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Journal articleChen L, Zuo H, Cai Z, et al., 2024,
Towards controllable generative design: a conceptual design generation approach leveraging the FBS ontology and Large Language Models
, Journal of Mechanical Design, Vol: 146, ISSN: 1050-0472Recent research in the field of design engineering is primarily focusing on using AI technologies such as Large Language Models (LLMs) to assist early-stage design. The engineer or designer can use LLMs to explore, validate and compare thousands of generated conceptual stimuli and make final choices. This was seen as a significant stride in advancing the status of the generative approach in computer-aided design. However, it is often difficult to instruct LLMs to obtain novel conceptual solutions and requirement-compliant in real design tasks, due to the lack of transparency and insufficient controllability of LLMs. This study presents an approach to leverage LLMs to infer Function-Behavior-Structure (FBS) ontology for high-quality design concepts. Prompting design based on the FBS model decomposes the design task into three sub-tasks including functional, behavioral, and structural reasoning. In each sub-task, prompting templates and specification signifiers are specified to guide the LLMs to generate concepts. User can determine the selected concepts by judging and evaluating the generated function-structure pairs. A comparative experiment has been conducted to evaluate the concept generation approach. According to the concept evaluation results, our approach achieves the highest scores in concept evaluation, and the generated concepts are more novel, useful, functional, and low-cost compared to the baseline.
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Journal articleCutting J, Deterding S, 2024,
The task-attention theory of game learning: a theory and research agenda
, Human-Computer Interaction, Vol: 39, Pages: 257-287, ISSN: 0737-0024Why do learning games fail or succeed? Recent evidence suggests that attention forms an important moderator of learning from games. While existing media effects and learning theories acknowledge the role of attentional limits, they fail to account for the specific ways that games as interactive media steer attention. In response, we here develop the Task-Attention Theory of Game Learning. Drawing on current psychological and games research, task-attention theory argues that games as interactive media demand and structure the pursuit of tasks, which ties into distinct attentional mechanisms, namely learned attentional sets which focus attentional selection onto task-relevant features, as well as active sampling: users navigate and manipulate the game to elicit task-relevant information. This active sampling and selection precedes and moderates what information can be learned. We identify task-related game features (mechanics, goals, rewards and uncertainty) and demands (cognitive and perceptual load, pressure) that affect active sampling and attentional selection. We articulate implications and future work for game-based learning research and design, as well as wider media effects, learning, and HCI research.
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Journal articleQian Q, Wang Y, Boyle D, 2024,
On solving close enough orienteering problems with overlapped neighborhoods
, European Journal of Operational Research, Vol: 318, Pages: 369-387, ISSN: 0377-2217The Close Enough Traveling Salesman Problem (CETSP) is a well-known variant of the classic TravelingSalesman Problem whereby the agent may complete its mission at any point within a target neighborhood.Heuristics based on overlapped neighborhoods, known as Steiner Zones (SZ), have gained attention inaddressing CETSPs. While SZs offer effective approximations to the original graph, their inherent overlapimposes constraints on the search space, potentially conflicting with global optimization objectives. Here weshow how such limitations can be converted into advantages in the Close Enough Orienteering Problem (CEOP)by aggregating prizes across overlapped neighborhoods. We further extend the classic CEOP with Non-uniformNeighborhoods (CEOP-) by introducing non-uniform cost considerations for prize collection. To tackle CEOP(and CEOP-), we develop a new approach featuring a Randomized Steiner Zone Discretization (RSZD)scheme coupled with a hybrid algorithm based on Particle Swarm Optimization (PSO) and Ant Colony System(ACS) — CRaSZe-AntS. The RSZD scheme identifies sub-regions for PSO exploration, and ACS determinesthe discrete visiting sequence. We evaluate the RSZD’s discretization performance on CEOP instances derivedfrom established CETSP instances and compare CRaSZe-AntS against the most relevant state-of-the-art heuristicfocused on single-neighborhood optimization for CEOP instances. We also compare the performance of theinterior search within SZs and the boundary search on individual neighborhoods in the context of CEOP-. Ourexperimental results show that CRaSZe-AntS can yield comparable solution quality with significantly reducedcomputation time compared to the single neighborhood strategy, where we observe an averaged 140.44%increase in prize collection and 55.18% reduction of algorithm execution time. CRaSZe-AntS is thus highlyeffective in solving emerging CEOP-, examples of which include truck-and-drone delivery scenarios.
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Journal articleYu X, Baker CE, Ghajari M, 2024,
Head impact location, speed and angle from falls and trips in the workplace
, Annals of Biomedical Engineering, Vol: 52, Pages: 2687-2702, ISSN: 0090-6964Traumatic brain injury (TBI) is a common injury in the workplace. Trips and falls are the leading causes of TBI in the workplace. However, industrial safety helmets are not designed for protecting the head under these impact conditions. Instead, they are designed to pass the regulatory standards which test head protection against falling heavy and sharp objects. This is likely to be due to the limited understanding of head impact conditions from trips and falls in workplace. In this study, we used validated human multi-body models to predict the head impact location, speed and angle (measured from the ground) during trips, forward falls and backward falls. We studied the effects of worker size, initial posture, walking speed, width and height of the tripping barrier, bracing and falling height on the head impact conditions. Overall, we performed 1692 simulations. The head impact speed was over two folds larger in falls than trips, with backward falls producing highest impact speeds. However, the trips produced impacts with smaller impact angles to the ground. Increasing the walking speed increased the head impact speed but bracing reduced it. We found that 41% of backward falls and 19% of trips/forward falls produced head impacts located outside the region of helmet coverage. Next, we grouped all the data into three sub-groups based on the head impact angle: [0°, 30°], (30°, 60°] and (60°, 90°] and excluded groups with small number of cases. We found that most trips and forward falls lead to impact angles within the (30°, 60°] and (60°, 90°] groups while all backward falls produced impact angles within (60°, 90°] group. We therefore determined five representative head impact conditions from these groups by selecting the 75th percentile speed, mean value of angle intervals and median impact location (determined by elevation and azimuth angles) of each group. This led to two representative head impact conditions for trip
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Journal articlevan der Meer D, Pinson P, Camal S, et al., 2024,
CRPS-based online learning for nonlinear probabilistic forecast combination
, International Journal of Forecasting, Vol: 40, Pages: 1449-1466, ISSN: 0169-2070Forecast combination improves upon the component forecasts. Most often, combination approaches are restricted to the linear setting only. However, theory shows that if the component forecasts are neutrally dispersed—a requirement for probabilistic calibration—linear forecast combination will only increase dispersion and thus lead to miscalibration. Furthermore, the accuracy of the component forecasts may vary over time and the combination weights should vary accordingly, necessitating updates as time progresses. In this paper, we develop an online version of the beta-transformed linear pool, which theoretically can transform the probabilistic forecasts such that they are neutrally dispersed. We show that, in the case of stationary synthetic time series, the performance of the developed method converges to that of the optimal combination in hindsight. Moreover, in the case of nonstationary real-world time series from a wind farm in mid-west France, the developed model outperforms the optimal combination in hindsight.
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Journal articleChen L, Cai Z, Jiang Z, et al., 2024,
AskNatureNet: a divergent thinking tool based on bio-inspired design knowledge
, Advanced Engineering Informatics: the science of supporting knowledge-intensive activities, Vol: 62, ISSN: 0954-1810Divergent thinking is a process in design by exploring multiple possible solutions, is crucial in the early stages of design to break fixation and expand the design ideation. Design-by-Analogy promotes divergent thinking, by studying solutions have solved similar problems and using this knowledge to make inferences and solve problems in new and unfamiliar situations. Bio-inspired design (BID) is a form of design by analogy and its knowledge provides diverse sources for analogy, making BID knowledge as a potential source for divergent thinking. Existing BID database has focused on collecting BID cases and facilitating the retrieval of biological knowledge. Despite its success, applying BID knowledge into divergent thinking still encounters challenge, as the association between source domain and target domain are always limited within a single case. In this work, a novel approach is proposed to support divergent thinking from three subsequent phases: encoding, retrieval and mapping. Specifically, biological knowledge is encoded in a triple form by employing a large language model (LLM) to extract key information from a well-known BID knowledge base. The created triples are implemented in a semantic network to facilitate bidirectional retrieval modes: problem-driven and solution-driven, as well as mapping for divergent thinking. The mapping algorithm calculates the semantic similarity between nodes in the semantic network based on their attributes in three progressive steps by following the paradigm of divergent thinking. The proposed approach is implemented as tool called AskNatureNet,1 which supports divergent thinking by retrieving and mapping knowledge in a visualized interactive semantic network. An ideation case study on evaluating the effectiveness of AskNatureNet shows that our tool is capable of supporting divergent thinking efficiently.
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Journal articleZou Y, Zhao C, Childs P, et al., 2024,
Cross-cultural design in costume: case study on totemic symbols of China and Thailand
, Humanities and Social Sciences Communications, Vol: 11, ISSN: 2662-9992Cross-cultural design has emerged as a pivotal domain of significance within the context of globalization. In the field of cross-cultural design, designers are tasked with addressing user requirements and identity characteristic contexts across diverse cultural backgrounds, aiming to achieve enhanced service delivery and cultural dissemination outcomes. Nonetheless, the landscape of contemporary fashion design research exhibits a noticeable dearth in studies that effectively integrate with cross-cultural design. This study selects the iconic cultural symbols of the Chinese loong (dragon) and Thai naga as case subjects, embarking upon research that employs costume design as a medium and bridge for cross-cultural design and communication. The research methodology integrates qualitative and quantitative approaches, including field investigations, participatory research, and Analytic Hierarchy Process (AHP) analysis, thereby substantiating theoretical constructs through empirical investigation. The study proposes that cross-cultural costume design, undertaken with the purpose of cross-cultural communication, can be conceptualized as a cyclical process involving multiple encoding and decoding iterations. The research elaborates on how costume, functioning as a non-verbal language, serve as a medium for facilitating cross-cultural interactions. Furthermore, the design extraction of cultural symbols is approached through a four-tiered framework. By articulating its research perspective, methodologies, and design cases, this study contributes valuable insights to researchers and practitioners engaged in cross-cultural design and related fields.
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Journal articleZHOU J, Porat T, Van Zalk N, 2024,
Humans mindlessly treat AI virtual agents as social beings, but this tendency diminishes among the young: evidence from a Cyberball experiment
, Human Behavior and Emerging TechnologiesThe "social being" perspective has largely influenced the design and research of AI virtual agents. Do humans really treat these agents as social beings? To test this, we conducted a 2 between (Cyberball condition: exclusion vs. fair play) ×2 within (co-player type: AGENT vs. HUMAN) online experiment employing the Cyberball paradigm; we investigated how participants (N = 244) responded when they observed an AI virtual agent being ostracised or treated fairly by another human in Cyberball, and we compared our results with those from human-human Cyberball research. We found that participants mindlessly responded to the ostracised agent as they would to other humans by applying the social norm of inclusion during the interaction. This finding suggests that individuals tend to mindlessly treat AI virtual agents as social beings, supporting the media equation theory; however, age (no other user characteristics) influenced this tendency, with younger participants less likely to mindlessly apply the inclusion norm. We also found that participants showed increased sympathy towards the ostracised agent, but they did not devalue the human player for their ostracising behaviour; this indicates that participants did not mindfully perceive AI virtual agents as comparable to humans. Furthermore, we uncovered two other exploratory findings: the association between frequency of agent usage and sympathy, and the carryover effect of positive usage experience. Our study advances the theoretical understanding of the human side of human-agent interaction. Practically, it provides implications for the design of AI virtual agents, including the consideration of social norms, caution in human-like design, and age-specific targeting.
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Journal articlePatel AM, Baxter W, Porat T, 2024,
Towards Guidelines for Designing Holistic Integrated Information Visualisations [HI-viz] for Time-Critical Contexts: A Systematic Review
, Journal of Medical Internet Research, ISSN: 1438-8871 -
Journal articleAunger R, Deterding S, Zhao X, et al., 2024,
Applying the Barker School concept of 'behaviour settings' to virtual contexts
, Philosophical Transactions of the Royal Society B: Biological Sciences, Vol: 379, ISSN: 0962-8436People are spending more and more time interacting with virtual objects and environments. We argue that Roger Barker's concept of a 'behaviour setting' can be usefully applied to such experiences with relatively little modification if we recognize subjective aspects of such experiences such as presence and immersion. We define virtual behaviour settings as virtual environments where the partly or fully digital milieu is synomorphic with and circumjacent to embodied behaviour, as opposed to the fragmented behaviour settings of much-mediated interaction. We present two tools that can help explain and predict the outcomes of virtual experiences-the behaviour setting canvas (BSC) and model-and demonstrate their utility through examples. We conclude that the behaviour setting concept is helpful in both designing virtual environments and understanding their impact, while virtual environments offer a powerful new methodological paradigm for studying behaviour settings. This article is part of the theme issue 'People, places, things, and communities: expanding behaviour settings theory in the twenty-first century'.
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Journal articleBaker CE, Yu X, Lovell B, et al., 2024,
How well do popular bicycle helmets protect from different types of head injury?
, Annals of Biomedical Engineering, ISSN: 0090-6964Bicycle helmets are designed to protect against skull fractures and associated focal brain injuries, driven by helmet standards. Another type of head injury seen in injured cyclists is diffuse brain injuries, but little is known about the protection provided by bicycle helmets against these injuries. Here, we examine the performance of modern bicycle helmets in preventing diffuse injuries and skull fractures under impact conditions that represent a range of real-world incidents. We also investigate the effects of helmet technology, price, and mass on protection against these pathologies. 30 most popular helmets among UK cyclists were purchased within 9.99-135.00 GBP price range. Helmets were tested under oblique impacts onto a 45° anvil at 6.5 m/s impact speed and four locations, front, rear, side, and front-side. A new headform, which better represents the average human head's mass, moments of inertia and coefficient of friction than any other available headforms, was used. We determined peak linear acceleration (PLA), peak rotational acceleration (PRA), peak rotational velocity (PRV), and BrIC. We also determined the risk of skull fractures based on PLA (linear risk), risk of diffuse brain injuries based on BrIC (rotational risk), and their mean (overall risk). Our results show large variation in head kinematics: PLA (80-213 g), PRV (8.5-29.9 rad/s), PRA (1.6-9.7 krad/s2), and BrIC (0.17-0.65). The overall risk varied considerably with a 2.25 ratio between the least and most protective helmet. This ratio was 1.76 for the linear and 4.21 for the rotational risk. Nine best performing helmets were equipped with the rotation management technology MIPS, but not all helmets equipped with MIPS were among the best performing helmets. Our comparison of three tested helmets which have MIPS and no-MIPS versions showed that MIPS reduced rotational kinematics, but not linear kinematics. We found no significant effect of helmet price on exposure-adjusted inju
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Journal articleBaker CE, Martin P, Montemeglio A, et al., 2024,
Inherent uncertainty in pedestrian collision reconstruction: How evidence variability affects head kinematics and injury prediction.
, Accid Anal Prev, Vol: 208Reconstructing individual cases from real-world collision data is used as a tool to better understand injury biomechanics and determine injury thresholds. However, real-world data tends to have inherent uncertainty within parameters, such as ranges of impact speed, pre-impact pedestrian stance or pedestrian anthropometric characteristics. The implications of this input parameter uncertainty on the conclusions made from case reconstruction about injury biomechanics and risk is not well investigated, with a 'best-fit' approach more frequently adopted, leaving uncertainty unexplored. This study explores the implications of uncertain parameters in real-world data on the biomechanical kinematic metrics related to head injury risk in reconstructed real-world pedestrian-car collisions. We selected six pedestrian-car cases involving seven pedestrians from the highly detailed GB Road Accident In-Depth Studies (RAIDS) database. The collisions were reconstructed from the images, damage measurements and dynamics available in RAIDS. For each case, we varied input parameters within uncertain ranges and report the range of head kinematic metrics from each case. This includes variations of reconstructed collision scenarios that fits within the constraints of the available evidence. We used a combination of multibody and finite element modelling in Madymo to test whether the effect of input data uncertainty is the same on the initial head-vehicle and latter head-ground impact phase. Finally, we assessed whether the predicted range of head kinematics correctly predicted the injuries sustained by the pedestrian. Varying the inputs resulted in a range of output head kinematic parameters. Real-world evidence such as CCTV footage enabled predicted simulated values to be further constrained, by ruling out unrealistic scenarios which do not fit the available evidence. We found that input data uncertainty had different implications for the initial head-vehicle and latter head-ground impact
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Conference paperHu X, Li J, Picinali L, et al., 2024,
HRTF spatial upsampling in the spherical harmonics domain employing a generative adversarial network
, 27th International Conference on Digital Audio Effects (DAFx24), Publisher: University of Surrey, Pages: 396-403A Head-Related Transfer Function (HRTF) is able to capture alterations a sound wave undergoes from its source before it reaches the entrances of a listener’s left and right ear canals, and is imperative for creating immersive experiences in virtual and augmented reality (VR/AR). Nevertheless, creating personalized HRTFs demands sophisticated equipment and is hindered by time-consuming data acquisition processes. To counteract these challenges, various techniques for HRTF interpolation and up-sampling have been proposed. This paper illustrates how Generative Adversarial Networks (GANs) can be applied to HRTF data upsampling in the spherical harmonics domain. We propose using Autoencoding Generative Adversarial Networks (AE-GAN) to upsample low-degree spherical harmonics coefficients and get a more accurate representation of the full HRTF set. The proposed method is bench-marked against two baselines: barycentric interpolation and HRTFselection. Results from log-spectral distortion (LSD) evaluation suggest that the proposed AE-GAN has significant potential for upsampling very sparse HRTFs, achieving 17% improvement over baseline methods.
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Journal articleFerraro P, Yu JY, Ghosh R, et al., 2024,
On unique ergodicity of coupled AIMD flows
, International Journal of Control, Vol: 97, Pages: 2151-2161, ISSN: 0020-7179The AIMD algorithm, which underpins the Transmission Control Protocol (TCP) for transporting data packets in communication networks, is perhaps the most successful control algorithm ever deployed. Recently, its use has been extended beyond communication networks, and successful applications of the AIMD algorithm have been reported in transportation, energy, and mathematical biology. A very recent development in the use of AIMD is its application in solving large-scale optimisation and distributed control problems without the need for inter-agent communication. In this context, an interesting problem arises when multiple AIMD networks are coupled in some sense (usually through a nonlinearity). The purpose of this note is to prove that such systems in certain settings inherit the ergodic properties of individual AIMD networks. This result has important consequences for the convergence of the aforementioned optimisation algorithms. The arguments in the paper also correct conceptual and technical errors in Alam et al. (2020, The convergence of finite-averaging of AIMD for distributed heterogeneous resource allocations. arXiv:2001.08083 [math.OC].).
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Journal articlePan Y, Ruan H, Wu B, et al., 2024,
A machine learning driven 3D+1D model for efficient characterization of proton exchange membrane fuel cells
, Energy and AI, Vol: 17, ISSN: 2666-5468The computational demands of 3D continuum models for proton exchange membrane fuel cells remain substantial. One prevalent approach is the hierarchical model combining a 2D/3D flow field with a 1D sub-model for the catalyst layers and membrane. However, existing studies often simplify the 1D domain to a linearized 0D lumped model, potentially resulting in significant errors at high loads. In this study, we present a computationally efficient neural network driven 3D+1D model for proton exchange membrane fuel cells. The 3D sub-model captures transport in the gas channels and gas diffusion layers and is coupled with a 1D electrochemical sub-model for microporous layers, membrane, and catalyst layers. To reduce computational intensity of the full 1D description, a neural network surrogates the 1D electrochemical sub-model for coupling with the 3D domain. Trained by model-generated large synthetic datasets, the neural network achieves root mean square errors of less than 0.2%. The model is validated against experimental results under various relative humidities. It is then employed to investigate the nonlinear distribution of internal states under different operating conditions. With the neural network operating at 0.5% of the computing cost of the 1D sub-model, the hybrid model preserves a detailed and nonlinear representation of the internal fuel cell states while maintaining computational costs comparable to conventional 3D+0D models. The presented hybrid data-driven and physical modeling framework offers high accuracy and computing speed across a broad spectrum of operating conditions, potentially aiding the rapid optimization of both the membrane electrode assembly and the gas channel geometry.
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Journal articleKench S, Squires I, Dahari A, et al., 2024,
Li-ion battery design through microstructural optimization using generative AI
, Matter, ISSN: 2590-2385 -
Journal articleHeath BE, Suzuki R, LePenru NP, et al., 2024,
Spatial ecosystem monitoring with a Multichannel Acoustic Autonomous Recording Unit (MAARU)
, Methods in Ecology and Evolution, Vol: 15, Pages: 1568-1579, ISSN: 2041-210X1. Multi-microphone recording adds spatial information to recorded audio with emerging applications in ecosystem monitoring. Specifically placing sounds in space can improve animal count accuracy, locate illegal activity like logging and poaching, track animals to monitor behaviour and habitat use and allow for ‘beamforming’ to amplify sounds from target directions for downstream classification. Studies have shown many advantages of spatial acoustics, but uptake remains limited as the equipment is often expensive, complicated, inaccessible or only suitable for short-term deployments.2. With an emphasis on enhanced uptake and usability, we present a low-cost, open-source, six-channel recorder built entirely from commercially available components which can be integrated into a solar-powered, online system. The MAARU (Multichannel Acoustic Autonomous Recording Unit) works as an independent node in long-term autonomous, passive and/or short-term deployments. Here, we introduce MAARU's hardware and software and present the results of lab and field tests investigating the device's durability and usability.3. MAARU records multichannel audio with similar costs and power demands to equivalent omnidirectional recorders. MAARU devices have been deployed in the United Kingdom and Brazil, where we have shown MAARUs can accurately localise pure tones up to 6 kHz and bird calls as far as 8 m away (±10° range, 100% and >60% of signals, respectively). Louder calls may have even further detection radii. We also show how beamforming can be used with MAARUs to improve species ID confidence scores.4. MAARU is an accessible, low-cost option for those looking to explore spatial acoustics accurately and easily with a single device, and without the formidable expenses and processing complications associated with establishing arrays. Ultimately, the added directional element of the multichannel recording provided by MAARU allows for enhanced recording
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Journal articleLin L, Hamedmoghadam H, Shorten R, et al., 2024,
Quantifying indirect and direct vaccination effects arising in the SIR model.
, J R Soc Interface, Vol: 21Vaccination campaigns have both direct and indirect effects that act to control an infectious disease as it spreads through a population. Indirect effects arise when vaccinated individuals block disease transmission in any infection chain they are part of, and this in turn can benefit both vaccinated and unvaccinated individuals. Indirect effects are difficult to quantify in practice but, in this article, working with the susceptible-infected-recovered (SIR) model, they are analytically calculated in important cases, through pivoting on the final size formula for epidemics. Their relationship to herd immunity is also clarified. The analysis allows us to identify the important distinction between quantifying the indirect effects of vaccination at the 'population level' versus the 'per capita' level, which often results in radically different conclusions. As an example, our analysis unpacks why the population-level indirect effect can appear significantly larger than its per capita analogue. In addition, we consider a recently proposed epidemiological non-pharmaceutical intervention (by the means of recovered individuals) used over the COVID-19 pandemic, referred to as 'shielding', and study its impact on our mathematical analysis. The shielding scheme is extended to take advantage of vaccination including imperfect vaccination.
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Journal articleEspinoza F, Re MD, Calvo RA, 2024,
Designing with community health workers in Latin America
, Interactions, Vol: 31, Pages: 55-57, ISSN: 1072-5520Community + Culture features practitioner perspectives on designing technologies for and with communities. We highlight compelling projects and provocative points of view that speak to both community technology practice and the interaction design field as a whole.
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Journal articleTu Y, Wu B, Ai W, et al., 2024,
Mechanical Failure of Core-Shell Cathode Particles: The Effects of Concentration-Dependent Material Properties and Phase Field Fracture Modelling
, ECS Meeting Abstracts, Vol: MA2024-01, Pages: 488-488<jats:p> The use of core-shell cathode particles in lithium-ion batteries is an attractive approach to enhancing energy density whilst retaining lifetime, through reducing undesired reactions at the electrode-electrolyte interface and limiting the volume change of the electrode particles. However, mechanical failure through the fracture and debonding of the core-shell interface is a major challenge. In this work, we employ a coupled finite-element model to predict and mitigate the mechanical failure of core-shell cathode structures, taking as example a particle of NMC811 (core) coated with NMC111 (shell). In particular, we focus on two aspects:</jats:p> <jats:p>The first one involves the assumptions of material properties as inputs of the model. The material properties are often considered constant by battery modelling researchers, yet these parameters can vary significantly during charge/discharge. For example, experiments have unveiled a three-orders-of-magnitude drop in the diffusion coefficient of NMC materials during discharge [1]. Here, we incorporate material properties obtained from experimental data, including concentration-dependent diffusion coefficient obtained from GITT measurement and partial molar volume derived from in situ X-ray diffraction data. Our results indicate that when assuming a concentration-dependent partial molar volume, the maximum values of tensile hoop stress in the shell are nearly three times lower than those predicted with constant average properties, diminishing the likelihood of fracture.</jats:p> <jats:p>When accounting for concentration-dependent diffusion coefficient, large concentration gradient is observed near the outer surface of the core due to reduced lithium mobility at high states of lithiation, hindering full electrode capacity utilisation. The significant concentration gradient and capacity underutilisation align with direct observations from experiments [2]
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Journal articleAlmukhtar A, Batcup C, Bowman M, et al., 2024,
Interventions to achieve environmentally sustainable operating theatres: an umbrella systematic review using the behaviour change wheel
, International Journal of Surgery, ISSN: 1743-9159Introduction: The healthcare sector is a major contributor to the climate crisis, and operating theatres (OTs) are one of the highest sources of emissions. To inform emissions reduction, this study aimed to (i) compare the outcomes of interventions targeting sustainable behaviours in OTs using the Triple Bottom Line framework, (ii) categorise the intervention strategies using the 5Rs (reduce, recycle, reuse, refuse, and renew) of circular economy, and (iii) examine Intervention Functions (IFs) using the Behaviour Change Wheel (BCW).Methods: Medline, Embase, PsychInfo, Scopus, and Web of Science databases were searched until June 2023 using the concepts: sustainability and surgery. The review was conducted in line with the Cochrane and Joanna Briggs Institution’s recommendations and was registered on PROSPERO. The results were reported in line with PRISMA, Supplemental Digital Content 1, https://links.lww.com/JS9/D210 (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines.Results: Sixteen reviews encompassing 43 life-cycle analyses, 30 interventions, 5 IFs, and 9 BCW policy categories were included. 28/30 (93%) interventions successfully led to sustainability improvements; however, the environmental outcomes were not suitable for meaningful comparisons due to their using different metrics and dependence on local factors. The ‘reduce’ strategy was the most prolific and commonly achieved through ‘education’ and/or ‘environmental restructuring’. However, single-session educational interventions were ineffective. Improving recycling relied on ‘environmental restructuring’. More intensive strategies such as ‘reuse’ require multiple intervention functions to achieve, either through a sustainability committee or through an intervention package.Conclusion: Policymakers must examine interventions within the local context. Comparing the outcomes of different interventions is difficult an
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Journal articleFerraro P, Penzkofer A, King C, et al., 2024,
Feedback control for distributed ledgers: an attack mitigation policy for DAG-based DLTs
, IEEE Transactions on Automatic Control, Vol: 69, Pages: 5492-5499, ISSN: 0018-9286In this paper we present a feedback approach to the design of an attack mitigation policy for DAG-based Distributed Ledgers. We develop a model to analyse the behaviour of the ledger under the so called Tips Inflation Attack , which endangers the liveness of transactions, and we design a control strategy to counteract this attack strategy. The efficacy of this approach is showcased through a theoretical analysis, in the form of two theorems about the stability properties of the ledger with and without the controller, and extensive Monte Carlo simulations of an agent-based model of the distributed ledger.
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Journal articleBallou N, Denisova A, Ryan R, et al., 2024,
The Basic Needs in Games Scale (BANGS): A new tool for investigating positive and negative video game experiences
, INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES, Vol: 188, ISSN: 1071-5819 -
Journal articleWang Y, Boyle D, 2024,
Constrained reinforcement learning using distributional representation for trustworthy quadrotor UAV tracking control
, IEEE Transactions on Automation Science and Engineering, ISSN: 1545-5955Simultaneously accurate and reliable tracking control for quadrotors in complex dynamic environments is challenging. The chaotic nature of aerodynamics, derived from drag forces and moment variations, makes precise identification difficult. Consequently, many existing quadrotor tracking systems treat these aerodynamic effects as simple ‘disturbances’ in conventional control approaches. We propose a novel and interpretable trajectory tracker integrating a distributional Reinforcement Learning (RL) disturbance estimator for unknown aerodynamic effects with a Stochastic Model Predictive Controller (SMPC). Specifically, the proposed estimator ‘Constrained Distributional REinforced-Disturbance-estimator’ (ConsDRED) effectively identifies uncertainties between the true and estimated values of aerodynamic effects. Control parameterization employs simplified affine disturbance feedback to ensure convexity, which is seamlessly integrated with the SMPC. We theoretically guarantee that ConsDRED achieves an optimal global convergence rate, and sublinear rates if constraints are violated with certain error decreases as neural network dimensions increase. To demonstrate practicality, we show convergent training, in simulation and real-world experiments, and empirically verify that ConsDRED is less sensitive to hyperparameter settings compared with canonical constrained RL. Our system substantially improves accumulative tracking errors by at least 70%, compared with the recent art. Importantly, the proposed ConsDRED-SMPC framework balances the trade-off between pursuing high performance and obeying conservative constraints for practical implementations. Note to Practitioners —This work is motivated by challenges in training Reinforcement Learning (RL) for autonomous navigation in unmanned aerial vehicles, but its implications extend to other high-criticality applications in, for example, healthcare and financial services. The implementation of RL algo
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Journal articleSmith F, Sadek M, Wan E, et al., 2024,
Codesigning AI with end-users: an AI literacy toolkit for nontechnical audiences
, Interacting with Computers, ISSN: 0953-5438This study addresses the challenge of limited AI literacy among the general public hindering effective participation in AI codesign. We present a card-based AI literacy toolkit designed to inform nontechnical audiences about AI and stimulate idea generation. The toolkit incorporates 16 competencies from the AI Literacy conceptual framework and employs ‘What if?’ prompts to encourage questioning, mirroring designers’ approaches. Using a mixed methods approach, we assessed the impact of the toolkit. In a design task with nontechnical participants (N = 50), we observed a statistically significant improvement in critical feedback and breadth of AI-related questions after toolkit use. Further, a codesign workshop involving six participants, half without an AI background, revealed positive effects on collaboration between practitioners and end-users, fostering a shared vision and common ground. This research emphasizes the potential of AI literacy tools to enhance the involvement of nontechnical audiences in codesigning AI systems, contributing to more inclusive and informed participatory processes.
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Conference paperWang Y, Qian Q, Boyle D, 2024,
Probabilistic constrained reinforcement learning with formal interpretability
, International Conference on Machine Learning, Publisher: MLResearchPress, Pages: 51303-51327, ISSN: 2640-3498Reinforcement learning can provide effective reasoning for sequential decision-making problems with variable dynamics. Such reasoning in practical implementation, however, poses a persistent challenge in interpreting the reward function and the corresponding optimal policy. Consequently, representing sequential decision-making problems as probabilistic inference can have considerable value, as, in principle, the inference offers diverse and powerful mathematical tools to infer the stochastic dynamics whilst suggesting a probabilistic interpretation of policy optimization. In this study, we propose a novel Adaptive Wasserstein Variational Optimization, namely AWaVO, to tackle these interpretability challenges. Our approach uses formal methods to achieve the interpretability for convergence guarantee, training transparency, and intrinsic decision-interpretation. To demonstrate its practicality, we showcase guaranteed interpretability with a global convergence rate Θ(1/√T) in simulation and in practical quadrotor tasks. In comparison with state-of-the-art benchmarks, including TRPO-IPO, PCPO, and CRPO, we empirically verify that AWaVO offers a reasonable trade-off between high performance and sufficient interpretability.
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Journal articleWu B, 2024,
Addressing the battery talent shortage with interdisciplinarity
, Nature Energy, ISSN: 2058-7546 -
Conference paperWang M, Zhou Y, Stewart R, 2024,
Soft Wearable Robotics: Innovative Knitting-Integrated Approaches for Pneumatic Actuators Design
, Pages: 234-238Soft wearable robotics presents an opportunity to bridge robotics and textiles, offering lightweight, flexible, and ergonomic solutions for human-robot interaction, but previous studies on wearable soft robotics primarily focus on actuator performance without also considering wearability and interactivity. A rudimentary attachment method is usually adopted using external fixation devices such as straps to attach actuators to the user's body, resulting in a poor wearing experience. This study focus on compatible and compact textile architectures to support actuators to be seamlessly integrated into daily wearing. It presents a research-through-design method to propose innovative knitting-integrated approaches for pneumatic actuator design to provide soft wearable robots with both aesthetic and functional values. Through a series of tests in which various knitting techniques and parameters are used to create sleeves that house silicone actuators, it explores design possibilities and understands the complex relationships between textiles and actuators. The findings contribute to advancing soft wearable robotics by offering practical solutions for integrating pneumatic actuators seamlessly into wearable textiles, thereby unlocking new possibilities for human-centered robotic systems.
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Journal articleLei G, Docherty R, Cooper SJ, 2024,
Materials science in the era of large language models: a perspective
, Digital Discovery, Vol: 3, Pages: 1257-1272, ISSN: 2635-098XLarge Language Models (LLMs) have garnered considerable interest due to their impressive natural language capabilities, which in conjunction with various emergent properties make them versatile tools in workflows ranging from complex code generation to heuristic finding for combinatorial problems. In this paper we offer a perspective on their applicability to materials science research, arguing their ability to handle ambiguous requirements across a range of tasks and disciplines means they could be a powerful tool to aid researchers. We qualitatively examine basic LLM theory, connecting it to relevant properties and techniques in the literature before providing two case studies that demonstrate their use in task automation and knowledge extraction at-scale. At their current stage of development, we argue LLMs should be viewed less as oracles of novel insight, and more as tireless workers that can accelerate and unify exploration across domains. It is our hope that this paper can familiarise materials science researchers with the concepts needed to leverage these tools in their own research.
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Journal articleXie R, Pinson P, Xu Y, et al., 2024,
Robust Generation Dispatch With Purchase of Renewable Power and Load Predictions
, IEEE Transactions on Sustainable Energy, Vol: 15, Pages: 1486-1501, ISSN: 1949-3029The increasing use of renewable energy sources (RESs) and responsive loads has made power systems more uncertain. Meanwhile, thanks to the development of advanced metering and forecasting technologies, predictions by RES and load owners are now attainable. Many recent studies have revealed that pooling the predictions from RESs and loads can help the operators predict more accurately and make better dispatch decisions. However, how the prediction purchase decisions are made during the dispatch processes needs further investigation. This paper fills the research gap by proposing a novel robust generation dispatch model considering the purchase and use of predictions from RESs and loads. The prediction purchase decisions are made in the first stage, which influence the accuracy of predictions from RESs and loads, and further the uncertainty set and the worst-case second-stage dispatch performance. This two-stage procedure is essentially a robust optimization problem with decision-dependent uncertainty (DDU). A mapping-based column-and-constraint generation (C and CG) algorithm is developed to overcome the potential failures of traditional solution methods in detecting feasibility, guaranteeing convergence, and reaching optimal strategies under DDU. Case studies demonstrate the effectiveness, necessity, and scalability of the proposed model and algorithm.
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