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  • Journal article
    Wang J, Yang K, Sun S, Ma Q, Yi G, Chen X, Wang Z, Yan W, Liu X, Cai Q, Zhao Yet al., 2023,

    Advances in thermal-related analysis techniques for solid-state lithium batteries

    , INFOMAT, Vol: 5
  • Journal article
    Zendle D, Flick C, Deterding S, Cutting J, Gordon-Petrovskaya E, Drachen Aet al., 2023,

    The Many Faces of Monetisation: Understanding the Diversity and Extremity of Player Spending in Mobile Games via Massive-scale Transactional Analysis

    , Games: Research and Practice, Vol: 1, Pages: 1-28

    <jats:p>With the rise of microtransactions, particularly in the mobile games industry, there has been ongoing concern that games reliant on these obtain substantial revenue from a small proportion of heavily involved individuals, to an extent that may be financially burdensome to these individuals. Yet despite substantive grey literature and speculation on this topic, there is little robust data available. We explore the revenue distribution in microtransaction-based mobile games using a transactional dataset of $4.7B in in-game spending drawn from 69,144,363 players of 2,873 mobile games over the course of 624 days. We find diverse revenue distributions in mobile games, ranging from a “uniform” cluster, in which all spenders invest approximately similar amounts, to “hyper-Pareto” games, in which a large proportion of revenue (approximately 38%) stems from 1% of spenders alone. Specific kinds of games are typified by higher spending: The more a game relies on its top 1% for revenue generation, the more these individuals tend to spend, with simulated gambling products (“social casinos”) at the top. We find a small subset of games across all genres, clusters, and age ratings in which the top 1% of gamers are highly financially involved—spending an average of $66,285 each in the 624 days under evaluation in the most extreme case. We discuss implications for future studies on links between gaming and wellbeing.</jats:p>

  • Journal article
    Deterding S, Mitchell K, Kowert R, King Bet al., 2023,

    Games Futures I

    , Games: Research and Practice, Vol: 1, Pages: 1-4

    <jats:p>Games Futures collect short opinion pieces by industry and research veterans and new voices envisioning possible and desirable futures and needs for games and playable media. This inaugural series features eight of over thirty pieces.</jats:p>

  • Journal article
    Deterding S, Mitchell K, Kowert R, King Bet al., 2023,

    Inaugural Editorial: A Lighthouse for Games and Playable Media

    , Games: Research and Practice, Vol: 1, Pages: 1-9

    <jats:p> In games and playable media, almost nothing is as it was at the turn of the millennium. Digital and analog games have exploded in reach, diversity, and relevance. Digital platforms and globalisation have shifted and fragmented their centres of gravity and how they are made and played. Games are converging with other media, technologies, and arts into a wide field of playable media. Games research has similarly exploded in volume and fragmented into disciplinary specialisms. All this can be deeply disorienting. The journal <jats:italic>Games: Research and Practice</jats:italic> wants to offer a lighthouse that helps readers orient themselves in this new, ever-shifting reality of games industry and games research. </jats:p>

  • Journal article
    Yu Z, Sadati H, Perera S, Houser H, Childs P, Nanayakkara Tet al., 2023,

    Tapered whisker reservoir computing for real-time terrain identification-based navigation

    , Scientific Reports, Vol: 13, Pages: 1-13, ISSN: 2045-2322

    This paper proposes a new method for real-time terrain recognition-based navigation for mobile robots. Mobile robots performing tasks in unstructured environments need to adapt their trajectories in real-time to achieve safe and efficient navigation in complex terrains. However, current methods largely depend on visual and IMU (inertial measurement units) that demand high computational resources for real-time applications. In this paper, a real-time terrain identification-based navigation method is proposed using an on-board tapered whisker-based reservoir computing system. The nonlinear dynamic response of the tapered whisker was investigated in various analytical and Finite Element Analysis frameworks to demonstrate its reservoir computing capabilities. Numerical simulations and experiments were cross-checked with each other to verify that whisker sensors can separate different frequency signals directly in the time domain and demonstrate the computational superiority of the proposed system, and that different whisker axis locations and motion velocities provide variable dynamical response information. Terrain surface-following experiments demonstrated that our system could accurately identify changes in the terrain in real-time and adjust its trajectory to stay on specific terrain.

  • Journal article
    Yu X, Baker C, Brown M, Ghajari Met al., 2023,

    In-depth bicycle collision reconstruction: from a crash helmet to brain injury evaluation

    , Bioengineering, Vol: 10, Pages: 1-16, ISSN: 2306-5354

    Traumatic brain injury (TBI) is a prevalent injury among cyclists experiencing head collisions. In legal cases, reliable brain injury evaluation can be difficult and controversial as mild injuries cannot be diagnosed with conventional brain imaging methods. In such cases, accident reconstruction may be used to predict the risk of TBI. However, lack of collision details can render accident reconstruction nearly impossible. Here, we introduce a reconstruction method to evaluate the brain injury in a bicycle–vehicle collision using the crash helmet alone. Following a thorough inspection of the cyclist’s helmet, we identified a severe impact, a moderate impact and several scrapes, which helped us to determine the impact conditions. We used our helmet test rig and intact helmets identical to the cyclist’s helmet to replicate the damage seen on the cyclist’s helmet involved in the real-world collision. We performed both linear and oblique impacts, measured the translational and rotational kinematics of the head and predicted the strain and the strain rate across the brain using a computational head model. Our results proved the hypothesis that the cyclist sustained a severe impact followed by a moderate impact on the road surface. The estimated head accelerations and velocity (167 g, 40.7 rad/s and 13.2 krad/s2) and the brain strain and strain rate (0.541 and 415/s) confirmed that the severe impact was large enough to produce mild to moderate TBI. The method introduced in this study can guide future accident reconstructions, allowing for the evaluation of TBI using the crash helmet only.

  • Journal article
    Pinson P, 2023,

    Distributionally robust trading strategies for renewable energy producers

    , IEEE Transactions on Energy Markets, Policy and Regulation, Vol: 1, Pages: 37-47

    Renewable energy generation is offered through electricity markets, quite some time in advance. This then leads to a problem of decision-making under uncertainty, which may be seen as a newsvendor problem. Contrarily to the conventional case for which underage and overage penalties are known, such penalties in the case of electricity markets are unknown, and difficult to estimate. In addition, one is actually only penalized for either overage or underage, not both. Consequently, we look at a slightly different form of a newsvendor problem, for a price-taker participant offering in electricity markets, which we refer to as Bernoulli newsvendor problem. After showing that its solution is consistent with that for the classical newsvendor problem, we then introduce distributionally robust versions, with ambiguity possibly about both the probabilistic forecasts for power generation and the chance of success of the Bernoulli variable. Both versions of the distributionally robust Bernoulli newsvendor problem admit closed-form solutions. We finally use simulation studies, as well as a real-world case-study application, to illustrate the workings and benefits from the approach.

  • Journal article
    Labazanova L, Peng S, Qiu L, Lee H-Y, Nanayakkara T, Navarro-Alarcon Det al., 2023,

    Self-Reconfigurable Soft-Rigid Mobile Agent With Variable Stiffness and Adaptive Morphology

    , IEEE ROBOTICS AND AUTOMATION LETTERS, Vol: 8, Pages: 1643-1650, ISSN: 2377-3766
  • Journal article
    Fu Y, Yang K, Xue S, Li W, Chen S, Song Y, Song Z, Zhao W, Zhao Y, Pan F, Yang L, Sun Xet al., 2023,

    Surface Defects Reinforced Polymer-Ceramic Interfacial Anchoring for High-Rate Flexible Solid-State Batteries

    , ADVANCED FUNCTIONAL MATERIALS, Vol: 33, ISSN: 1616-301X
  • Journal article
    Burge T, Jeffers J, Myant C, 2023,

    Applying machine learning methods to enable automatic customisation of knee replacement implants from CT data

    , Scientific Reports, Vol: 13, Pages: 1-9, ISSN: 2045-2322

    The aim of this study was to develop an automated pipeline capable of designing custom total knee replacement implants from CT scans. The developed pipeline firstly utilised a series of machine learning methods including classification, object detection, and image segmentation models, to extract geometrical information from inputted DICOM files. Statistical shape models then used the information to create femur and tibia 3D surface model predictions which were ultimately used by computer aided design scripts to generate customised implant designs. The developed pipeline was trained and tested using CT scan images, along with segmented 3D models, obtained for 98 Korean Asian subjects. The performance of the pipeline was tested computationally by virtually fitting outputted implant designs with ‘ground truth’ 3D models for each test subject’s bones. This demonstrated the pipeline was capable of repeatably producing highly accurate designs, and its performance was not impacted by subject sex, height, age, or knee side. In conclusion, a robust, accurate and automatic, CT-based total knee replacement customisation pipeline was shown to be feasible and could afford significant time and cost advantages over conventional methods. The pipeline framework could also be adapted to enable customisation of other medical implants.

  • Conference paper
    Reed CN, McPherson AP, 2023,

    The Body as Sound: Unpacking Vocal Embodiment through Auditory Biofeedback

    , TEI '23: Seventeenth International Conference on Tangible, Embedded, and Embodied Interaction, Publisher: ACM
  • Journal article
    Tillfors M, Van Zalk N, Boersma K, Anniko Met al., 2023,

    Longitudinal links between adolescent social anxiety and depressive symptoms: stressful experiences at home, in school and with peers

    , Nordic Psychology, Pages: 1-20, ISSN: 0029-1463

    Social anxiety and depressive symptoms often co-occur during early adolescence but contributing factors to this development are still a matter of debate. This study examined the role of daily stressors (peers, school and homelife) in the links between adolescent social anxiety and depressive symptoms. 7-8th graders at Time 1 (N = 2,752, Mage = 13.65; 47.5% girls) were followed across three time-points. Cross-lagged path models showed that depressive symptoms predicted later social anxiety, but not vice versa. Bidirectional links were identified between peer stress and social anxiety, and between school/homelife stress and depressive symptoms, respectively. Indirect effects of social anxiety, depressive symptoms, and daily stressors were found, though stressors did not mediate the links between social anxiety and depressive symptoms (or vice versa). Our findings indicate an intricate role of daily stressors in different domains on the links between social anxiety and depressive symptoms.

  • Journal article
    Camara O, Xu Q, Park J, Yu S, Lu X, Dzieciol K, Schierholz R, Tempel H, Kungl H, George C, Mayer J, Basak S, Eichel R-Aet al., 2023,

    Effect of Low Environmental Pressure on Sintering Behavior of NASICON-Type Li1.3Al0.3Ti1.7(PO4)3 Solid Electrolytes: An<i> In</i><i> Situ</i> ESEM Study

    , CRYSTAL GROWTH & DESIGN, ISSN: 1528-7483
  • Journal article
    Chard I, van Zalk N, Picinali L, 2023,

    Virtual reality exposure therapy for reducing social anxiety in stuttering: a randomized controlled pilot trial

    , Frontiers in Digital Health, Vol: 5, Pages: 1-14, ISSN: 2673-253X

    We report on findings from the first randomized controlled pilot trial of virtual reality exposure therapy (VRET) developed specifically for reducing social anxiety associated with stuttering. People who stutter with heightened social anxiety were recruited from online adverts and randomly allocated to receive VRET (n = 13) or be put on a waitlist (n = 12). Treatment was delivered remotely using a smartphone-based VR headset. It consisted of three weekly sessions, each comprising both performative and interactive exposure exercises, and was guided by a virtual therapist. Multilevel model analyses failed to demonstrate the effectiveness of VRET at reducing social anxiety between pre- and post-treatment. We found similar results for fear of negative evaluation, negative thoughts associated with stuttering, and stuttering characteristics. However, VRET was associated with reduced social anxiety between post-treatment and one-month follow-up. These pilot findings suggest that our current VRET protocol may not be effective at reducing social anxiety amongst people who stutter, though might be capable of supporting longer-term change. Future VRET protocols targeting stuttering-related social anxiety should be explored with larger samples. The results from this pilot trial provide a solid basis for further design improvements and for future research to explore appropriate techniques for widening access to social anxiety treatments in stuttering.

  • Journal article
    Yu X, Baker CE, Ghajari M, 2023,

    Head impact location, speed and angle from falls and trips in the workplace

    , Annals of Biomedical Engineering, Pages: 1-16, ISSN: 0090-6964

    Traumatic 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

  • Journal article
    Calvo RA, Peters D, Moradbakhti L, Cook D, Rizos G, Schuller B, Kallis C, Wong E, Quint Jet al., 2023,

    Assessing the feasibility of a text-based conversational agent for asthma support: protocol for a mixed methods observational study

    , JMIR Research Protocols, Vol: 12, Pages: 9-9, ISSN: 1929-0748

    BACKGROUND: Despite efforts, the UK death rate from asthma is the highest in Europe, and 65% of people with asthma in the United Kingdom do not receive the professional care they are entitled to. Experts have recommended the use of digital innovations to help address the issues of poor outcomes and lack of care access. An automated SMS text messaging-based conversational agent (ie, chatbot) created to provide access to asthma support in a familiar format via a mobile phone has the potential to help people with asthma across demographics and at scale. Such a chatbot could help improve the accuracy of self-assessed risk, improve asthma self-management, increase access to professional care, and ultimately reduce asthma attacks and emergencies. OBJECTIVE: The aims of this study are to determine the feasibility and usability of a text-based conversational agent that processes a patient's text responses and short sample voice recordings to calculate an estimate of their risk for an asthma exacerbation and then offers follow-up information for lowering risk and improving asthma control; assess the levels of engagement for different groups of users, particularly those who do not access professional services and those with poor asthma control; and assess the extent to which users of the chatbot perceive it as helpful for improving their understanding and self-management of their condition. METHODS: We will recruit 300 adults through four channels for broad reach: Facebook, YouGov, Asthma + Lung UK social media, and the website Healthily (a health self-management app). Participants will be screened, and those who meet inclusion criteria (adults diagnosed with asthma and who use WhatsApp) will be provided with a link to access the conversational agent through WhatsApp on their mobile phones. Participants will be sent scheduled and randomly timed messages to invite them to engage in dialogue about their asthma risk during the period of study. After a data collection period (28

  • Journal article
    Zhao S, Haskell WB, Cardin M-A, 2023,

    A flexible system design approach for multi-facility capacity expansion problems with risk aversion

    , IISE Transactions, Vol: 55, Pages: 187-200, ISSN: 2472-5854

    This paper studies a model for risk aversion when designing a flexible capacity expansion plan for a multi-facility system. In this setting, the decision maker can dynamically expand the capacity of each facility given observations of uncertain demand. We model this situation as a multi-stage stochastic programming problem, and we express risk aversion through the conditional value-at-risk (CVaR) and a mean-CVaR objective. We optimize the multi-stage problem over a tractable family of if–then decision rules using a decomposition algorithm. This algorithm decomposes the stochastic program over scenarios and updates the solutions via the subgradients of the function of cumulative future costs. To illustrate the practical effectiveness of this method, we present a numerical study of a decentralized waste-to-energy system in Singapore. The simulation results show that the risk-averse model can improve the tail risk of investment losses by adjusting the weight factors of the mean-CVaR objective. The simulations also demonstrate that the proposed algorithm can converge to high-performance policies within a reasonable time, and that it is also more scalable than existing flexible design approaches.

  • Journal article
    Campanella D, Bertoni G, Zhu W, Trudeau M, Girard G, Savoie S, Clement D, Guerfi A, Vijh A, George C, Belanger D, Paolella Aet al., 2023,

    Gram-scale carbothermic control of LLZO garnet solid electrolyte particle size

    , CHEMICAL ENGINEERING JOURNAL, Vol: 457, ISSN: 1385-8947
  • Journal article
    Cutting J, Deterding S, Demediuk S, Sephton Net al., 2023,

    Difficulty-skill balance does not affect engagement and enjoyment: a pre-registered study using artificial intelligence-controlled difficulty

    , Royal Society Open Science, Vol: 10, Pages: 1-15, ISSN: 2054-5703

    How does the difficulty of a task affect people's enjoyment and engagement? Intrinsic motivation and flow theories posit a 'goldilocks' optimum where task difficulty matches performer skill, yet current work is confounded by questionable measurement practices and lacks scalable methods to manipulate objective difficulty-skill ratios. We developed a two-player tactical game test suite with an artificial intelligence (AI)-controlled opponent that uses a variant of the Monte Carlo Tree Search algorithm to precisely manipulate difficulty-skill ratios. A pre-registered study (n = 311) showed that our AI produced targeted difficulty-skill ratios without participants noticing the manipulation, yet different ratios had no significant impact on enjoyment or engagement. This indicates that difficulty-skill balance does not always affect engagement and enjoyment, but that games with AI-controlled difficulty provide a useful paradigm for rigorous future work on this issue.

  • Journal article
    Wainwright T, Demirel P, 2023,

    Multiple logics in financialisation? Moving to carbon sustainability in build-to-rent development

    , Environment and Planning A: Economy and Space, Vol: 55, Pages: 22-45, ISSN: 0308-518X

    <jats:p> Real-estate has become an integral part of financialised economies, but while scholars have turned to examine the emergence of carbon markets, the role of carbon in real-estate finance has been broadly overlooked. Real-estate as a sector has been historically slow to innovate, particularly in response to pressure from climate change. More recently, the attitude of UK build-to-rent (BTR) developers to carbon is changing, partly due to global initiatives including the United Nation's Sustainable Development Goals (UNSDGs), but also pressure from institutional investors. In this paper, we provide nuanced insight into the emergence of new logics within financialisation's governance in the UK BTR sector and examine how investors attempt to steer developers into adopting low carbon building materials and designs, while identifying barriers. First, we highlight the multiplicity of financialisation's logics wrapped within assets, highlighting the presence of a carbon logic, which creates pressure for low-carbon activity. Second, we contribute to debates on assetisation and financialisation by examining the tools and knowledge used to create low-carbon real-estate assets, and how carbon attributes are ‘retrofitted’ into existing asset classes. </jats:p>

  • Book chapter
    Peters D, Calvo RA, 2023,

    Self-Determination Theory and Technology Design

    , The Oxford Handbook of Self-Determination Theory, Editors: Ryan, Publisher: Oxford University Press, ISBN: 9780197600047
  • Conference paper
    Li K, Chappell D, Rojas N, 2023,

    Immersive Demonstrations are the Key to Imitation Learning

    , IEEE International Conference on Robotics and Automation
  • Journal article
    Dahari A, Kench S, Squires I, Cooper SJet al., 2023,

    Fusion of Complementary 2D and 3D Mesostructural Datasets Using Generative Adversarial Networks

    , ADVANCED ENERGY MATERIALS, Vol: 13, ISSN: 1614-6832
  • Journal article
    He R, Xie W, Wu B, Brandon NP, Liu X, Li X, Yang Set al., 2023,

    Towards interactional management for power batteries of electric vehicles

    , RSC Advances: an international journal to further the chemical sciences, Vol: 13, Pages: 2036-2056, ISSN: 2046-2069

    With the ever-growing digitalization and mobility of electric transportation, lithium-ion batteries are facing performance and safety issues with the appearance of new materials and the advance of manufacturing techniques. This paper presents a systematic review of burgeoning multi-scale modelling and design for battery efficiency and safety management. The rise of cloud computing provides a tactical solution on how to efficiently achieve the interactional management and control of power batteries based on the battery system and traffic big data. The potential of selecting adaptive strategies in emerging digital management is covered systematically from principles and modelling, to machine learning. Specifically, multi-scale optimization is expounded in terms of materials, structures, manufacturing and grouping. The progress on modelling, state estimation and management methods is summarized and discussed in detail. Moreover, this review demonstrates the innovative progress of machine learning based data analysis in battery research so far, laying the foundation for future cloud and digital battery management to develop reliable onboard applications.

  • Journal article
    Cutting J, Deterding S, Demediuk S, Sephton Net al., 2023,

    Difficulty-skill balance does not affect engagement and enjoyment: A pre-registered study using AI-controlled difficulty

    , Royal Society Open Science, ISSN: 2054-5703

    How does the difficulty of a task affect people’s enjoyment and engagement? Intrinsic motivation and flow theories posit a ‘goldilocks’ optimum where task difficulty matches performer skill, yet current work is confounded by questionable measurement practices and lacks scalable methods to manipulate objective difficulty-skill ratios. We developed a 2-player tactical game test suite with an AI-controlled opponent that uses a variant of the Monte Carlo Tree Search algorithm to precisely manipulate difficulty-skill ratios. A pre-registered study (n=311) showed that our AI produced targeted difficulty-skill ratios without participants noticing the manipulation, yet different ratios had no significant impact on enjoyment or engagement. This indicates that difficulty-skill balance does not always affect engagement and enjoyment, but that games with AI-controlled difficulty provide a useful paradigm for rigorous future work on this issue.

  • Journal article
    He L, Maiolino P, Leong F, Lalitharatne T, Lusignan SD, Ghajari M, Iida F, Nanayakkara Tet al., 2023,

    Robotic simulators for tissue examination training with multimodal sensory feedback

    , IEEE Reviews in Biomedical Engineering, Vol: 16, Pages: 514-529, ISSN: 1941-1189

    Tissue examination by hand remains an essential technique in clinical practice. The effective application depends on skills in sensorimotor coordination, mainly involving haptic, visual, and auditory feedback. The skills clinicians have to learn can be as subtle as regulating finger pressure with breathing, choosing palpation action, monitoring involuntary facial and vocal expressions in response to palpation, and using pain expressions both as a source of information and as a constraint on physical examination. Patient simulators can provide a safe learning platform to novice physicians before trying real patients. This paper reviews state-of-the-art medical simulators for the training for the first time with a consideration of providing multimodal feedback to learn as many manual examination techniques as possible. The study summarizes current advances in tissue examination training devices simulating different medical conditions and providing different types of feedback modalities. Opportunities with the development of pain expression, tissue modeling, actuation, and sensing are also analyzed to support the future design of effective tissue examination simulators.

  • Conference paper
    Alattar A, Hmida IB, Renda F, Kormushev Pet al., 2023,

    Kinematic-Model-Free Tip Position Control of Reconfigurable and Growing Soft Continuum Robots

    Soft robots have many advantages over their rigid counterparts. These include their inherent compliance, lightweight and high adaptability to cluttered workspaces. Soft continuum robots, biologically inspired snake-like robots, are hyper-redundant and highly deformable. These robots can be challenging to control due to their complex kinematic and dynamic models. This paper presents a novel kinematic-model-free controller that uses a quasi-static assumption in order to control the tip-position of soft continuum robots with threadlike actuation while compensating for gravity simultaneously. The controller was tested on simulated continuum soft robots to demonstrate its ability to guide the tip while following a given trajectory. Novel kinematic-model-free control methods are introduced for soft robots' route and length control. The robustness of the controller is demonstrated with an actuator-failure test. The kinematic-model-free controller provides an adaptive control method for static, re-configuring, and growing soft continuum robots with threadlike actuation.

  • Conference paper
    Cedeno MR, Baxter W, Porat T, Peck Jet al., 2023,

    A METHOD FOR PRESCRIBING PSYCHOLOGICAL OWNERSHIP: A PROJECT HANDOVER CASE STUDY

    , Pages: 251-261

    Among the topics of psychological ownership (PO) within current literature, a significant gap exists in understanding PO within a prescriptive lens. This study will examine how instigating the PO mapping method will help us understand how the PO mapping method can support an ownership journey. In addition, we want to see how we can create a prescriptive ownership structure that one follows rather than using the tool as a descriptive method. To do this we will follow a Research Through Design methodology and test the PO mapping method in an organisational case study. We believe that the PO mapping method can help frame and guide organisational project handovers. We want to examine the factors that influence the parties (project teams) emergence and relinquishment of ownership, and how that affects the feeling of ownership of a project over time. Based on this understanding we will derive prescriptive phases to integrate into our PO mapping method. Thus this study demonstrates how the PO mapping method can be used in different contexts to support and provide prescriptive guidance for ownership journeys.

  • Journal article
    Ranjan A, Angelini F, Nanayakkara T, Garabini Met al., 2023,

    Design Guidelines for Bioinspired Adaptive Foot for Stable Interaction With the Environment

    , IEEE/ASME Transactions on Mechatronics, ISSN: 1083-4435

    Robotic exploration in natural environments requires adaptable, resilient, and stable interactions with uncertain terrains. Most state-of-the-art legged robots utilize flat or ball feet that lack adaptability and are prone to slip due to point contact with the ground. In this article, we present guidelines to design an adaptive foot that can interact with the terrain to achieve a stable configuration. The foot is inspired by goat hoof anatomy that incorporates roll and yaw rotations in the Fetlock and Pastern joints, respectively. To ensure adaptability with stability in physical interaction and to prevent the foot from collapsing, we provide a lower bound on each joint&#x0027;s stiffness. In addition, we also render an upper bound to conform to the high force exchange during interactions with the ground consisting of certain roughness. Based on these guidelines, we design the hoof and experimentally validate the theoretical results with a loading test setup in lab settings. We use four different friction materials with various triangular, rectangular, and semicircular extrusions to simulate common ground features. We observe that hooved pads require more load for the system to be unstable. Any anatomically inspired foot can be designed based on the guidelines proved analytically and experimentally in this article.

  • Conference paper
    Rizos G, Calvo RA, Schuller BW, 2023,

    Positive-Pair Redundancy Reduction Regularisation for Speech-Based Asthma Diagnosis Prediction

    , ISSN: 1520-6149

    Asthma affects an estimated 334 million people worldwide, causing over 461 000 deaths. Exacerbations or asthma attacks can be predicted with new sensor technologies. We explore how recordings of human voice, and machine learning can provide better diagnostics for pulmonary diseases like asthma, as well as tools for helping patients better manage it. Past studies have focused on data collection processes that either mimic traditional auscultation, or make multi-sensor measurements, where the application of specialised recording hardware is required, possibly by expert personnel. This is costly and places limits on the size of the studies (e.g., number of study participants, and recording devices). In this paper, we consider another avenue, that of modelling self-recorded voice samples made using regular smartphones, along with self-reported clinical diagnosis annotations; specifically of asthma. We propose the usage of self-supervised learning that aims to reduce within-class representation redundancy among heterogeneous samples as an auxiliary task to promote robust, bias-free learning. The application of our method achieves an absolute increase of 1.80% in area under the Precision-Recall curve, compared to not using it, and a total of 3.54% compared to our baseline.

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