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  • Journal article
    Lou Z, Min X, Li G, Avery J, Stewart Ret al., 2024,

    Advancing sensing resolution of impedance hand gesture recognition devices

    , IEEE Journal of Biomedical and Health Informatics, Vol: 28, Pages: 5855-5864, ISSN: 2168-2194

    Gestures are composed of motion information (e.g. movements of fingers) and force information (e.g. the force exerted on fingers when interacting with other objects). Current hand gesture recognition solutions such as cameras and strain sensors primarily focus on correlating hand gestures with motion information and force information is seldom addressed. Here we propose a bio-impedance wearable that can recognize hand gestures utilizing both motion information and force information. Compared with previous impedance-based gesture recognition devices that can only recognize a few multi-degrees-of-freedom gestures, the proposed device can recognize 6 single-degree-of-freedom gestures and 20 multiple-degrees-of-freedom gestures, including 8 gestures in 2 force levels. The device uses textile electrodes, is benchmarked over a selected frequency spectrum, and uses a new drive pattern. Experimental results show that 179 kHz achieves the highest signal-to-noise ratio (SNR) and reveals the most distinct features. By analyzing the 49,920 samples from 6 participants, the device is demonstrated to have an average recognition accuracy of 98.96%. As a comparison, the medical electrodes achieved an accuracy of 98.05%.
  • Journal article
    Chen L, Cai Z, Jiang Z, Luo J, Sun L, Childs P, Zuo Het 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: 0954-1810

    Divergent 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.
  • Journal article
    Zou Y, Zhao C, Childs P, Luh D-Bet al., 2024,

    Cross-cultural design in costume: case study on totemic symbols of China and Thailand

    , Humanities and Social Sciences Communications, Vol: 11, ISSN: 2662-9992

    Cross-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.
  • Journal article
    Aunger R, Deterding S, Zhao X, Baxter Wet 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: 0962-8436

    People 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'.
  • Journal article
    Lin L, Hamedmoghadam H, Shorten R, Stone Let al., 2024,

    Quantifying indirect and direct vaccination effects arising in the SIR model

    , JOURNAL OF THE ROYAL SOCIETY INTERFACE, Vol: 21, ISSN: 1742-5689
  • Journal article
    Yu Z, Childs P, Ge Y, Nanayakkara Tet al., 2024,

    Whisker sensor for robot environments perception: a review

    , IEEE Sensors Journal, Vol: 24, Pages: 28504-28521, ISSN: 1530-437X

    Nocturnal mammals such as rats heavily depend onwhisker based tactile perception to find their way through burrows and to identify objects. There is diversity in the whiskers in terms of the physical structure and nervous innervation. The robotics community has developed many different whisker sensors inspired by this biological basis. They take diverse mechanical, electronic, andcomputational approaches to use whiskers to identify the geometry, mechanical properties, and texture of objects. Some work address specific object features and some others address multiple features. Therefore, it is vital to have a comprehensive discussion of the literature and to understand the merits of both bio-inspired and pureengineered approaches to whisker based tactile perception. In this paper we report and discuss the progress in following areas: The body of mammalian whisker follicle, unimodal whiskered sensors, multimodal whiskered sensors with variable stiffness that can capture tactile sensory stimuli of different frequencies, obstacles detection, shape detection, texture classification and robot navigation using whiskers.
  • Journal article
    Cacciarelli D, Kulahci M, 2024,

    Active learning for industrial applications

    , QUALITY ENGINEERING, ISSN: 0898-2112
  • Conference paper
    Hu X, Li J, Picinali L, Hogg Aet 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: 396-403

    A 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.
  • Journal article
    Ferraro P, Yu JY, Ghosh R, Alam SE, Marecek J, Wirth F, Shorten Ret al., 2024,

    On unique ergodicity of coupled AIMD flows

    , International Journal of Control, Vol: 97, Pages: 2151-2161, ISSN: 0020-7179

    The 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].).
  • Journal article
    Espinoza F, Re MD, Calvo RA, 2024,

    Designing with community health workers in Latin America

    , Interactions, Vol: 31, Pages: 55-57, ISSN: 1072-5520

    Community + 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.
  • Journal article
    Cacciarelli D, Tyssedal JS, Kulahci M, 2024,

    Real-Time Sampling Strategies for Regression with Irrelevant Features

    , International Journal of Semantic Computing, Vol: 18, Pages: 417-435, ISSN: 1793-351X

    <jats:p> In the era of big data, companies are increasingly driven to amass vast amounts of data, particularly in process industries where advanced sensor technologies are prevalent. However, obtaining accurate labels or product information through quality inspections can be prohibitively expensive. Active learning emerges as a promising approach to optimize data sampling by prioritizing the most informative data points. Nevertheless, active learning strategies heavily rely on predictive models that are iteratively updated. Aligning with the principles of data-centric AI, this study highlights the detrimental effects of passively incorporating all available process variables into a predictive model for guiding data collection. Specifically, in real-time sampling strategies based on online active learning, the inclusion of irrelevant features significantly hampers the efficiency of the learning process. </jats:p>
  • Journal article
    Wu B, 2024,

    Addressing the battery talent shortage with interdisciplinarity

    , Nature Energy, Vol: 9, Pages: 1044-1045, ISSN: 2058-7546
  • Journal article
    Pan Y, Ruan H, Wu B, Regmi YN, Wang H, Brandon NPet al., 2024,

    A machine learning driven 3D+1D model for efficient characterization of proton exchange membrane fuel cells

    , Energy and AI, Vol: 17, ISSN: 2666-5468

    The 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.
  • Journal article
    Heath BE, Suzuki R, LePenru NP, Skinner J, Orme CDL, Ewers RM, Sethi SS, Picinali Let al., 2024,

    Spatial ecosystem monitoring with a Multichannel Acoustic Autonomous Recording Unit (MAARU)

    , Methods in Ecology and Evolution, Vol: 15, Pages: 1568-1579, ISSN: 2041-210X

    1. 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
  • Thesis dissertation
    Hewitt S, 2024,

    Emotions and Exercise: Mapping Emotions through Exercise to Design for a Healthier Lifestyle

    This thesis details an investigation of the relationship between human emotions and exercise. A new understanding of the emotional experiences resulting from taking part in exercise is reported and used to inform the development of visualisations and tools to support those seeking to design new exercise interventions. The key argument of this research is that if we can understand fully the emotional drivers for, and barriers against exercise, we can inform exercisers and designers alike how to best navigate an active lifestyle and the connected emotional experience. Analysis and synthesis of previous literature across the fields of public health, exercise psychology and sports science highlighted that emotions are both barriers and benefits to exercise. This led to an exploration of emotions and emotion models used within the fields of design, behavioural science and emotion psychology and, an investigation into existing design solutions to determine their efficacy and what makes them successful or unsuccessful. The interest then shifts to the specific relationship between emotions and exercise leading to a systematic literature review which discovers a lack of focused understanding of emotions and exercise types across the published research. Two user focused studies are presented, each exploring this relationship in breadth and depth to help understand better the link between emotions and exercise and build a more comprehensive map of their relationship. The first study, a quantitative exploration, presents an overview of the emotion and exercise relationship and identifies eight key emotions: calmness, excitement, interest, joy, pride, relief, satisfaction and, triumph. Each emotion is experienced differently depending on five variables: participant sex, time of elicitation, level of athlete, number of participants, and exercise type. The findings give a greater understanding of how different variables can affect individual’s emotional experiences and all
  • Journal article
    Li H, Zhou H, Li N, 2024,

    An integrated convolutional neural network-based surrogate model for crashworthiness performance prediction of hot-stamped vehicle panel components

    , MATEC Web of Conferences, Vol: 401, ISSN: 2261-236X

    During the structural design of vehicle components, Finite Element (FE) modelling has been extensively used for simulations of physical experiments. A typical design optimisation task requires iterative simulations to identify the optimum design, where FE simulations can be too time-consuming. Surrogate models have been developed to approximate complex simulations, which can reduce computational time and improve the efficiency of the design cycle. This paper presents a novel application of convolutional neural network (CNN) on rapid predictions of crashworthiness performance of vehicle panel components considering manufacturability. The dataset for training the model was generated based on the FE results of hot-stamped ultra-high strength steel (UHSS) B-pillar components. The formed components were analysed with a simplified lateral crash test to evaluate the deformation under impact. The trained model can instantly predict the deformation of the designed component with high accuracy compared to the FE results. Due to its high computational efficiency and precision, the surrogate model enables faster and more extensive design evaluations.
  • Journal article
    Wu H, Tian F, Tang C, Guo Z, Li Net al., 2024,

    Novel Heat Stamping of Ti6Al4V panels with high drawability and low springback

    , MATEC Web of Conferences, Vol: 401, ISSN: 2261-236X

    Conventional commonly used titanium stamping techniques like hot forming and superplastic forming each have distinct disadvantages. Hot forming usually leads to limited formability and significant springback, whilst superplastic forming often results in long cycle times, high energy use, and costly tooling. A novel, energy-efficient, and cost-effective Heat Stamping process emerges as a promising solution. Two types of Ti6Al4V components, cup-shaped and U-shaped, were produced using the novel Heat Stamping process, and their properties were examined. A defect-free cup-shaped component achieving a considerably high draw ratio of up to 1.8, was successfully fabricated. A moderate enhancement in the hardness of the component indicated a superior post-form strength achieved by Heat Stamping. Further analysis of the U-shaped components shows that the one produced using the novel method demonstrated a notable reduction in springback angle, from 4.8° to 0.7°, highlighting the potential of H eat Stamping in achieving high shape accuracy for near-net-shape forming.
  • Journal article
    Hair SW, Cooper SJ, Shaffer MSP, 2024,

    Beyond slurry cast: patterning of a monolithic active material sheet to form free-standing, solvent-free, and low-tortuosity battery electrodes

    , Cell Reports Physical Science, Vol: 5, ISSN: 2666-3864

    Commercial lithium-ion battery electrodes today are manufactured by slurry casting active material powder onto a metal current collector foil. This manufacturing process has become embedded over recent decades but limits commercial cell performance. This paper presents patterning of a monolithic active material sheet as an alternative to slurry casting. The concept is proven experimentally by laser drilling a pyrolytic graphite sheet to increase the gravimetric active material capacity from 10 mA h g−1 to 450 mA h g−1, when used as a negative lithium-intercalation electrode. Cell-level calculations show that, without changing the chemistry, a pyrolytic graphite sheet electrode with a hexagonal array of 5 μm diameter, 20 μm pitch channels could increase the gravimetric energy density of a LGM50 cell by 22% to 322 W h kg−1. By moving beyond slurry casting, patterned monolithic electrodes could enable batteries with lower cost, reduced energy intensity, and enhanced performance.
  • Journal article
    Tu Y, Wu B, Ai W, Martinez-Paneda Eet 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]
  • Journal article
    Ferraro P, Penzkofer A, King C, Shorten Ret 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: 0018-9286

    In 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.
  • Journal article
    Ballou N, Denisova A, Ryan R, Rigby CS, Deterding Set 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 article
    Ewers RM, Orme CDL, Pearse WD, Zulkifli N, Yvon-Durocher G, Yusah KM, Yoh N, Yeo DCJ, Wong A, Williamson J, Wilkinson CL, Wiederkehr F, Webber BL, Wearn OR, Wai L, Vollans M, Twining JP, Turner EC, Tobias JA, Thorley J, Telford EM, Teh YA, Tan HH, Swinfield T, Svátek M, Struebig M, Stork N, Sleutel J, Slade EM, Sharp A, Shabrani A, Sethi SS, Seaman DJI, Sawang A, Roxby GB, Rowcliffe JM, Rossiter SJ, Riutta T, Rahman H, Qie L, Psomas E, Prairie A, Poznansky F, Pillay R, Picinali L, Pianzin A, Pfeifer M, Parrett JM, Noble CD, Nilus R, Mustaffa N, Mullin KE, Mitchell S, Mckinlay AR, Maunsell S, Matula R, Massam M, Martin S, Malhi Y, Majalap N, Maclean CS, Mackintosh E, Luke SH, Lewis OT, Layfield HJ, Lane-Shaw I, Kueh BH, Kratina P, Konopik O, Kitching R, Kinneen L, Kemp VA, Jotan P, Jones N, Jebrail EW, Hroneš M, Heon SP, Hemprich-Bennett DR, Haysom JK, Harianja MF, Hardwick J, Gregory N, Gray R, Gray REJ, Granville N, Gill R, Fraser A, Foster WA, Folkard-Tapp H, Fletcher RJ, Fikri AH, Fayle TM, Faruk A, Eggleton P, Edwards DP, Drinkwater R, Dow RA, Döbert TF, Didham RK, Dickinson KJM, Deere NJ, de Lorm T, Dawood MM, Davison CW, Davies ZG, Davies RG, Dančák M, Cusack J, Clare EL, Chung A, Chey VK, Chapman PM, Cator L, Carpenter D, Carbone C, Calloway K, Bush ER, Burslem DFRP, Brown KD, Brooks SJ, Brasington E, Brant H, Boyle MJW, Both S, Blackman J, Bishop TR, Bicknell JE, Bernard H, Basrur S, Barclay MVL, Barclay H, Atton G, Ancrenaz M, Aldridge DC, Daniel OZ, Reynolds G, Banks-Leite Cet al., 2024,

    Thresholds for adding degraded tropical forest to the conservation estate

    , Nature, Vol: 631, Pages: 808-813, ISSN: 0028-0836

    Logged and disturbed forests are often viewed as degraded and depauperate environments compared with primary forest. However, they are dynamic ecosystems1 that provide refugia for large amounts of biodiversity2,3, so we cannot afford to underestimate their conservation value4. Here we present empirically defined thresholds for categorizing the conservation value of logged forests, using one of the most comprehensive assessments of taxon responses to habitat degradation in any tropical forest environment. We analysed the impact of logging intensity on the individual occurrence patterns of 1,681 taxa belonging to 86 taxonomic orders and 126 functional groups in Sabah, Malaysia. Our results demonstrate the existence of two conservation-relevant thresholds. First, lightly logged forests (<29% biomass removal) retain high conservation value and a largely intact functional composition, and are therefore likely to recover their pre-logging values if allowed to undergo natural regeneration. Second, the most extreme impacts occur in heavily degraded forests with more than two-thirds (>68%) of their biomass removed, and these are likely to require more expensive measures to recover their biodiversity value. Overall, our data confirm that primary forests are irreplaceable5, but they also reinforce the message that logged forests retain considerable conservation value that should not be overlooked.
  • Journal article
    Smith F, Sadek M, Wan E, Ito A, Mougenot Cet al., 2024,

    Codesigning AI with end-users: an AI literacy toolkit for nontechnical audiences

    , Interacting with Computers, ISSN: 0953-5438

    This 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.
  • Conference paper
    Wang Y, Qian Q, Boyle D, 2024,

    Probabilistic constrained reinforcement learning with formal interpretability

    , International Conference on Machine Learning, Publisher: MLResearchPress, Pages: 51303-51327, ISSN: 2640-3498

    Reinforcement 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.
  • Conference paper
    Wang M, Zhou Y, Stewart R, 2024,

    Soft Wearable Robotics: Innovative Knitting-Integrated Approaches for Pneumatic Actuators Design

    , Pages: 234-238

    Soft 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.
  • Journal article
    Lei 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: 2635-098X

    Large 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.
  • Journal article
    Chen L, Xiao S, Chen Y, Sun L, Childs PRN, Han Jet al., 2024,

    An artificial intelligence approach for interpreting creative combinational designs

    , Journal of Engineering Design, ISSN: 0954-4828

    Combinational creativity, a form of creativity involving the blending of familiar ideas, is pivotal in design innovation. While most research focuses on how combinational creativity in design is achieved through blending elements, this study focuses on the computational interpretation, specifically identifying the ‘base’ and ‘additive’ components that constitute a creative design. To achieve this goal, the authors propose a heuristic algorithm integrating computer vision and natural language processing technologies, and implement multiple approaches based on both discriminative and generative artificial intelligence architectures. A comprehensive evaluation was conducted on a dataset created for studying combinational creativity. Among the implementations of the proposed algorithm, the most effective approach demonstrated a high accuracy in interpretation, achieving 87.5% for identifying ‘base’ and 80% for ‘additive’. We conduct a modular analysis and an ablation experiment to assess the performance of each part in our implementations. Additionally, the study includes an analysis of error cases and bottleneck issues, providing critical insights into the limitations and challenges inherent in the computational interpretation of creative designs.
  • Journal article
    Xie R, Pinson P, Xu Y, Chen Yet al., 2024,

    Robust Generation Dispatch With Purchase of Renewable Power and Load Predictions

    , IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, Vol: 15, Pages: 1486-1501, ISSN: 1949-3029
  • Journal article
    Jakobsson Støre S, Van Zalk N, Granander Schwartz W, Nilsson V, Tillfors Met al., 2024,

    The relationship between social anxiety disorder and ADHD in adolescents and adults: a systematic review

    , Journal of Attention Disorders, Vol: 28, Pages: 1299-1319, ISSN: 1087-0547

    Objective:This review aimed to systematically gather empirical data on the link between social anxiety disorder and ADHD in both clinical and non-clinical populations among adolescents and adults.Method:Literature searches were conducted in PsycInfo, PubMed, Scopus, and Web of Science, resulting in 1,739 articles. After screening, 41 articles were included. Results were summarized using a narrative approach.Results:The prevalence of ADHD in adolescents and adults with SAD ranged from 1.1% to 72.3%, while the prevalence of SAD in those with ADHD ranged from 0.04% to 49.5%. Studies indicate that individuals with both SAD and ADHD exhibit greater impairments. All studies were judged to be of weak quality, except for two studies which were rated moderate quality.Discussion:Individuals with SAD should be screened for ADHD and vice versa, to identify this common comorbidity earlier. Further research is needed to better understand the prevalence of comorbid ADHD and SAD in adolescents.
  • Journal article
    Bonkile M, Jiang Y, Kirkaldy N, Sulzer V, Timms R, Wang H, Offer G, Wu Bet al., 2024,

    Is silicon worth it? Modelling degradation in composite silicon–graphite lithium-ion battery electrodes

    , Journal of Power Sources, Vol: 606, ISSN: 0378-7753

    The addition of silicon into graphite lithium-ion battery anodes has the potential to increase cell energy density. However, understanding the complex degradation behaviour in these composite systems remains a research challenge. Here, we developed a coupled electrochemical–mechanical model of a composite silicon/graphite electrode, including stress-driven crack formation and solid electrolyte interphase layer growth for each material, validated with experimental degradation data from an LG M50T cell. The model reveals self-limiting loss of silicon due to decreasing stress in the silicon as the silicon activity shifts to a lower state-of-charge. Higher C-rates can lead to lower degradation due to lower phase utilisation as voltage cut-offs are reached earlier. Increasing silicon content can reduce the stress in the silicon by distributing reaction current density over more material. Using this model, we explored whether the extra capacity from silicon is generally ‘worth’ the faster degradation compared to graphite-only electrodes. The model shows if you use the silicon, you lose it, as the higher initial capacity is rapidly lost with regular high depth-of-discharge events. However, silicon does have value if it enables full graphite utilisation without range anxiety; if high depth-of-discharge events are minimised then graphite’s superior longevity can be utilised while exploiting silicon’s high specific capacity. The model is integrated into PyBaMM (an open-source physics-based modelling platform); providing the research community and industry with the capability to reproduce our results and further explore the dynamic lifetime behaviour of composite electrodes.

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