26207 research outputs found
Sort by
The self-management support needs of people diagnosed with psoriatic arthritis: A realist review protocol
Get PDFIntroduction: Psoriatic arthritis (PsA) is a form of inflammatory arthritis linked to psoriasis. Previous research from the UK has found that many people feel unsupported when diagnosed with PsA and lack confidence in managing their condition. This realist review aims to understand what works and does not work for whom and in what circumstances, in relation to healthcare professionals engaging with people to support them in developing self-management skills. Methods and analysis: This protocol was developed by defining the scope of the review, using a brief directed literature review to support discussion by an expert group of researchers, healthcare professionals and a patient partner. A theoretical domains framework was generated, consisting of nine initial programme theories. These were further refined with input from Patient and Public Involvement and Engagement groups and used to develop a database search strategy. A systematic search of MEDLINE, CINAHL, Embase, Emcare and APA PsycINFO will be carried out, supplemented by citation tracking, exploration of grey literature and a mixed methods survey of rheumatology health professionals. Data selection will be performed by a minimum of two reviewers and data from included sources will be extracted using a template. Data will be synthesised narratively with respect to the identified initial programme theories, using these data to refine or refute these theories. This will generate refined programme theories to explain what works for whom and in what circumstances. Ethics and dissemination: Ethical approval for the health professionals survey was granted through the Research Ethics Committee, University of the West of England (Project ID: 10991848). Outputs will be disseminated to the research community through conference presentations and a peer-reviewed journal article. The strategy for sharing outputs with patients and health professionals will be discussed and agreed with knowledge user groups
Optimised waveband selection for low-cost multispectral estimation of tomato lycopene concentration using machine learning
No full textThis paper presents an investigation into a cost-effective method for non-destructive lycopene quantification in tomatoes using multispectral imaging, while aiming for high precision and practical applicability across various phases of tomato harvesting, processing, and storage. Lycopene, a carotenoid with antioxidant properties, is known for its health benefits, with its consumption being linked with reduced risk of cardiovascular disease, cancer, and neurodegenerative disorders. Tomatoes are the primary dietary source of lycopene due to their high concentration levels and widespread consumption. This study adopts a multispectral imaging approach, strategically selecting wavebands to enhance sensitivity and accuracy beyond conventional RGB systems. It does this while limiting the number of wavebands to the minimum required to reduce hardware complexity and operational costs. A primary contribution of this work lies in the streamlined approach to waveband selection in optimised capture conditions, which iteratively adds wavebands and evaluates their individual contributions to the model's performance using the coefficient of determination of predictors (R²). The method is validated through repeated cross-validation. The study evaluates four machine learning methods—SVR (R² = 0.940), k-NN (0.920), CNN (0.932), and SNN (0.959), to assess their performance on low-cost hardware. Notably, a simplified two-waveband configuration using a fast SNN achieved an R² of 0.951 and RMSEP of 6.317mg/kg, offering substantial reductions in hardware cost and processing time while maintaining high predictive accuracy, making it a promising and inexpensive solution
Integrating British Sign Language into deaf education
No full textThis article argues for the integration of British Sign Language (BSL) into the core of deaf education in England. Despite progress in early identification and intervention, deaf pupils continue to experience educational disadvantage, with persistent attainment gaps compared to their hearing peers. Current policy remains rooted in a medicalised model of deafness, with BSL marginalised in favour of speech and audiological interventions. The authors identify five key challenges: the dominance of disability frameworks over language rights; the medicalisation of deaf education; the exclusion of BSL from the National Curriculum; the impact of socio-economic inequality; and weak assessment arrangements. The paper explores how meaningful integration of BSL would look, including the adoption of the BSL Curriculum, the rollout of the BSL GCSE, workforce development for BSL teachers, and reform of assessment practices. Drawing on policy reviews, attainment data, and practitioner insights, the authors show how a bilingual model, placing BSL on an equal footing with English, can support improved outcomes for deaf pupils and enrich learning for hearing peers
Learning to care in the food system: Education for sustainable development resources, food and farming
No full textDespite calls for curricula to be repurposed around environmental concerns, and the related significance of food-related emissions to global climate change, consideration of the wider impacts of the global food system (positive and negative) are frequently not well-integrated into education of 5–11-year-olds. This paper makes an important contribution to nascent research around the nature and role of learning resources for education for sustainable development, providing the first review of the place of animals in learning resources for food education. The 117 resources we drew on focused on those that were freely-available, directed at ages 5-11, and available to support those implementing the Curriculum for Wales. We reflect on the implications of these findings for the design of future learning resources, focusing specifically on how they could incorporate ideas from literature on more-than-human ethics of care and, through this, how they might prompt not only critical reflection but meaningful actions and engagement amongst learners
Large language models for sustainable water management: A review
Get PDFSustainable Water Management (SWM) balances environmental protection, social equity, economic efficiency, and governance transparency. Achieving this balance requires timely data, specialised expertise, and adaptive decision-making, yet current approaches often face challenges of fragmented information, limited expert capacity, and costly decision-support tools. Large language models (LLMs) offer a promising alternative as general-purpose artificial intelligence (AI) systems capable of summarising documents, generating analytical code, connecting to databases, and communicating insights in natural language (NL). By reusing a single pretrained model across multiple applications, LLMs can lower analytical costs, expand access to expertise, and enhance transparency through grounded and auditable outputs. This study presents one of the earliest systematic reviews of LLMs in SWM, synthesising evidence from 34 studies. The review analyses publication trends, thematic and conceptual landscapes, and global collaboration patterns, revealing a rapidly expanding yet methodologically fragmented field. Evidence grading and risk-of-bias assessments show that most studies remain conceptual or observational, emphasising the need for greater empirical rigor and reproducibility. Across environmental, social, economic, and governance (ESEG) dimensions, LLMs demonstrate potential to improve data integration, operational efficiency, and decision transparency, while challenges persist in bias propagation, data dependency, and model interpretability. Emerging design patterns, such as retrieval-augmented generation (RAG), human-in-the-loop frameworks, and explainable AI, are advancing safer and more accountable deployment. By consolidating fragmented literature, this review provides a foundation for responsible and sustainable AI in water management
Special section: Introducing deaf legal studies
Get PDFThis editorial introduces Deaf Legal Studies (DLS) as an emerging field that examines how law constructs, regulates, and often misrecognises deaf people. While legal systems have historically viewed deafness narrowly through a disability lens, DLS centres deaf epistemologies, sign languages, and lived experience to reveal how hearing‑centred assumptions shape participation, authority, and justice. The Special Section situates this work within current global and UK developments, including growing sign language legislation and shifts in deaf education. It traces key themes in over 140 years of deaf legal research—from legal status and procedural justice to interpreting, everyday encounters with law, sign language recognition, and comparative human rights analysis—highlighting the field’s increasing empirical and interdisciplinary character. The four contributions to the Special Section illustrate how formal legal recognition often fails without effective implementation and institutional change, and how law continues to govern deaf people’s lives through systems, practices, and access structures. The editorial positions DLS as a developmental, collaborative project and invites further scholarly engagement, supported by the newly established Deaf Legal Studies Association
Functional foods in health promotion and disease prevention: Innovations, evidence and challenges
Get PDFFunctional foods have attracted increasing scientific and commercial interest due to their potential roles in health promotion and the prevention of non-communicable diseases such as diabetes and cardiovascular diseases. In this review, we will critically examine the current evidence on functional foods by focusing on their classification, bioactive components, biological mechanisms, consumer acceptance and regulatory frameworks. Bioactive compounds, such as polyphenols, dietary fibre and probiotics, from both plant- and animal-origin functional foods, have also been examined in this review. Despite substantial experimental and epidemiological evidence, the translation of functional foods into consistent health benefits remains challenged by variability in bioavailability, food matrix effects, processing conditions and interindividual differences in genetics and gut microbiota. Key mechanistic determinants of bioefficacy, including intestinal transport processes, molecular structure, stereochemistry, and food–drug interactions, are discussed. Consumers’ perception and purchasing behaviour are examined, identifying the influence of product format, socio-demographic characteristics, information sources, health motivation and price sensitivity. Our review also compares the regulatory approaches in the United States, European Union, Japan and China, highlighting the heterogeneity in definitions and health claim substantiation requirements. Finally, emerging opportunities such as metabolic profiling technologies and personalised nutrition are highlighted as future directions to support evidence-based, effective and equitable functional food development
Deep learning architectures for software fault prediction: The impact of error-type metrics and class imbalance
No full textSoftware Fault Prediction (SFP) plays a crucial role in modern software development by enabling early identification of fault‐prone modules and efficient allocation of testing resources. While deep learning approaches have shown promise in this domain, challenges persist regarding architectural choices, metric selection, and class imbalance issues. This study presents a comprehensive comparison between Deep Neural Networks (DNNs) and hybrid Graph Neural Network‐Long Short‐Term Memory (GNN+LSTM) models for SFP, investigating their effectiveness when combined with both conventional software metrics and Error‐type Metrics. We evaluate these approaches on four real‐world Java projects: ANTLR v4, JUnit, OrientDB, and Elastic Search. Our results demonstrate that GNN+LSTM models consistently outperform traditional DNN approaches, achieving improvements of up to 4% in accuracy and 4% in F1‐score. However, we identify challenges in combining different metric sets, with performance actually degrading compared to our previous study using Error‐type Metrics alone, suggesting potential multi‐collinearity issues. Additionally, we examine the effectiveness of the Synthetic Minority Oversampling Technique (SMOTE) in addressing the class imbalance issue, observing improvements of up to 6.6% in accuracy for GNN+LSTM models in severely imbalanced datasets. Our findings provide practical insights for selecting appropriate model architectures and metric combinations in SFP while highlighting the importance of carefully considering feature interactions and class imbalance mitigation strategies
Hardware security modules for secure communications in the industrial Internet of Things
Get PDFThe Industrial Internet of Things (IIoT) offers transformative potential but introduces critical security risks, including unauthorized access, data breaches, and privacy compromise. Hardware Security Modules (HSMs) have emerged as robust solutions to protect IIoT ecosystems by enabling secure cryptographic operations, providing tamperresistant hardware and creating trusted execution environments. This work presents the first comprehensive review of HSMs tailored for secure IIoT communications, addressing their architectural foundations, operational mechanisms, and deployment scenarios. It first outlines the IIoT security landscape and HSM deployment architectures, including cloud-based, edge-integrated, and distributed models. Next, cutting-edge HSM implementations are analyzed, emphasizing their effectiveness in authentication, secure communication protocols, and physical tamper resistance. It then explores attack surfaces and vulnerabilities, such as firmware exploits, logical flaws, and network-based threats, along with mitigation strategies. Case studies from smart manufacturing, energy grids, and logistics demonstrate practical HSM applications, while a comparative evaluation assesses commercial and open-source solutions based on performance, compliance, and scalability. Emerging trends such as AI-driven threat detection, post-quantum cryptography, and decentralized HSMs are also discussed. Finally, key challenges are highlighted, including latency in real-time systems, supply chain risks, and regulatory hurdles, and future directions for research and industry adoption are proposed. This work serves as a roadmap for securing IIoT deployments, offering actionable insights for researchers, practitioners, and policymakers
Joie de vivre
No full textSarah Bodman talks with Sarah Boris about her love of joyful colours and how she uses screenprint to express solidarity and hope