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Journal articleThrän J, Green TC, Shorten R, 2026,
Levelised cost of demand response: estimating the cost-competitiveness of flexible demand
, Energy Conversion and Management, Vol: 349, ISSN: 0196-8904To make well-informed investment decisions, energy system stakeholders require reliable cost frameworks for demand response and storage technologies. While the levelised cost of storage permits comprehensive cost comparisons between different storage technologies, no generic cost measure for the comparison of different demand response schemes exists. This paper introduces the levelised cost of demand response, which is an analogous measure to the levelised cost of storage but crucially differs from it by considering consumer reward payments. Additionally, the value factor from cost estimations of variable renewable energy is adapted to account for the variable availability of demand response. The levelised cost of demand response is estimated for four direct load control schemes and twelve storage applications, and then contrasted against literature values for the levelised cost of the most competitive storage technologies. The direct load control schemes are vehicle-to-grid, smart charging, smart heat pumps, and heat pumps with thermal storage. The results show that only heat pumps with thermal storage consistently outcompete storage technologies, with EV-based schemes being competitive for some applications. The results and the underlying methodology offer a tool for energy system stakeholders to assess the competitiveness of demand response schemes even with limited user data.
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Journal articleTu Y, Wu B, Martínez-Pañeda E, 2026,
Phase field modelling of cracking and capacity fade in core-shell cathode particles for lithium-ion batteries
, Applied Energy, Vol: 403, ISSN: 0306-2619Core-shell electrode particles are a promising morphology control strategy for high-performance lithium-ion batteries. However, experimental observations reveal that these structures remain prone to mechanical failure, with shell fractures and core-shell debonding occurring after a single charge. In this work, we present a novel, comprehensive computational framework to predict and gain insight into the failure of core-shell morphologies and the associated degradation in battery performance. The fully coupled chemo-mechano-damage model presented captures the interplay between mechanical damage and electrochemical behaviours, enabling the quantification of particle cracking and capacity fade. Both bulk material fracture and interface debonding are captured by utilising the phase field method. We quantify the severity of particle cracking and capacity loss through case studies on a representative core-shell system (NMC811@NMC532). The results bring valuable insights into cracking patterns, underlying mechanisms, and their impact on capacity loss. Surface cracks are found to initiate when a significantly higher lithium concentration accumulates in the core compared to the shell. Interfacial debonding is shown to arise from localised hoop stresses near the core-shell interface, due to greater shell expansion. This debonding develops rapidly, impedes lithium-ion transport, and can lead to more than 10 % capacity loss after a single discharge. Furthermore, larger particles may experience crack branching driven by extensive tensile zones, potentially fragmenting the entire particle. The framework developed can not only bring new insight into the degradation mechanisms of core-shell particles but also be used to design electrode materials with improved performance and extended lifetime.
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Journal articleZhao Y, Chen Q, Li H, et al., 2026,
Recurrent U-Net-based Graph Neural Network (RUGNN) for accurate deformation predictions in sheet material forming
, Advanced Engineering Informatics, Vol: 69, ISSN: 1474-0346In recent years, various artificial intelligence-based surrogate models have been proposed to provide rapid manufacturability predictions of material forming processes. However, traditional AI-based surrogate models, typically built with scalar or image-based neural networks, are limited in their ability to capture complex 3D spatial relationships and to operate in a permutation-invariant manner. To overcome these issues, emerging graph-based surrogate models are developed using graph neural networks. This study developed a new graph neural network surrogate model named Recurrent U Net-based Graph Neural Network (RUGNN). The RUGNN model can achieve accurate predictions of sheet material deformation fields across multiple forming timesteps. The RUGNN model incorporates Gated Recurrent Units (GRUs) to model temporal dynamics and a U-Net inspired graph-based downsample/upsample mechanism to handle spatial long-range dependencies. A novel ’node-to-surface’ contact representation method was proposed, offering significant improvements in computational efficiency for large-scale contact interactions. The RUGNN model was validated using a cold forming case study and a more complex hot forming case study using aluminium alloys. Results demonstrate that the RUGNN model provides accurate deformation predictions closely matching ground truth FE simulations and outperforming several baseline GNN architectures. Model tuning was also performed to identify suitable hyperparameters, training strategies, and input feature representations. These results demonstrate that RUGNN is a reliable approach to support sheet material forming design by enabling accurate manufacturability predictions.
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Journal articlePinson P, 2026,
Editorial and introduction to the special section on the Bernanke’s review of the Bank of England’s forecasting activities
, International Journal of Forecasting, Vol: 42, Pages: 1-2, ISSN: 0169-2070 -
Journal articleBaxter W, Echavarri IV, Porat T, 2025,
Exploring the impact of changing government policy on vaccination eligibility for 50-64 year olds: A qualitative thematic analysis in England and Scotland
, HUMAN VACCINES & IMMUNOTHERAPEUTICS, Vol: 21, ISSN: 2164-5515 -
Journal articleChan EYK, Yu X, Qin C, et al., 2025,
Balancing efficiency and accuracy: extreme gradient boosting and neural networks for near real-time brain deformation prediction in sports collisions
, Engineering Applications of Artificial Intelligence, Vol: 162, ISSN: 0952-1976Rapid head motion during sports collisions can cause traumatic brain injury. Head motion can be measured with instrumented mouthguards and fed into finite element (FE) models to predict brain strain, a measure of brain deformation and injury. Due to the computational cost of FE models, deep neural networks have been developed for near real-time prediction. However, they are not used in pitch-side assessments due to their complexity and reliance on full kinematic data, which cannot be reliably transmitted in real-time.We propose an extreme gradient boosting (XGBoost) model with simple input of two kinematic features. Its accuracy and efficiency were compared with two deep learning models: a multilayer perceptron (MLP) using 20 features, and a convolutional neural network (CNN) using entire kinematics. All models were trained on 1701 rugby impacts collected with mouthguards and simulated using the Imperial brain FE model. The XGBoost model predicted strain in key brain regions, while the deep learning models predicted whole-brain strain distributions.All models showed reasonable accuracy in predicting regional strain, with R2 values 0.764–0.851 for XGBoost, 0.721–0.876 for MLP, and 0.744–0.887 for CNN. XGBoost required orders of magnitude fewer floating-point operations, and it used simple input that can be calculated on mouthguards and reliably transmitted in real-time.This study suggests that different models can be used at different stages of brain injury assessment. We hope that the XGBoost model proposed here will lower the barriers for adopting brain strain combined with instrumented mouthguards for pitch-side assessments from elite to grassroot collision sports.
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Journal articleVohra S, Childs P, 2025,
Boundary objects as catalysts for creative thinking in adolescent education
, Education Sciences, ISSN: 2227-7102Creative thinking has become more important in education globally due to industry demand and a fast-paced world. Boundary objects that can be tangible or digital objects are investigated to understand their role in facilitating creative thinking across 5 subject areas for teenagers aged 13-18 and their teachers, in their natural learning environment. A multiple case study method is used to investigate learners’ and their teachers’ experience in using boundary objects, to enable communication and understanding between individuals or groups in learning. Participants from an inner London secondary school comprised case groups: 8 Teachers and 16 Learners (8 from the lower school, aged 13-15 years, and 8 from the upper school, aged 16-18 years). Participants were invited through email and a short presentation. Consented participants were organised into male and female across teachers and students and were approached in lessons where boundary objects were being used. Data was collected through interviews and comprised photos of tool use, analysed throughReflexive Thematic Analysis for data analysis. The resulting five themes for teacher and student themes showed that boundary objects were perceived to facilitate creative thinking across all case groups within the studied context, with important insights such as iterative design which develops real-world skills; metacognition which is critical in learning that enables students to actively question their own thinking; memory which is very important in enabling students to remember what they learned and how; individual liberty suggesting that learning need not be linear nor prescribed but with freedom to learn in ways that are enjoyable and challenging too, amongst others. The study’s interpretive results indicate that when participants experience the use of boundary objects in a natural classroom or learning setting, the learning process is perceived to bring benefits that allow for the process of creative
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Journal articleVicente T, González-Toledo D, Cuevas-Rodríguez M, et al., 2025,
Exploring the relationship between task difficulty, head-related transfer function and spatial release from masking in a speech-on-speech experiment.
, Hear Res, Vol: 470It is known that individuals make use of spatial hearing cues to improve the audibility of a target signal and separate it from competing sounds. This phenomenon is known as spatial release from masking (SRM). Recent research has shown that this happens also when sources are located in the median plane, where interaural differences are limited. When assessing this within virtual conditions, it has been shown that employing individually measured head-related transfer functions (HRTFs) results in higher SRM abilities compared to using non-individual filters. In a previously published work, we found that Spanish speakers benefit from individual HRTFs when discriminating a target English speech from a single masker in the median plane. This study replicates the protocol of that previous work, varying the number of maskers and participants' English proficiency levels to explore relationships among task difficulty and HRTF use. Results from a first experiment show that English speakers behave differently to Spanish ones; their SRM advantage is not significant. We suggest that this is due to their language proficiency, which allows them to rely on spectral glimpsing alone, that is, exploiting spectro-temporal gaps between voices rather than spectral cues introduced by spatial separation. A second experiment introduces a second speech masker, co-located with the first; by making the task more complex, participants seem to increase their reliance on spatial cues, resulting in significant effects of masker position and HRTF. This highlights a trade-off between the use of target glimpsing and spatial cues and the need for further exploration into how task difficulty influences SRM with different HRTFs.
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Journal articleLai H, Soreq E, Bourke N, et al., 2025,
Traumatic brain injury (TBI) and mortality in older adults with and without pre-injury dementia
, Age and Ageing, ISSN: 0002-0729Background: Incidence of traumatic brain injury (TBI) is rising in older adults. Dementia is a common comorbidity and may worsen post-TBI outcomes, but its effects have not been studied. Objective: To compare all-cause mortality following TBI or non-TBI trauma (NTT) and quantify the impacts of age, deprivation, and dementia.Design: Population-based retrospective cohort studySetting: Linked primary and secondary care electronic health records (EHRs) from the Secure Anonymised Information Linkage (SAIL) DatabankSubjects: Adult residents in Wales aged 18-100 with hospitalised TBI or NTT recorded between January 2000-December 2022. Methods: Cox proportional hazard models were used to estimate survival within 1, 6, and 12 months of hospitalised TBI/NTT in those with and without pre-injury dementia diagnosis. Groups were propensity-matched by age, sex, and morbidities. Models were stratified by age, sex, and deprivation.Results: 23,428 TBIs (n=18,940) and 589,169 NTTs (n=421,259) were identified. TBIs were associated with higher mortality than NTT at all timepoints. Older age was associated with higher mortality after TBI, with 16.9% one-month mortality in patients aged 65-79, and 31% in patients aged 80-100.In TBI patients, 30-day mortality was significant irrespective of dementia. 6- and 12-month mortality were higher in those with than without pre-injury dementia diagnosis. Conclusions: TBI is associated with higher all-cause mortality than NTT, particularly in older age. Among those with TBI, patients with pre-injury dementia had particularly high chronic mortality. There is an urgent need to understand the reasons for poor outcomes in older adult TBI populations, especially those with dementia.
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Journal articleLei G, Cooper SJ, 2025,
Do Llamas understand the periodic table?
, Digital Discovery, Vol: 4, Pages: 3455-3465, ISSN: 2635-098XLarge Language Models (LLMs) demonstrate remarkable abilities in synthesizing scientific knowledge, yet their limitations, particularly with basic arithmetic, raise questions about their reliability. As materials science increasingly employs LLMs for tasks like hypothesis generation, understanding how these models encode specialized knowledge becomes crucial. Here, we investigate how the open-source Llama series of LLMs represent the periodic table of elements. We observe a 3D spiral structure in the hidden states of LLMs that aligns with the conceptual structure of the periodic table, suggesting that LLMs can reflect the geometric organization of scientific concepts learned from text. Linear probing reveals that middle layers encode continuous, overlapping attributes that enable indirect recall, while deeper layers sharpen categorical distinctions and incorporate linguistic context. These findings suggest that LLMs represent symbolic knowledge not as isolated facts, but as structured geometric manifolds that intertwine semantic information across layers. We hope this inspires further exploration into the interpretability mechanisms of LLMs within chemistry and materials science, enhancing trust of model reliability, guiding model optimization and tool design, and promoting mutual innovation between science and AI.
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Journal articleThillaithevan D, Murphy R, Hewson R, et al., 2025,
Correction to: Inverse design of periodic microstructures with targeted nonlinear mechanical behaviour
, Structural and Multidisciplinary Optimization, Vol: 68, ISSN: 1615-147X -
Journal articleHana Frade JL, Engracia Giraldi JDM, Porat T, 2025,
The influence of national origin cues in HPV vaccination advertising: an eye-tracking study of visual attention and vaccine perception using quantitative and qualitative analysis
, Human Vaccines & Immunotherapeutics, Vol: 21, ISSN: 2164-5515This study is among the first to investigate how national origin cues influence visual attention and perception in HPV vaccine advertisements, using eye-tracking technology to provide objective insights into consumer responses. By integrating methods from public health, psychology, and advertising research, this study explores how visual attention is shaped by national affiliation cues. In a controlled experimental setting with a sample of 40 UK university students, we investigated visual attention and effectiveness of HPV vaccination advertisements by comparing ads disclosing the national origin of the vaccine and without any origin information. We assessed total fixation duration and time to first fixation to various elements of the ad, along with intention and attitude measures. Contrary to one of our hypotheses, we did not find significant differences in intention (p = .758) and attitude (p = .620) measures. However, there was significant difference in total fixation duration toward one of the ad images between conditions (p = .043). The qualitative analysis reveals the role of country-of-origin (COO) in HPV vaccination advertising, suggesting a shift in attention from that image to the COO cue. Furthermore, eight out of the 20 participants in the treatment condition did not fixate at the COO cue. Findings provide critical insights for public health communication strategies, suggesting that the use (or omission) of national origin cues in vaccine advertisements could influence vaccine perception and hesitancy. These results highlight the need for strategic messaging approaches to enhance HPV vaccine acceptance and improve public trust in domestic and international vaccines.
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Journal articlePanahi AA, Luder D, Wu B, et al., 2025,
Fast and generalisable parameter-embedded neural operators for lithium-ion battery simulation
, Energy and AI, Vol: 22Digital twins of lithium-ion batteries are increasingly used to enable predictive monitoring, control, and design at system scale. Increasing their capabilities involves improving their physical fidelity while maintaining sub-millisecond computational speed. In this work, we introduce machine learning surrogates that learn physical dynamics. Specifically, we benchmark three operator-learning surrogates for the Single Particle Model (SPM): Deep Operator Networks (DeepONets), Fourier Neural Operators (FNOs) and a newly proposed parameter-embedded Fourier Neural Operator (PE-FNO), which conditions each spectral layer on particle radius and solid-phase diffusivity. We extend the comparison to classical machine-learning baselines by including U-Nets. Models are trained on simulated trajectories spanning four current families (constant, triangular, pulse-train, and Gaussian-random-field) and a full range of State-of-Charge (SOC) (0 % to 100 %). DeepONet accurately replicates constant-current behaviour but struggles with more dynamic loads. The basic FNO maintains mesh invariance and keeps concentration errors below 1 %, with voltage mean-absolute errors under 1.7 mV across all load types. Introducing parameter embedding marginally increases error but enables generalisation to varying radii and diffusivities. PE-FNO executes approximately 200 times faster than a 16-thread SPM solver. Consequently, PE-FNO's capabilities in inverse tasks are explored in a parameter estimation task with Bayesian optimisation, recovering anode and cathode diffusivities with 1.14 % and 8.4 % mean absolute percentage error, respectively, and 0.5918 percentage points higher error in comparison with classical methods. These results pave the way for neural operators to meet the accuracy, speed and parametric flexibility demands of real-time battery management, design-of-experiments and large-scale inference. PE-FNO outperforms conventional neural surrogates, offering a practical path towards high-s
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Journal articleKharman AM, Jursitzky C, Zhou Q, et al., 2025,
An adversarially robust data market for spatial, crowd-sourced data
, Distributed Ledger Technologies: Research and Practice, Vol: 4, Pages: 1-20, ISSN: 2769-6472We describe an architecture for a decentralised data market for applications in which agents are incentivised to collaborate to crowd-source their data. The architecture is designed to reward data that furthers the market’s collective goal, and distributes reward fairly to all those that contribute with their data. We show that the architecture is resilient to Sybil, wormhole and data poisoning attacks. In order to evaluate the resilience of the architecture, we characterise its breakdown points for various adversarial threat models in an automotive use case.
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Journal articleCacciarelli D, Pinson P, Panagiotopoulos F, et al., 2025,
Do we actually understand the impact of renewables on electricity prices? A causal inference approach
, IENERGY, Vol: 4, Pages: 247-258 -
Journal articleBatcup C, Almukhtar A, Menon A, et al., 2025,
Barriers and enablers to sustainable anaesthetic practice: a mixed-methods study
, British Journal of Anaesthesia, ISSN: 0007-0912Background: Anaesthetic practices contribute significantly to the environmental impact of healthcare. Using local or regional anaesthesia instead of general anaesthesia, and TIVA instead ofinhalational anaesthesia, can reduce this impact. This study investigated why general anaesthesia is sometimes used over local and regional anaesthesia, and why inhalational agents are often chosen over TIVA.Methods: We conducted a mixed-methods study in the UK (June 2023–April 2024), underpinned by the Theoretical Domains Framework. Semi-structured interviews (n=19) with anaesthetists, surgeons and nurses of differing seniority were analysed using Framework Analysis. A national survey (n=347), distributed via posters and professional networks, was developed from early interview findings. Quantitative data were analysed descriptively and open-text responses were coded using the qualitative framework.Results: Four key themes were identified: (1) contextual factors affecting anaesthesia decision making; (2) patient differences and preferences; (3) influence of key decision makers on anaesthesia choice; and (4) default practices and lack of confidence in alternatives. These encompassed 17subthemes and mapped to 9 of 14 Theoretical Domains Framework domains.Conclusions: This study provides new insights into behavioural influences underlying anaesthetic practice, which can inform the design of interventions to improve the sustainability of anaesthesia, without compromising patient safety and comfort. Addressing systemic and behavioural barriers through dedicated local anaesthesia operating lists, improved patient communication, targeted training and supportive technologies may enhance efficiency while promoting safe, sustainable, patient-centred practice. Future interventions should be co-designed with surgeons, anaesthetists and patients to ensure clinical acceptability, feasibility, and sustainability
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Journal articleAlmukhtar A, Batcup C, Jagannath S, et al., 2025,
Understanding sustainability in operating theatres: an ethnographic study to determine drivers of unsustainable behaviours
, Annals of Surgery Open, ISSN: 2691-3593BackgroundClimate change is the biggest threat to human health. Paradoxically, the healthcare sector is a major contributor to climate change, and operating theatres are among the highest sources of emissions. Unsustainable practices are actions that compromise environmental, social, and financial sustainability, leading to unnecessary resource use, avoidable harm to the wider population, and reduced ability to provide effective healthcare in the future. Drivers of unsustainable practices and barriers to sustainability in practice (a top priority identified by the James Lind Alliance Priority Setting Partnership) are unexplored, hindering interventions which can help meet net-zero targets within healthcare. We conducted the first known ethnographic study to investigate behaviours related to sustainability in operating theatres, and their influences on those behaviours to inform the design of effective behaviour change interventions.MethodsNon-participant ethnographic observations with opportunistic discussions in elective general surgical operating theatres were conducted between June and December 2023 at two university hospitals in Central London. Data were collected until saturation using a template developed during the initial observations. Inductive thematic analysis was conducted, with sub-themes (influences) deductively mapped to the Theoretical Domains Framework (TDF).ResultsTwenty-six procedures were observed (42 hours). Unsustainable behaviours included: (i) unnecessary and inappropriate glove use, potentially compromising safety (average 8-10 pairs per operation), (ii) incorrect waste disposal, (iii) unnecessary package opening, and (iv) energy waste. Thematic analysis generated 6 themes and 16 influences (mapped to 9 TDF domains). Key themes were that sustainable practices are “infrequent and inconsistent” due to limited awareness (Knowledge) and low environmental concerns (Memory, Attention and Decision Processes). Unsustainable behaviours we
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Journal articleLiu H, Luo W, Xu M, et al., 2025,
Emerging bioelectronics and optogenetics for neurogastroenterology
, BIOSENSORS & BIOELECTRONICS, Vol: 288, ISSN: 0956-5663 -
Conference paperSun S, Cao J, Zhang Z, et al., 2025,
Culturally Adapted Design of a Digital Mental Health Intervention to the Chinese context: A design case study
, Pages: 120-130Culture plays a crucial role in the design of mental health interventions since it influences how people seek assistance, participate in healthy behaviours, and how services are provided. It is believed that including cultural factors in the intervention will improve its relevance, acceptability, effectiveness, and sustainability. In this paper, we present the design process of a case study of adapting an evidence-proven Australian digital mental health intervention to the Chinese context, following culturally sensitive design frameworks including the ADAPT Model and the Ecological Validity Model. Through cultural adaptation, the localised intervention, namely 云舒 (CloudEase), received a higher overall satisfaction score on the System Usability Scale compared to the literal translated version of the original intervention. Documenting each step of this process demonstrates a practical roadmap and guidelines for customising similar digital mental health interventions in Chinese or other cultural settings.
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Conference paperCho M, Re MD, Simpson T, et al., 2025,
Conversational AI in Community Care: Preliminary Insights from a Scoping Review
, Pages: 471-480Community-based care represents a strategic priority for healthcare systems globally, yet the integration of conversational artificial intelligence (CAI) in these settings remains underexplored. This ongoing scoping review investigates current applications of CAI in community care settings to identify and categorise functional capabilities that can guide future implementation decisions. Through systematic database searches, we identified 65 papers for detailed analysis. Our initial observation surfaced eight CAI capabilities: identify, detect, generate, create, record, send, adapt, and operate. ‘Generation’ was frequently observed to produce personalised responses, data summaries or care recommendations. ‘Adaptation’ appeared particularly relevant in community care, facilitating linguistically and culturally responsive care. Emergent insights include the role of CAI in supporting relational care, enhancing cultural and contextual sensitivity, enabling collaboration with human agents, and processing multimodal data inputs for diverse care settings. This capability-centred analysis will provide an evidence-based foundation for innovators and clinical teams to make informed decisions about CAI integration in community care environments, with implications for scaling accessible and culturally appropriate care delivery.
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Journal articleHamdan S, Pinson P, Treluyer J-M, et al., 2025,
Trends of Opioids and Nefopam Use Among French Children: A Population-Based Study From 2009 to 2023
, CLINICAL DRUG INVESTIGATION, ISSN: 1173-2563 -
Conference paperZHOU J, Aloufi R, Porat T, et al., 2025,
Human-AI collaboration in high-stakes decision-making: work in progress
, 38th International BCS Human-Computer Interaction Conference (BCS HCI 25), Publisher: BCS Learning and Development Ltd, Pages: 465-470This work in progress investigates human interaction with an LLM-powered chatbot, presented as either a fellow human or a transparently disclosed AI collaborator, in a high-stakes decision-making simulation—the NASA Moon Survival Task. We will employ a one-way between-subjects design to examine how individuals’ collaboration and communication are influenced by the identity of their partner (AI vs. human). Specifically, we will evaluate individuals’ collaboration processes (i.e., collaborative behaviour and communicative dynamics) and outcomes, alongside their retrospective interaction experience andperceptions of the partner. We will also examine dyadic-level linguistic coordination during the interaction and conduct user profiling to uncover variations in AI collaborative benefits. We anticipate that this studywill have four key impacts: safeguarding human-AI collaboration, democratising AI benefits, guiding modelimprovement, and making methodological contributions. The anonymised dialogues and associated data will be open-sourced upon study completion.
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Journal articleQi N, Pinson P, Almassalkhi MR, et al., 2025,
Capacity credit evaluation of generalized energy storage considering strategic capacity withholding and decision-dependent uncertainty
, APPLIED ENERGY, Vol: 397, ISSN: 0306-2619 -
Journal articleCardenas MK, Anza-Ramirez C, Bernabe-Ortiz A, et al., 2025,
Validation and cost-effectiveness of an mHealth tool for cognitive impairment detection in Peru: protocol for the IMPACT Salud observational study
, BMJ Open, Vol: 15, ISSN: 2044-6055Introduction Dementia is a chronic and progressive neurological condition characterised by cognitive and functional impairment. It is often associated with multimorbidity and imposes a significant economic burden on healthcare systems and families, especially in low-income and middle-income countries. In Peru, where dementia cases are increasing rapidly, timely detection and referral for diagnosis is crucial. This protocol is part of the IMPACT Salud project in Peru. Here, we focus on a specific component aimed at validating an mHealth tool for the detection of cognitive and functional impairment and assessing its cost-effectiveness. We will also assess changes in cognitive and functional impairment as well as health economic outcomes over 1 year.Methods and analysis This observational study will be conducted in four geographically diverse regions of Peru. Community health workers are expected to contact approximately 32 000 participants (≥60 years) to apply an mHealth-enabled tool that includes cognitive and functional instruments: Ascertain Dementia 8, Peruvian version of Rowland Universal Dementia Assessment Scale and Pfeffer Functional Activities Questionnaire. From this large sample, we aim to find 3600 participants and their study partners to enrol and interview at baseline regarding sociodemographic characteristics, lifestyles, comorbidities and health economic data including resource use, costs and health-related quality of life (HR-QoL). Psychologists, blind to previous results, will assess dementia stage of the participants using an abbreviated Clinical Dementia Rating (CDR) scale. At 6-month follow-up, participants will complete a brief health economics questionnaire on resource use, costs and HR-QoL. To validate the accuracy of the detection tool, a subsample of 600 participants who completed the baseline will undergo a gold-standard clinical neuropsychological assessment. This subsample will participate in a 12-month follow-up, including healt
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Journal articleKoczias C, Demirel P, 2025,
The impact of green skills and green capabilities on firms’ financial performance: a systematic literature review
, Journal of Innovation & Knowledge, Vol: 10, ISSN: 2444-569XGreen skills and capabilities in organisations are important enablers of net-zero transition as they determine firms’ ability to adopt sustainability as a core business strategy. This study conducts a systematic literature review of 84 scholarly articles and 7 industry publications to examine the conceptualisation of green skills and green capabilities in firms, their influence on firms’ financial performance, and the factors shaping these complex relationships. Findings reveal that the green skills and green capabilities concepts are broad and ambiguously defined, necessitating greater conceptual clarity, especially with respect to how the green skills and capabilities relate to each other. Accordingly, the study develops a framework outlining different green skill and capability typologies and their interconnections. Additionally, six key performance categories—(1) costs, (2) profitability, (3) efficiency and productivity, (4) firm growth, (5) liquidity, and (6) market performance— are identified inductively, with evidence from the literature indicating that both green skills and green capabilities positively impact each of these areas. Results suggest that while the overall effect of green skills and capabilities on financial performance is positive, several contextual factors, such as firm size, sector, and region, influence this relationship. The study highlights the most significant caveats and their implications, contributing to a more nuanced understanding of the knowledge–performance link for the net-zero transition.
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Journal articleBoufelja Y S, Quinn A, Shorten R, 2025,
Randomized transport plans via hierarchical fully probabilistic design
, Information Sciences, Vol: 718, ISSN: 0020-0255An optimal randomized strategy for design of balanced, normalized mass transport plans is developed. It replaces—but specializes to—the deterministic, regularized optimal transport (OT) strategy, which yields only a certainty-equivalent plan. The incompletely specified—and therefore uncertain—transport plan is acknowledged to be a random process. Therefore, hierarchical fully probabilistic design (HFPD) is adopted, yielding an optimal hyperprior supported on the set of possible transport plans, and consistent with prior mean constraints on the marginals of the uncertain plan. This Bayesian resetting of the design problem for transport plans—which we call HFPD-OT—confers new opportunities. These include (i) a strategy for the generation of a random sample of joint transport plans; (ii) randomized marginal contracts for individual source-target pairs; and (iii) consistent measures of uncertainty in the plan and its contracts. An application in fair market matching is outlined, in which HFPD-OT enables the recruitment of a more diverse subset of contracts—than is possible in classical OT—into the delivery of an expected plan.
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Journal articleIkeya K, Cardin M-A, Cilliers J, et al., 2025,
Multi-objective decision-making for flexible design and planning of oxygen production facilities in an uncertain lunar environment
, ACTA ASTRONAUTICA, Vol: 236, ISSN: 0094-5765 -
Journal articleJin B, Kobayashi A, Bhattacharya D, et al., 2025,
Automated Straight-Line Sewing of Stretchable Fabrics With Different Lengths
, IEEE ROBOTICS AND AUTOMATION LETTERS, Vol: 10, Pages: 12165-12172, ISSN: 2377-3766 -
Journal articleLu X, Owen RE, Du W, et al., 2025,
Unravelling electro-chemo-mechanical processes in graphite/silicon composites for designing nanoporous and microstructured battery electrodes
, NATURE NANOTECHNOLOGY, ISSN: 1748-3387 -
Journal articleZHOU J, Van Zalk N, 2025,
A person-oriented approach to social anxiety and depression: latent profiles and emotional functioning in adults
, Frontiers in Psychology, Vol: 16, ISSN: 1664-1078Objectives: Symptoms of social anxiety and depression often co-occur, but many questions remain about symptom-level co-occurrence and the heterogeneity of symptom presentations across individuals, as well as their emotional functioning. This study aimed to investigate the co-occurrence of social anxiety and depressive symptoms in adults and variations in emotional functioning linking symptom heterogeneity. Methods: This study used a person-oriented approach, Latent Profile Analysis (LPA), to identify distinct profiles (i.e., subgroups) in a UK adult sample (N = 222) varying in presentations of social anxiety and depressive symptoms. Further analyses examined between-profile differences in emotional functioning, including daily affect and emotion regulation. Results: Four profiles were identified: Comorbid (12.61%), Dysphoric (10.36%), Socially Anxious (36.94%), and Low Distress (40.09%), replicating the four-profile solution revealed in prior research on adolescents. The Comorbid subgroup reported the most pronounced emotional dysfunction, with higher daily negative affect, lower positive affect, and greater emotion dysregulation than the other three subgroups. The Low Distress subgroup reported the best emotional functioning. Conclusions: The cross-sectional study design restricts our ability to evaluate the long-term stability of the identified profiles. Nevertheless, this study illuminates the diverse ways social anxiety and depression intertwine, underscoring the necessity of transdiagnostic interventions that cater to a wide range of symptom patterns and emotional functioning.
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