturation using a template developed during the initial observations. Inductive thematic analysis was conducted, with subthemes (influences) deductively mapped to the Theoretical Domains Framework.Results: Twenty-six procedures were observed (42 hours). Unsustainable behaviors included: (1) unnecessary and inappropriate glove use, potentially compromising safety (average 8–10 pairs per operation), (2) incorrect waste disposal, (3) unnecessary package opening, and (4) energy waste. Thematic analysis generated 6 themes and 16 influences (mapped to 9 Theoretical Domains Framework domains). Key themes were that sustainable practices are “infrequent and inconsistent” due to limited awareness (Knowledge) and low environmental concerns (Memory, Attention, and Decision Processes). Unsusta
-
Journal articleXie R, Chen Y, Pinson P, 2026,
Predict-and-Optimize Robust Unit Commitment with Statistical Guarantees via Weight Combination
, IEEE Transactions on Power Systems, Vol: 41, Pages: 1163-1177, ISSN: 0885-8950The growing uncertainty from renewable power and electricity demand brings significant challenges to unit commitment (UC). While various advanced forecasting and optimization methods have been developed to better predict and address this uncertainty, most previous studies treat forecasting and optimization as separate tasks. This separation can lead to suboptimal results due to misalignment between the objectives of the two tasks. To overcome this challenge, we propose a robust UC framework that integrates forecasting and optimization processes while ensuring statistical guarantees. In the forecasting stage, we combine multiple predictions derived from diverse data sources and methodologies for an improved prediction, aiming to optimize the UC performance. In the optimization stage, the combined prediction is used to construct an uncertainty set with statistical guarantees, based on which the robust UC model is formulated. The optimal robust UC solution provides feedback to refine the weight used for combining multiple predictions. To solve the proposed integrated forecasting-optimization framework efficiently and effectively, we develop a neural network-based surrogate model for acceleration and introduce a reshaping method for the uncertainty set based on the optimization result to reduce conservativeness. Case studies on modified IEEE 30-bus and 118-bus systems demonstrate the advantages of the proposed approach.
-
Journal articleLuh D-B, Childs P, 2026,
Preface
, Design for Augmented Humanity, Vol: 1, Pages: 3-4, ISSN: 2977-6481 -
Journal articleZou Y, Childs P, 2026,
Shifting workflow practices with implementation of AI in design in apparel and fashion
, International Journal of Industrial and Manufacturing Engineering, ISSN: 2575-3150Design has conventionally been associated with iterative phases of requirements capture, ideation, concept development, stakeholder engagement, detailing and prototyping. The advent of widespread use of AI has challenged workflows in various industrial sectors including fashion and apparel. Practitioners can explore a wide range of concepts within moments of conception using generative design tools and expose these to stakeholder evaluation, short-cutting formerly laborious phases of detailing and focus group formation. In addition, use can be made of a wide range of CAD tools for pattern production, further accelerating the product cycle. The arising workflow can now occur at a pace allowing rapid exploration of concepts and their potential market acceptance whereby concepts can be evaluated prior to commitment of significant resources. This shifts perspectives in an industry where concepts were formerly developed based on speculative approaches or attempts to form a future market with commitment of many person months or years of effort prior to release of the product. Now, ideas can be formulated and their potential evaluated on a time scale of days compatible with influencing the concept and concept team workflow. This paper explores conceptual design development for a range of garments ranging from contemporary teenage fashion to maxi dress design and sportswear. The arising insights have potential to inform design studio and industrial practice across the sector.
-
Journal articleDhopatkar R, Sadan MK, George C, 2026,
Infrared Active Actuators Mimicking Locomotion Patterns of Soft-Bodied Invertebrates
, ACS Applied Polymer Materials, Vol: 8, Pages: 1595-1602Soft-bodied invertebrates such as caterpillars and leeches transduce muscle contraction and relaxation sequences toward their locomotion ability. Mimicking these complex locomotory patterns to design soft robots with predictable gaits remains a significant challenge to date. Here we report infrared responsive actuators based on graphite ink-coated low-density polyethylene (LDPE) sheets, capable of performing caterpillar-like crawling and somersaulting motion with high predictability, reversibility and rapidity. By strategically patterning graphite ink on LDPE and performing thermal imaging, we show that the heat generation across actuators upon photoirradiation correlates to actuation response time and magnitude. These actuators achieve a curvature angle of 270° in 9 s and return consistently to their original state, with over 70% improvement in the restoration time. Similarly, somersaulting (in 5 s) and wave-like crawling (30.8 mm/min) achieve over 60% improvement in the actuation speed. Our findings therefore open possibilities of designing untethered actuators with high precision and adaptable locomotion modes.
-
Journal articleGao X, Yan Z, Lin L, et al., 2026,
Microsized Sn-Hard Carbon Composite Anode with Capacities of 583 mAh g–1and 1073 mAh cm–3for Sodium-Ion Batteries
, ACS Energy Letters, Vol: 11, Pages: 1916-1925Sodium-ion batteries (SIBs) are applied for large-scale energy storage systems, yet their energy density remains capped by hard carbon (HC) anodes with modest gravimetric and volumetric capacities. Herein, we report an alloying-carbon strategy that applies microsized Sn particles with microsized HC particles to form thick-film anodes. The optimized Sn-HC composite couples the high capacity and compaction density of Sn with the structural robustness of HC, displaying the gravimetric and volumetric capacities of 583 mAh g<sup>–1</sup> and 1073 mAh cm<sup>–3</sup>, an initial Coulombic efficiency of 90.5%, a capacity retention of ∼89.5% after 1000 cycles at 0.5 A g<sup>–1</sup>, and limited electrode swelling of 33.7%. Coupled with the Na<inf>3</inf>V<inf>2</inf>(PO<inf>4</inf>)<inf>3</inf> cathode, the SIB full cell delivers an energy density of 254 Wh kg<sup>–1</sup> and high-rate capabilities. Such Sn-HC architecture offers a scalable and industrially relevant route to simultaneously increase the gravimetric and volumetric capacities of anodes for SIBs.
-
Journal articleWang P, Zhang X, Wei L, et al., 2026,
Human-AI co-ideation via combinational generative model
, Journal of engineering design, Vol: 37, Pages: 458-494, ISSN: 0954-4828Ideation is a critical step in the engineering design process, enabling designers to develop creative and innovative concepts and prototypes. Currently, the ideation workflow requires designers to generate new designs based on product requirements, heavily relying on their personal expertise and experience. To advance human-AI collaboration design and assist designers in the idea-generation process, this paper proposes an Object Combination Generative Adversarial Network (OC-GAN) for combinational creativity. The proposed method includes an image encoder module and a cross-domain object combination generator module. The image encoder module captures and encodes image structure information into latent space, while the cross-domain object combination generator module leverages GANs to combine object images based on user preferences, producing new design images. A design case study is used to evaluate the new ideation approach and reveal not only strong cross-domain concept combination capabilities but also improvement in designers' workflow and provision of novelty to the design case.HighlightsAn AI approach to improve the efficiency of idea generation in the design process.A case study evaluates its support for idea generation and design creativity.The OC-GAN is used for multi-domain object image combining tasks.Exemplifies the feasibility of human-AI collaboration design for enhancing creativity.
-
Journal articleHuppe M, Myant C, 2026,
3D tibial HU reconstruction from biplanar X-rays utilizing a hybrid PCA-CNN framework
, Computers in Biology and Medicine, Vol: 202, ISSN: 0010-4825High-resolution Computed Tomography (CT) is the gold standard medical imaging technique for bone assessment. However, its clinical use is limited by high radiation dose (8.8 mSv; biplanar X-rays 1.4 mSv), cost, and reduced accessibility. These barriers are particularly significant for patients requiring frequent imaging. This study introduces a novel hybrid framework combining statistical intensity modeling with Deep Learning to reconstruct 3D tibial CT volumes including internal density distributions from biplanar radiographs. The method employs principal component analysis (PCA) to capture intensity variations in a compact latent space and trains a convolutional neural network (CNN) to regress PCA coefficients directly from radiographs. The framework was developed and validated using 60 subjects from the publicly available Korea Institute of Science and Technology Information (KISTI) database. Compared to ground truth CT, it achieved a mean absolute error of 127.17 ± 12.08 Hounsfield Units (HU), a structural similarity index of 0.8558 ± 0.0215, and a peak signal-to-noise ratio of 21.40 ± 0.78 dB. The method has the potential to achieve substantial radiation dose reduction compared to conventional CT while preserving sufficient anatomical detail for potential clinical tasks such as patient-specific implant planning and bone quality triage. However, the actual dose reduction depends on clinical imaging protocols and requires validation through protocol-matched dosimetry on actual radiographs. Moreover, it produces interpretable outputs that reflect anatomical intensity variations (e.g., cortical vs. trabecular regions), demonstrating feasibility for hybrid statistical-Deep Learning bone reconstruction. The proposed pipeline establishes a foundation for reduced-dose 3D bone imaging and offers a pathway toward clinical translation pending validation on real-world radiographic data.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.