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DensEst: an automated empirical potential-based means of determining the densities of disordered materials from total scattering data
Full text linkWe investigate the fundamental limits of using total-scattering measurements to simultaneously determine the atomic number density (ρ) and pair distribution function (g(r)) of disordered materials. Building on rigorous Fourier-transform relationships between the structure factor S(Q) and g(r), we first show analytically that even infinitely precise, noise-free S(Q) data-spanning an unbounded Q-range-cannot uniquely specify both ρ and g(r). This non-uniqueness arises from phase information loss, finite-dimensional projections inherent in one-dimensional pair distributions, and the mathematical insensitivity of S(Q) to coordinated rescaling of density and radial distances. In addition, we highlight practical problems arising from mathematical methods aimed at extracting ρ via Fourier transform of data. Direct calculation from integrating g(r)−1 (Yarnell method) converges badly for high density because of long-range structure in g(r), and at low density because of a bias coming from the central atom in g(r). Indirect calculation from the slope of f⋅[g(r)−1] (Eggert method) depends sensitively on having good quality high-Q data. To address these ambiguities, we introduce a density-sweep protocol using the empirical potential structure refinement (EPSR) within the ab initio augmented structure solving engine framework. By systematically varying trial densities around target values ( ±5%–50%) and evaluating both the internal EPSR R-factor and an external R-factor based on final F(Q), one can identify a clear minimum bracketing the true ρ without reliance on external equations of state or arbitrary fitting ranges. We showcase the effectiveness of the method by application to supercritical krypton at multiple pressures, liquid D2O at 298 K and amorphous silica and reliably recover known densities within ±5%
Designing an mHealth App to Encourage Uptake of Muscle Strengthening Exercise in Older Adults: A Co-Design Focus Group Study (Preprint)
Full text linkBackground: Sarcopenia, the age-related decline in muscle mass and strength, poses a significant threat to functional independence in older adults. Despite strong evidence supporting resistance training as a preventive and therapeutic strategy, adherence to muscle-strengthening guidelines remains low. Mobile health (mHealth) technologies offer a promising avenue to bridge this gap; however, few apps are tailored to older adults or designed with their input. Objective: This study aimed to identify key features that a muscle-strengthening exercise app should include to enhance engagement and uptake among older adults. Secondary aims were to explore perceived barriers and facilitators to app use and to inform the development of an evidence-based, co-designed mHealth intervention. Methods: We used a qualitative co-design approach, involving 4 focus groups with 18 older adults (aged 60-83 years); each group comprised 3 to 6 older adults, stratified by experience with mHealth apps. Sessions were conducted online via Microsoft Teams and guided by a semistructured protocol informed by prior mHealth research and behavior change theory. Transcripts were analyzed using deductive thematic analysis, underpinned by the Technology Acceptance Model, focusing on perceived usefulness and perceived ease of use. Results: A total of 4 overarching themes and 10 subthemes were identified. Theme 1, mHealth as a tool for supporting health and well-being, highlighted participants’recognition of digital tools in promoting activity and overcoming accessibility barriers. Theme 2, motivation and engagement through app features, revealed the importance of reminders, progress tracking, and feedback, although views on gamification were mixed. Theme 3, drawbacks of current mobile apps, captured concerns around complexity, poor usability, and lack of age-appropriate content, with skepticism regarding safety and evidence base. Theme 4, desired app elements and features, emphasized the need for customizable reminders, clear instructional videos, adaptable exercise options, and optional social features. Participants stressed the importance of simplicity, personalization, and relatable content to foster trust and sustained engagement. Conclusions: Older adults are receptive to mHealth interventions for muscle-strengthening when design is user centered and grounded in their lived experiences. This study provides a framework for future app development, highlighting the need for intuitive interfaces, personalized features, and credible educational content. By aligning design with Technology Acceptance Model constructs and co-design principles, mHealth apps can better support healthy aging and sarcopenia prevention. These findings offer actionable guidance for developers and researchers aiming to enhance digital health equity and effectiveness in older populations
Self‐Catalyzed AlGaAs Nanowires and AlGaAs/GaAs Axial Heterostructures Grown by Molecular Beam Epitaxy
Full text linkSelf-catalyzed AlGaAs nanowires (NWs) offer advantageous properties, including lattice matching to GaAs, a wide range of electronic bandgaps, and monolithic integration with the mature Si platform due to elastic strain relaxation. However, the growth of self-catalyzed AlGaAs NWs is typically characterized by morphological challenges, such as branching and tapering. Here, we comprehensively investigate the optimization of the group III growth rate and V/III ratio. We demonstrate the growth of AlGaAs NWs using a Ga/Al alloy droplet as a co-catalyst, achieving minimal branching and NW uniformity with up to 40% nominal Al content. Embedding a single GaAs segment in an optimized NW structure results in QD-like properties, including strong spatially localized emission at room temperature. Our findings demonstrate the control of branching events in self-catalyzed AlGaAs NWs, highlighting their potential for applications including nanolasers and quantum light emitters
Nursing Handover Talk: An Ethnographic Study
Full text linkBackground: One of the most important forms of communication between nurses, whichremains a formal process within the nursing workflow, is the nursing handover. As acommunicative event, the handover constitutes a complex process that requires clinicalknowledge, as well as social, professional, and organisational skills. Althoughformalisation and standardisation are widely encouraged, the nuanced meanings, purposes,and lived experiences of oral handover practices remain underexplored. This study aimedto explore verbal nursing handovers between staff in an Acute Medical Unit (AMU) touncover how nurses talk, structure, and make sense of handovers in real-time interactions.Methods: This study employed an ethnographic design combining participant observation,field notes, and informal discussion alongside conversation analysis and dramaturgicaltheory. Data were collected within a UK NHS hospital’s AMU over twelve weeks,capturing naturally occurring handover interactions among experienced nurses.Findings: Analysis revealed the interactional structures and social functions of nursinghandovers, showing how nurses adopt various roles and construct meaning through talk.Handovers served multiple purposes beyond information exchange, including professionalidentity work, boundary negotiation, emotional labour, and coordination. Ritualisedelements of the interaction supported continuity, while shared narratives aided prioritisationand team alignment.Value: The findings shed light to the under-recognised complexities of nurse-to-nursehandovers and their role in maintaining clinical and cultural coherence. In an era ofincreasing digitalisation of hospital systems, this study highlights the enduring value of oralcommunication, providing new insights for designing future handover policies and digitaldocumentation tools.Keywords: nursing handover, ethnography, conversation analysis, dramaturgy,communication, acute medical uni
Attribution Processes in Media Coverage of Minoritised Group Members
Full text linkFollowing England’s loss in the UEFA 2020 European Football Championship final, three players of colour faced widespread racist abuse for missing their respective penalties. The abuse these players received coincided with a surge in racially and religiously aggravated offences in the UK. Using intergroup relations and social psychology, this thesis examines how the portrayal of interracial dynamics within the context of sports media coverage may influence societal attitudes. To explore these dynamics, this thesis develops a theoretical framework linking cognition, metaphors and linguistic intergroup bias, and behaviour, with implicit and explicit bias, showing how motivated group preferences emerge through nouns and descriptors. Three empirical studies are presented: the first uses vignette experiments to assess how word order in social category labels affects identity inferences and implicit bias; the second and third apply quantitative thematic analysis to YouTube videos and audience comments, revealing how interloper journalists and their viewers employ racial and fandom-based categorisation to express and reinforce bias. Findings highlight that interloper journalists tend to prioritise fan identity over racial identity, while commenters display explicit racial bias. The thesis concludes that although fan affiliation often outweighs racial identity in media narratives, racial identity remains a potent and divisive factor in public discourse. Additionally, a key implication is that focusing on superordinate labels to categorise social groups can encourage a greater level of social cohesion
COLLAB-LLM: A Communication-Centric Role-Based Framework for Scalable Multi-Agent LLM Collaboration
Full text linkLarge Language Models (LLMs) are increasingly deployed in multi-agent systems; however, existing frameworks continue to suffer from communication ambiguity, coordination failures, and poor scalability as task complexity increases. This paper introduces COLLAB-LLM, a communication-centric, role-based framework designed to enable reliable and scalable collaboration among LLM agents. The framework combines a structured communication protocol, a hierarchical role architecture, and a dynamic distributed task-graph engine to support coordinated planning, efficient negotiation, and adaptive task execution. COLLAB-LLM is evaluated on over 120 complex, multi-step tasks spanning software engineering, business process automation, and scientific research synthesis. Task success is defined using task-specific completion criteria, with a task considered successful when the aggregate completion score exceeds 0.8. Under identical underlying LLM configurations, COLLAB-LLM achieves an 89% overall success rate, representing a 13–19% improvement over strong single-agent and multi-agent state-of-the-art baselines, with statistically significant gains in performance, communication efficiency, and robustness. Experimental results demonstrate that structured communication and role specialization substantially reduce ambiguity, improve collaboration quality, and enable scalable coordination for teams of up to eight agents. This work establishes foundational design principles for high-performing collaborative AI systems and provides a practical, reproducible pathway toward scalable, human-aligned multi-agent LLM architectures. All experimental artifacts, task definitions, prompts, and evaluation scripts will be released to support reproducibility
Multi-Classifier-Weighted Adversarial Network-Based Open-Set Fault Diagnosis for Multi-mode Chemical Processes
Full text linkOwing to the complexity and uncertainty of industrial production, unknown faults inevitably occur. However, most existing methods struggle to effectively identify unknown fault categories in a new operating mode, and often either treat known and unknown categories equally or exhibit a bias toward known classes. To tackle these issues, this paper proposes a Multi-Classifier-Weighted Adversarial Network for Open-set fault diagnosis in industrial processes. During the adversarial training between the feature extractor and the extended classifier, ambiguous samples exhibit a higher probability of being biased toward the unknown category. Furthermore, an auxiliary classifier is designed to compute the membership of samples to the unknown class, thereby enabling adaptive and dynamic adjustment of the bias intensity toward the unknown category during adversarial training. Experiments on two chemical fault datasets verify that the proposed model can simultaneously identify known and unknown faults, outperforming other diagnostic models
Manchester stands united: Place‐based identity facilitates resilience in the aftermath of a massemergency
Full text linkUnderstanding community resilience to disasters is fundamentally important in a world characterized by increasing political and environmental instability. The Social Identity Model of Collective Resilience has examined how the shared identity that emerges among neighbourhood residents affected by disasters can facilitate and coordinate effective collective responses, but has yet to examine impacts on community members beyond those directly affected. This is particularly important given the role of social identities in creating shared vulnerability and resilience to collective trauma among those indirectly affected, as well as evidence that neighbourhood identification can provide residents with collective resilience to a range of shared socio‐economic and environmental stressors. The present study addresses this gap through an exploration of residents' accounts of the occurrence and aftermath of a terrorist attack on Manchester, England in 2017. The thematic analysis of retrospective interviews with 18 city residents indirectly affected by the bomb revealed that two key aspects of Mancunian identity – diversity and endurance of the city – were used to interpret the event and reported to facilitate coordinated coping and collective recovery. The implications are that identifying and enhancing local norms of cohesion and endurance can play a part in providing communities with resilience to future disasters
Spotlight: Media, Science, and Technology SIG
Full text linkAt the 2025 Society for Cinema and Media Studies (SCMS) conference, prior chairs of the Media, Science, and Technology (MST) scholarly interest group (SIG) held a roundtable to reflect upon the past and future of the group. Conceived by Andrew Lison, Kyle Stine, Elizabeth Ellcessor, and Juan Llamas-Rodriguez, the roundtable posed a series of necessary and uncomfortable questions. What is the state of the MST SIG within our fast-evolving media landscape? What are our commitments to terms such as science and technology? And how do we imagine ourselves in relation to the broader parent disciplines of film and media studies? This Spotlight aims to continue, and expand upon, this initial conversation, examining how the MST SIG could be more effectively situated—academically, theoretically, and politically—moving forward
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