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  • Journal article
    McKenzie T, Meyer-Kahlen N, Schlecht SJ, 2026,

    On the role of speech similarity in the detection of room acoustic differences.

    , J Acoust Soc Am, Vol: 159, Pages: 1373-1384

    Spatial audio systems are typically evaluated in comparative listening tests using the same source signal for each condition {such as ABX: ITU-R BS.1116-3 [(2015a) Methods for the Subjective Assessment of Small Impairments in Audio Systems (International Telecommunication Union, Geneva, Switzerland)] and multiple stimulus with hidden reference and anchor ITU-R BS.1534-3 [(2015b) Methods for the Subjective Assessment of Intermediate Quality Level of Audio Systems (International Telecommunication Union, Geneva, Switzerland)]}. However, in augmented reality (AR) scenarios, it is infeasible that the same sound source would exist at the same position in space, both real and virtual; instead, each sound source will emit a different signal. To investigate this discrepancy, a perceptual study is conducted on the effect of source signal similarity when distinguishing different room acoustics conditions. Specifically, these conditions are binaural room impulse responses measured at different distances from the source, modified to all use the same direct sound. Three classes of source signal are investigated in a three-alternative forced choice paradigm: the same speech signal for all conditions, the same speaker but a different sentence for each condition, and a different speaker and a different sentence for each condition. Results show that using different speech recordings significantly reduces the ability to identify differences in room acoustics. This suggests that spatial audio system fidelity requirements could vary depending on the source signals used in the target application; AR audio evaluation should use different signals for comparisons.
  • Conference paper
    Lissillour 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

    Points 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.
  • Journal article
    Deterding S, Guckelsberger C, Lintunen EM, Ady NMet al., 2026,

    Advancing Self-Determination Theory via computational modelling: the case of competence and optimal challenge

    , Motivation and Emotion, Vol: 50, Pages: 80-99, ISSN: 0146-7239

    Computational modelling is a powerful tool to specify psychological theories and conduct model-based empirical research. Yet it has seen little use in Self-Determination Theory (SDT), one of the most successful theories of human motivation. Here, we use two basic SDT constructs, competence and optimal challenge, to demonstrate how computational modelling can benefit theory building and practical application for SDT. Drawing on conceptual analysis and a toy model, we identify three plausible intensional facets of verbal competence definitions that unevenly align with operationalisations and propositions on optimal challenge. We then show how computational modelling, inspired by the AI field of computational intrinsic motivation, can help inform the refinement of these and other constructs, provide point-precise predictions, complement cognition-level mechanistic accounts of competence, refine practical guidance, and support implementation in digital task and goal-setting applications.
  • Journal article
    Thrän J, Green TC, Shorten R, 2026,

    Levelised cost of demand response: estimating the cost-competitiveness of flexible demand

    , Energy Conversion and Management, Vol: 349, ISSN: 0196-8904

    To make well-informed investment decisions, energy system stakeholders require reliable cost frameworks for demand response and storage technologies. While the levelised cost of storage permits comprehensive cost comparisons between different storage technologies, no generic cost measure for the comparison of different demand response schemes exists. This paper introduces the levelised cost of demand response, which is an analogous measure to the levelised cost of storage but crucially differs from it by considering consumer reward payments. Additionally, the value factor from cost estimations of variable renewable energy is adapted to account for the variable availability of demand response. The levelised cost of demand response is estimated for four direct load control schemes and twelve storage applications, and then contrasted against literature values for the levelised cost of the most competitive storage technologies. The direct load control schemes are vehicle-to-grid, smart charging, smart heat pumps, and heat pumps with thermal storage. The results show that only heat pumps with thermal storage consistently outcompete storage technologies, with EV-based schemes being competitive for some applications. The results and the underlying methodology offer a tool for energy system stakeholders to assess the competitiveness of demand response schemes even with limited user data.
  • Journal article
    Wang P, Zhang X, Wei L, Childs P, Jia Wang S, Guo Y, Kleinsmann Met al., 2026,

    Human-AI co-ideation via combinational generative model

    , Journal of engineering design, Vol: 37, Pages: 458-494, ISSN: 0954-4828

    Ideation 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 article
    Servi A, Gardner-Bougaard E, Mohamed S, McDermott A, Rodrigues R, Aveyard B, Van Zalk N, Hampshire A, Dewa L, Di Simplicio Met al., 2026,

    Early Evaluation of IMAGINATOR 2.0 Intervention Targeting Self-Harm in Young People: Single-Arm Feasibility Trial.

    , JMIR Form Res, Vol: 10

    BACKGROUND: Self-harm (SH) affects around 20% of all young people in the United Kingdom. Treatment options for SH remain limited and those available are long and costly and may not suit all young people. There is an urgent need to develop new scalable interventions to address this gap. IMAGINATOR is a novel imagery-based intervention targeting SH initially developed for individuals aged 16 to 25 years. It is a blended digital intervention delivering functional imagery training via therapy sessions and a smartphone app. OBJECTIVE: This study aimed to pilot a new version of the app, IMAGINATOR 2.0, extended to adolescents from the age of 12 years and coproduced with a diverse group of young people with lived experience. Our aim was also to test the feasibility and acceptability of delivering IMAGINATOR 2.0 in secondary mental health services. METHODS: A total of 4 co-design workshops were conducted online with UK-based lived-experience co-designers aged 14-25 years to develop the IMAGINATOR 2.0 app. The intervention was then piloted with participants recruited from West London NHS Trust Tier 2 Child and Adolescent Mental Health Services and adult Mental Health Integrated Network Teams. Participants received 3 face-to-face functional imagery training sessions in which the app was introduced and 5 brief phone support sessions. Outcome assessments were conducted after completing therapy, approximately 3 months post baseline. Two focus groups gathered the therapists' perspectives on IMAGINATOR 2.0's acceptability and means of improvement. For quantitative data, descriptives are reported. Qualitative data were analyzed using a coproduced thematic analysis method with young people with lived experiences. RESULTS: Overall, 83 participants were referred, and 29 (gender: n=28 women, n=1 transgender; mean age 18.9, SD 3.74 years) were eligible and completed screening. Of the 27 participants who started, 59% (n=16) completed therapy per protocol, while only 15 (55.6%) completed
  • Journal article
    Angeliki M, Picinali L, Vicente T, 2026,

    A pilot study to assess the challenges and efficacy of two hearing loss simulations

    , npj Acoustics, ISSN: 3005-141X

    Developing accurate and customisable hearing loss (HL) simulations is crucial for understanding and raising awareness of the challenge 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 simulatecommon HL deficits, with 3DTI offering greater customization. A pilot listening study was conducted involving normal-hearing listeners with simulated HL and those with real HL, focusing on mild-to-moderate high-frequency hearing loss. Audiometric tests and psychoacoustic tasks were employed, including gap and tone detection in noise, perceived sound intensity, andintelligibility tests. Results from two real HL listeners guided simulation adjustments for normal-hearing participants. Initial findings suggest reasonable accuracy in replicating spectral resolution and perceived sound intensity, but variability in intelligibility and temporal resolution tests indicates room for improvement in both implementations. This study highlights the need forenhanced customisation to improve accuracy and applicability, offering insights into development challenges. Furthermore, the employed methodology proves to be effective, offering valuable insight into challenges and biases that can occur during testing sessions, while highlighting the necessity for further research. This could include additional HL listeners in order to refine and develop more precise tools for understanding and addressing HL.
  • Journal article
    Tu Y, Wu B, Martínez-Pañeda E, 2026,

    Phase field modelling of cracking and capacity fade in core-shell cathode particles for lithium-ion batteries

    , Applied Energy, Vol: 403, ISSN: 0306-2619

    Core-shell electrode particles are a promising morphology control strategy for high-performance lithium-ion batteries. However, experimental observations reveal that these structures remain prone to mechanical failure, with shell fractures and core-shell debonding occurring after a single charge. In this work, we present a novel, comprehensive computational framework to predict and gain insight into the failure of core-shell morphologies and the associated degradation in battery performance. The fully coupled chemo-mechano-damage model presented captures the interplay between mechanical damage and electrochemical behaviours, enabling the quantification of particle cracking and capacity fade. Both bulk material fracture and interface debonding are captured by utilising the phase field method. We quantify the severity of particle cracking and capacity loss through case studies on a representative core-shell system (NMC811@NMC532). The results bring valuable insights into cracking patterns, underlying mechanisms, and their impact on capacity loss. Surface cracks are found to initiate when a significantly higher lithium concentration accumulates in the core compared to the shell. Interfacial debonding is shown to arise from localised hoop stresses near the core-shell interface, due to greater shell expansion. This debonding develops rapidly, impedes lithium-ion transport, and can lead to more than 10 % capacity loss after a single discharge. Furthermore, larger particles may experience crack branching driven by extensive tensile zones, potentially fragmenting the entire particle. The framework developed can not only bring new insight into the degradation mechanisms of core-shell particles but also be used to design electrode materials with improved performance and extended lifetime.
  • Conference paper
    Chen K, Astolfi A, 2026,

    Adaptive immersion-and-invariance control with normalizing regressor filter

    , 2025 IEEE 64th Conference on Decision and Control (CDC), Publisher: IEEE, Pages: 717-722

    This paper proposes a new scheme for the so-called adaptive immersion-and-invariance (I&I) control that requires neither solving partial differential equations nor adding dynamic scaling factors to obtain the static I&I parameter estimation term. By exploiting a specially designed time-varying candidate Lyapunov function, we show that it is sufficient to pass the regressor through a strictly passive filter with a normalization-like output nonlinearity to generate a proxy regressor that forms the static I&I estimate. Closed-loop boundedness and asymptotic stabilization can be guaranteed. Simulation results illustrate the theory.
  • Journal article
    Almukhtar A, Batcup C, Jagannath S, Leff D, Porat T, Judah G, Demirel Pet al., 2026,

    Understanding sustainability in operating theatres: an ethnographic study to determine drivers of unsustainable behaviours

    , Annals of Surgery Open, ISSN: 2691-3593

    BackgroundClimate change is the biggest threat to human health. Paradoxically, the healthcare sector is a major contributor to climate change, and operating theatres 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 which can help meet net-zero targets within healthcare. We conducted the first known ethnographic study to investigate behaviours related to sustainability in operating theatres, and their influences on those behaviours to inform the design of effective behaviour change interventions.MethodsNon-participant ethnographic observations with opportunistic discussions in elective general surgical operating theatres were conducted between June and December 2023 at two university hospitals in Central London. Data were collected until saturation using a template developed during the initial observations. Inductive thematic analysis was conducted, with sub-themes (influences) deductively mapped to the Theoretical Domains Framework (TDF).ResultsTwenty-six procedures were observed (42 hours). Unsustainable behaviours included: (i) unnecessary and inappropriate glove use, potentially compromising safety (average 8-10 pairs per operation), (ii) incorrect waste disposal, (iii) unnecessary package opening, and (iv) energy waste. Thematic analysis generated 6 themes and 16 influences (mapped to 9 TDF 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). Unsustainable behaviours we
  • Journal article
    Niu Z, Scarciotti G, Astolfi A, 2026,

    Interconnection-Based Model Reduction for Linear Hybrid Systems

    , Unmanned Systems, ISSN: 2301-3850

    In this paper, we address the model reduction problem for linear hybrid systems via the interconnection-based technique called moment matching. We consider two classical interconnections, namely the direct and swapped interconnections, in the hybrid setting, and we present families of reduced-order models for each interconnection via a hybrid characterization of the steady-state responses. By combining the results for each interconnection, the design of a reduced-order model that achieves moment matching simultaneously for both interconnections is studied. In addition, we show that the presented results have simplified counterparts when the jumps of the hybrid system are periodic. A numerical simulation is finally given to illustrate the results.
  • Conference paper
    Leonetti JL, Murphy R, Santer M, 2026,

    Robust Thermoelastic Topology Optimization of Hypersonic Structures under Manufacturing and Aerothermal Uncertainties

    A robust thermoelastic topology optimization framework based on the Augmented Lagrangian method is presented and coupled to a streamline-based hypersonic aerothermal heat flux solver in order to be applied to practical hypersonic use cases. This framework is novel in its ability to robustly handle highly constrained optimization problems subject to manufacturing and aerothermal uncertainties at a reduced computational cost. The Augmented Lagrangian method enables the use of large numbers of multidisciplinary constraints, without the need to compute the full constraint Jacobian matrix, and is suited to the topology optimization of transient thermoelastic problems in which multiple failure criteria must be considered, sometimes over time or under uncertain operational conditions. The optimization of an integrated thermal protection system and a wedge section subject to manufacturing and angle of attack uncertainties is carried out, accounting for multiple failure modes and performance metrics. The results highlight failure scenarios under uncertainty and showcase the capability of the framework to design resilient, multifunctional structures suited for hypersonic flight.
  • Conference paper
    Schaefer JW, Di Fiore F, Wu B, Mainini Let al., 2026,

    Physics-Aware Multi-Source Modelling for the Design Optimisation of Fuel Cells for Sustainable Aviation

    Low temperature proton exchange membrane fuel cells represent a promising pathway toward zero emission aviation, yet their design at the scales and power densities required for flight remains constrained by limited experimental data, limited available numerical models, high computational cost of numerical models, and numerical fragility of detailed multi-physics models. In this work, a physics-aware multi source surrogate modelling framework is proposed to support the design optimisation of aviation relevant fuel cell systems under severe data scarcity and variable model reliability. The approach combines low-fidelity and high-fidelity numerical models within an autoregressive multi-fidelity formulation in which the relative influence of each information source is evaluated and adjusted dynamically across the design space. The method is applied to the optimisation of a low-temperature proton exchange membrane fuel cell with parallel flow bipolar plates, in which a one dimensional through plane low-fidelity model and a three dimensional multiphase high-fidelity model are combined to target maximum specific power through joint optimisation of operating conditions and channel length. Results demonstrate that the proposed modelling approach enables stable and physically meaningful design exploration in regimes where classical single fidelity and fixed hierarchy multi-fidelity approaches are misled by unreliable data. When embedded within a global optimisation framework, our physics-aware multi-source model identifies feasible high performance designs validated by high-fidelity simulation, highlighting its potential as a robust tool for early stage design of emerging aerospace energy systems.
  • Journal article
    Ma P, Shen Z, Ge Y, Nanayakkara Tet al., 2026,

    Soft Auxetic Fingertip (SAF) for Shape-Adaptive and Stable Grasping

    , IEEE Robotics and Automation Letters, Vol: 11, Pages: 4219-4226

    This letter presents a soft auxetic fingertip (SAF) that decouples shape adaptability from payload ability by separating rigid support from compliant conformity. A stiff outer frame carries loads while auxetic array provides passive conformity and enlarges the contact area, distal tips improve grasping of flat and thin objects. The fingertip is fabricated by 3D printing conductive thermoplastic polyurethane (TPU). Based on its piezoresistive effect, the soft auxetic layer is used as a mechanical filter to enable contact and slip detection and to drive a grip-force compensation strategy. Under identical input torques, SAF achieves a 10.66%–151.72% payload improvement over the FinRay baseline across eight objects. Combined with slip detection based on resistance signal and grip-force compensation, it achieves a 89.29% success rate in pick-and-place tests on 28 tools and food items.
  • Journal article
    Wieberneit F, Crisostomi E, Quinn A, Shorten Ret al., 2026,

    Optimal Battery Sizing for Urban Electric Vehicles: Balancing Purchase Cost and Charging Inconvenience

    , IEEE Access, Vol: 14, Pages: 10552-10567

    Electric vehicles (EVs) are central to sustainable mobility systems. The optimal dimensioning of their batteries, however, is a topic of ongoing research. Large battery capacities may provide superior convenience, but this benefit is mediated by the available charging infrastructure and must be weighed against increased cost and mass. This paper introduces a quantitative framework to identify privately optimal EV battery capacities in urban settings, by jointly optimizing upfront vehicle cost and charging inconvenience cost. For this purpose, we: 1) derive a closed form surrogate model for charging inconvenience as a function of EV, user, and infrastructure characteristics, improving on existing studies by explicitly incorporating charging-related detours and infrastructure congestion effects; 2) demonstrate, through simulation and analytical models in abstract as well as realistic (Paris) scenarios, how charging inconvenience is jointly shaped by battery capacity and charging infrastructure; 3) introduce an optimization framework to determine privately optimal battery capacities; and 4) show how optimal battery capacities can be influenced through strategic investments in charging infrastructure and tax/incentive policies. The proposed framework can be used to identify optimal battery capacities in a given charging environment, but also to estimate the convenience benefits of charging infrastructure investments, providing a nuanced framework for optimizing the design of cost-effective, convenient and sustainable EV systems.
  • Journal article
    Jin X, Yu Z, Nanayakkara T, 2026,

    Bioinspired Tapered-Spring Turbulence Sensor for Underwater Flow Detection

    , IEEE Sensors Journal, Vol: 26, Pages: 6672-6681, ISSN: 1530-437X

    This article presents a bioinspired underwater whisker sensor for robust hydrodynamic disturbance detection and efficient signal analysis based on physical reservoir computing (PRC). The design uses a tapered nylon spring with embedded accelerometers to achieve spatially distributed vibration sensing and frequency separation along the whisker. Towing-tank experiments and computational fluid dynamics simulations confirmed that the whisker effectively distinguishes vortex regimes across different fin angles and maintains Strouhal scaling with flow velocity, where higher speeds increase vibration intensity without affecting the dominant frequencies. Frequency-domain analysis, Shannon entropy, and machine learning further validated the sensing performance: vortex shedding frequencies were identified with less than 10% error, entropy captured the transition from coherent vortex streets to turbulence, and logistic regression (LR) achieved 86.0% classification accuracy with millisecond-level inference. Importantly, the sensing objective of this work is to characterize repeatable and physically interpretable hydrodynamic disturbances, which provide a controlled basis for evaluating how the tapered geometry encodes flow-induced vibrations. Under this scope, the demonstrated frequency–spatial decoupling and multimodal responses show that structurally encoded whisker sensing provides a scalable and real-time solution for underwater perception, wake tracking, and turbulence-awarenavigation in autonomous marine robots.
  • Journal article
    Docherty R, Vamvakeros A, Cooper SJ, 2026,

    Upsampling DINOv2 Features for Unsupervised Vision Tasks and Weakly Supervised Materials Segmentation

    , Advanced Intelligent Systems

    The 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 article
    Vicente T, González-Toledo D, Cuevas-Rodríguez M, Molina-Tanco L, Reyes-Lecuona A, Picinali Let al., 2026,

    Exploring the relationship between task difficulty, head-related transfer function and spatial release from masking in a speech-on-speech experiment

    , Hearing Research, Vol: 470, ISSN: 0378-5955

    It is known that individuals make use of spatial hearing cues to improve the audibility of a target signal and separate it from competing sounds. This phenomenon is known as spatial release from masking (SRM). Recent research has shown that this happens also when sources are located in the median plane, where interaural differences are limited. When assessing this within virtual conditions, it has been shown that employing individually measured head-related transfer functions (HRTFs) results in higher SRM abilities compared to using non-individual filters. In a previously published work, we found that Spanish speakers benefit from individual HRTFs when discriminating a target English speech from a single masker in the median plane. This study replicates the protocol of that previous work, varying the number of maskers and participants’ English proficiency levels to explore relationships among task difficulty and HRTF use. Results from a first experiment show that English speakers behave differently to Spanish ones; their SRM advantage is not significant. We suggest that this is due to their language proficiency, which allows them to rely on spectral glimpsing alone, that is, exploiting spectro-temporal gaps between voices rather than spectral cues introduced by spatial separation. A second experiment introduces a second speech masker, co-located with the first; by making the task more complex, participants seem to increase their reliance on spatial cues, resulting in significant effects of masker position and HRTF. This highlights a trade-off between the use of target glimpsing and spatial cues and the need for further exploration into how task difficulty influences SRM with different HRTFs.
  • Journal article
    Pinson P, 2026,

    Editorial and introduction to the special section on the Bernanke's review of the Bank of England's forecasting activities

    , INTERNATIONAL JOURNAL OF FORECASTING, Vol: 42, Pages: 1-2, ISSN: 0169-2070
  • Journal article
    Jin X, Xiao B, Wang H, Wang W, Childs P, Yu Zet al., 2026,

    A Multisensor Wearable Device Based on Capacitive Microphone Sensors for Robust Human–Robot Interaction in Smart Home Environments

    , IEEE Sensors Journal, Vol: 26, Pages: 2935-2945, ISSN: 1530-437X

    This article presents a capacitive microphone sensor (CMS)-based multisensor fusion wearable system for intuitive and responsive control of mobile manipulators in smart home environments. The wearable integrates structurally optimized CMSs, inertial measurement units (IMUs), vibration motors, and pressure sensors to detect forearm muscle activity and arm motion in real time. To enhance signal fidelity, each CMS is embedded in a conical acoustic chamber filled with damping silicone, significantly improving robustness under noisy conditions. A convolutional neural network (CNN)-based classification model is employed to interpret six combined gesture–force classes, achieving 93.89% offline accuracy. Real-time experiments involving five participants yielded 83.33% practical accuracy with an average system latency of 1.2 s. The wearable system enables real-time gesture recognition, adaptive force feedback, and precise robot control, validated through object manipulation tasks under various texture and weight conditions. These results highlight the feasibility and effectiveness of a CMS-driven multisensor fusion wearable device for low-latency, noise-resilient, and user-adaptive assistive robotics in complex indoor environments.
  • Journal article
    Zhao Y, Chen Q, Li H, Zhou H, Attar HR, Pfaff T, Wu T, Li Net al., 2026,

    Recurrent U-Net-based Graph Neural Network (RUGNN) for accurate deformation predictions in sheet material forming

    , Advanced Engineering Informatics, Vol: 69, ISSN: 1474-0346

    In recent years, various artificial intelligence-based surrogate models have been proposed to provide rapid manufacturability predictions of material forming processes. However, traditional AI-based surrogate models, typically built with scalar or image-based neural networks, are limited in their ability to capture complex 3D spatial relationships and to operate in a permutation-invariant manner. To overcome these issues, emerging graph-based surrogate models are developed using graph neural networks. This study developed a new graph neural network surrogate model named Recurrent U Net-based Graph Neural Network (RUGNN). The RUGNN model can achieve accurate predictions of sheet material deformation fields across multiple forming timesteps. The RUGNN model incorporates Gated Recurrent Units (GRUs) to model temporal dynamics and a U-Net inspired graph-based downsample/upsample mechanism to handle spatial long-range dependencies. A novel ’node-to-surface’ contact representation method was proposed, offering significant improvements in computational efficiency for large-scale contact interactions. The RUGNN model was validated using a cold forming case study and a more complex hot forming case study using aluminium alloys. Results demonstrate that the RUGNN model provides accurate deformation predictions closely matching ground truth FE simulations and outperforming several baseline GNN architectures. Model tuning was also performed to identify suitable hyperparameters, training strategies, and input feature representations. These results demonstrate that RUGNN is a reliable approach to support sheet material forming design by enabling accurate manufacturability predictions.
  • Journal article
    Qin D, Pinson P, Wang Y, 2026,

    Load Forecasting Model Trading: A Cost-Oriented and Auction-Based Approach

    , IEEE Transactions on Smart Grid, Vol: 17, Pages: 766-779, ISSN: 1949-3053

    Data sharing is essential for accurate load forecasting and efficient energy management, yet data exchange remains severely constrained by a lack of effective economic incentives. Data markets have emerged as a potential solution by incentivizing the exchange of data and forecasting resources among stakeholders. Existing data market mechanisms, often designed around trading raw data or forecast outputs, face multiple barriers—such as privacy concerns, valuation misaligned with real operational benefits, unilateral pricing, computationally intensive allocation, and inflexible asset adaptation—that limit their practicality for real-world energy applications. The aim of this paper is to address these challenges by proposing a novel market framework that treats pre-trained forecasting models as tradable assets, thereby fundamentally redefining the data market paradigm. Specifically, a cost-oriented evaluation approach that links model quality to downstream operational costs is first established as the foundation throughout the entire market process. Subsequently, we propose a bilateral iterative model auction mechanism to enable efficient transactions between the buyer and sellers while maximizing social welfare. Furthermore, we propose a model adaptation strategy, including model fine-tuning and ensembling, for the buyer to enhance the applicability of purchased models to his decision-making problem. Case studies in building energy management based on public datasets demonstrate that our approach converges to the socially optimal solution, allowing all participants to benefit: sellers are appropriately compensated for providing high-quality models, and buyers achieve significant operational cost reductions through the utilization of traded models.
  • Journal article
    Chen L, Cai Z, Cheang W, Long Q, Sun L, Childs P, Zuo Het al., 2026,

    AskNatureGPT: an LLM-driven concept generation method based on bio-inspired design knowledge

    , Journal of engineering design, Vol: 37, Pages: 238-272, ISSN: 0954-4828

    Concept generation is the early stage in the engineering design process to produce initial design concepts. By applying bio-inspired design (BID) knowledge, designers can employ biological analogies for solution-driven BID concepts. Solution-driven BID starts with knowledge of a specific biological system for technical design. Despite the proven benefits of solution-driven BID, the gap between biological solutions and engineering problems hinders its effective application, with designers frequently encountering misaligned problem-solution pairs and facing multidisciplinary knowledge gaps in concept generation. Therefore, this research proposes a large language model (LLM) based concept generation method – AskNatureGPT – to automatically search for problems, transfer biological analogy, and generate solution-driven BID concepts in the form of natural language. A concept generator and two evaluators are identified and fine-tuned based on the LLM. The method is evaluated by an ablation study, machine-based quantitative assessments, subjective human evaluations, and a case study. The results show our method can generate solution-driven BID concepts with high quality.
  • Journal article
    Gao J, Chaudhuri B, Astolfi A, 2026,

    An explicit direct method for transient stability analysis of multimachine power systems with nonzero transfer conductances

    , IEEE Transactions on Control Systems Technology, Vol: 34, Pages: 112-122, ISSN: 1063-6536

    We propose an explicit analytical direct method for the transient stability analysis of multimachine power systems with nonzero transfer conductances (TCs). The proposed method addresses two issues. In the first issue, we study the transient stabilization of the entire power system through excitation control design. To this end, a globally well-defined Lyapunov function is constructed, and a locally well-defined dynamic passivity-based control law is proposed. The closed-loop equilibrium is therefore guaranteed to be locally asymptotically stable. In the second issue, we study the transient stability property of post-fault initial states. To this end, an optimization-based approach to calculate the critical level set of the proposed Lyapunov function is proposed. This allows to estimate an explicit region of attraction of the closed-loop equilibrium. Therefore, the transient stability property of a post-fault initial state can be directly assessed. A case study on the IEEE 10-machine 39-bus power system, to demonstrate the performance and effectiveness of the proposed direct method, is presented.
  • Journal article
    Ballaben R, Astolfi A, Braun P, Zaccarian Let al., 2026,

    Orchestrating on-board sensors for global hybrid robust stabilization of unicycles

    , AUTOMATICA, Vol: 183, ISSN: 0005-1098
  • Journal article
    Pope VC, Stewart R, Chew E, 2026,

    Timing structures in live comedy: A matched-sequence approach to mapping performance dynamics.

    , PNAS Nexus, Vol: 5

    Live performance is a ubiquitous cultural and social behavior that has not yet benefited from systematic scientific study. We present a computational methodology that visualizes and describes timing structures in live performance, showcasing their engineering. This novel analysis framework, Topology Analysis of Matching Sequences (TAMS), automatically detects matching sequences and maps their timing. Locating material that is repeated across performances reveals the skill behind apparently effortless communication between performer and audience. Applying TAMS to two stand-up comedy tours uncovered structural features at the macro- and microlevels, including consistently placed novel material at the beginning of shows and sections dedicated to tightly timed repeated material. TAMS also provides a new frame of reference for examining audience-performer dynamics through speech microtiming and laughter. TAMS can be applied to other forms of repeated speech, such as political stump speeches, as well as extended to other types of performance, such as dance.
  • Conference paper
    Barumerli R, Privitera AG, Fasolato A, Liu X, Scarfo G, Cesari P, Geronazzo Met al., 2026,

    Spatialized Looming Sounds in Virtual Reality: Reaction Times and Localization Accuracy

    , 2025 International Conference on Extended Reality-XR-Annual, Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 254-265, ISSN: 0302-9743
  • Journal article
    Baxter W, Echavarri IV, Porat T, 2025,

    Exploring the impact of changing government policy on vaccination eligibility for 50-64 year olds: A qualitative thematic analysis in England and Scotland

    , HUMAN VACCINES & IMMUNOTHERAPEUTICS, Vol: 21, ISSN: 2164-5515
  • Journal article
    Nutbeam T, Leech C, E Baker C, Box E, R Rodgers L, Dungay K, Johnson L, Lee B, MacQueen M, Fenwick R, Marritt I, Dunbar I, Barnard EBGet al., 2025,

    Identifying research priorities for post-collision care in the United Kingdom: protocol for a road injury priority setting partnership.

    , Scand J Trauma Resusc Emerg Med, Vol: 33

    BACKGROUND: Road traffic injury remains a significant global health challenge, causing over 1.2 million deaths and tens of millions of non-fatal injuries each year. In the United Kingdom (UK), more than 1,600 people died and nearly 30,000 sustained serious injuries on the roads in 2024. While improvements in vehicle design, road infrastructure, emergency response, and road user behaviour have contributed to a sustained reduction in road deaths over recent decades, outcomes for those injured remain variable, and post-collision care has received comparatively less research attention. The Road Injury Chain of Survival framework identifies five interdependent stages where timely, coordinated interventions can improve survival and recovery, however, there has been no systematic effort to define the most important research questions in this area. METHODS: This protocol describes a UK-wide Road Injury Priority Setting Partnership (PSP) conducted using the established methodology of the James Lind Alliance. Research uncertainties will be gathered via a national open-access survey and supplemented by a targeted evidence scan of published research recommendations and clinical guidelines. Submissions will be collated, categorised, and checked against the current evidence base to ensure that only true uncertainties progress. A multi-stakeholder consensus workshop, using the Nominal Group Technique, will identify a 'Top 10' list of research priorities. A secondary output will map priorities to each stage of the Road Injury Chain of Survival to guide targeted research and innovation. DISCUSSION: This is the first PSP focused specifically on post-collision care in the UK. By integrating the perspectives of patients, carers, bystanders, clinicians, policymakers, and researchers, the PSP aims to produce priorities that are directly actionable and relevant to national needs. The findings will inform funders, guideline developers, and service providers, supporting more effective
  • Conference paper
    Meyer-Kahlen N, Pollack K, Lladó P, 2025,

    Comparing Measures of Information in Head-Related Transfer Functions

    , 11th Convention of the European Acoustics Association Forum Acusticum / EuroNoise 2025, Publisher: European Acoustics Association, Pages: 3405-3412

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