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Ethical subjectivity and ontologies of English: implications for social justice in English language education
Get PDFWe are two English language teachers, mother (Rachel) and daughter (Clara); one born in the UK and currently teaching about English language teaching at a university in the UK, and one born in Indonesia and currently teaching English in Japan. In this chapter, we focus on the hiring, teaching and testing practices that constitute what ‘English’ is. We explore our own roles in these practices, and how these practices work by excluding options for individuals and groups, thereby limiting their freedom to be what/who they might like to be. We show how we have tried to take responsibility for challenging these exclusionary effects.
Our aim in writing this chapter is to demonstrate that thinking about ‘English’ is a necessary first step in deciding how we want to teach English in ways, and with outcomes, that we consider to be ethical, that is, socially just. We have tried to write the chapter in a way that makes it obvious how we have arrived at where we are; through being born in a particular place, educated in a particular way, and having had the teaching experiences that we have had so far
The Mobilities of Deep Time: An Anthro-apology from The Long Dead Stars
Get PDFThis paper discusses the artistic practice of electronic dance poets The Long Dead Stars in relation to walking-arts (human mobility) and the movement of rock (non-human mobility), contextualising an environmental agenda through a walking-arts inspired aesthetic that playfully but seriously attunes with earth materials. Exploring the significance of aesthetics, we ask “how might an artistic collaboration with rocks, with Earth, enable a non-othering, where rock and human are equal?” Through practices such as channelling, deep listening, ludicerious aesthetics, scanning and dithering, we consider how aesthetics can contribute to a successful human-rock partnership. As is right when trying to repair a broken relationship, in this case between the human and non-human, we start with an apology, an Anthro-apology, before exploring how aesthetic practice might move the relationship forward
Enhancing image compression through a novel Structural Fidelity Weighted Ensemble (SFWE) model
Get PDFWith the explosion of digital images across multiple sectors like social media, health care, medical imaging, and remote sensing, there is a demand to optimise the storage and transmission of images. In this paper, a novel Structural Fidelity Weighted Ensemble model is proposed to dynamically adjust the weights between SVD and PCA outputs to enhance the quality of reconstructed images.
Unlike traditional static fusion techniques, the proposed SFWE deploys a fast bounded scalar optimization strategy so as to dynamically estimate the optimal fusion weights thereby ensuring non-negativity and simplex constraints while significantly reducing computational overhead compared to Sequential Quadratic Programming(SQP) or constrained gradient descent methods.
Validation was done across multiple benchmarks datasets namely, USC-SIPI Sequences (grayscale TIFF), Kodak, BSDS500, DRIVE (Digital Retinal Images for Vessel Extraction), and ISPRS Potsdam which cover natural, medical, and remote-sensing images. Per-image processing, runtime measurement, and compressed ratio (CR) were produced automatically by the provided evaluation pipeline;
The SFWE method provides greater image quality and structural fidelity across diverse datasets, attaining a PSNR of 40 dB and SSIM of 0.95, outperforming existing approaches such as Discrete Cosine Transform (DCT), Wavelet Transform, Singular Value Decomposition (SVD), and Principal Component Analysis and JPEG2000 + CNN models. In addition, it also maintains a good compression ratio leading to an effective balance between the reduction in file size as well as visual quality of the images, which confirms enhanced structural preservation across diverse image types.
• To implement a novel ensemble model (SFWE) that optimally balances the outputs of SVD and PCA for doing effective image compression.
• To achieve a higher SSIM (0.95) and good PSNR (40 dB) compared to compression techniques such as DCT, Wavelet, SVD, PCA, and JPEG2000 + CNN.
• To ensure adaptive high-quality reconstruction across multiple datasets, demonstrating its suitability for diverse image-intensive applications
Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review.
Get PDFBackground and objectiveDiabetes mellitus (DM), particularly type 2 diabetes (T2D), represents a significant global health crisis, often complicated by severe and progressive conditions such as retinopathy, neuropathy, and cardiovascular disease. Traditional diagnostic approaches frequently detect these complications at advanced stages, limiting the opportunity for early, effective intervention. This review aims to examine how recent advancements in generative artificial intelligence (AI), particularly large language models (LLMs), can transform diabetes management by enabling earlier detection and more personalized interventions.MethodsA narrative review was conducted to evaluate the current literature on the application of generative AI and LLMs in diabetes care. The review focused on how these technologies analyse multi-dimensional datasets, including medical imaging, electronic health records (EHRs), genetic profiles, and lifestyle factors, and how they process both structured and unstructured data to enhance predictive analytics and risk stratification for diabetes complications.Key content and findingsGenerative AI models have demonstrated significant promise in detecting hidden trends and early risk factors for complications such as diabetic retinopathy and neuropathy, often before clinical symptoms manifest. LLMs enhance predictive performance by synthesising unstructured data sources, such as physician notes and patient-reported outcomes, with clinical datasets. Despite limitations concerning data quality, model transparency, and ethical concerns surrounding data privacy, these technologies offer powerful tools for proactive disease monitoring and personalized care.ConclusionsGenerative AI and LLMs are poised to redefine diabetes management by enabling earlier detection of complications and personalised treatment strategies. Their integration into clinical decision support systems (CDSS) and precision medicine frameworks may reduce the global burden of diabetes, improve patient outcomes, and shift care from reactive to preventative
On the Dangers of Overthinking: A Natural Experiment on Self‐Regulatory Thought, Mind‐Wandering and Undergraduate Exam Performance
Get PDFDespite extensive research on motivational factors in academic performance, little is known about the role of ongoing conscious thought. Mind‐wandering has been linked with poor educational outcomes, yet can also benefit goal‐directed behaviour. We reasoned that mind‐wandering should benefit exam performance under certain motivational conditions, including mental contrasting (viewing one's goal in terms of both desired outcome and obstacles to achievement). In an online survey followed by an exam, university students described their assessment goal and reported expectations, exam‐related mind‐wandering (EMW) and other measures. We predicted that (A) convergence between expectations and performance would be tighter, and (B) EMW would positively predict performance, in students exhibiting mental contrasting. Contrary to predictions, we found no moderation of the expectation‐performance relationship, and regarding the EMW‐performance relationship, mental contrasters achieved especially low grades when mind‐wandering frequently about the exam, possibly reflecting a tendency to ‘overthink’ negative aspects. Theoretical and methodological implications are discussed
Digital Health Disparities: A Review of Barriers and Solutions for Racially Diverse Groups
Get PDFDigital health applications have transformed healthcare delivery by offering convenient, cost-effective means of managing chronic diseases and promoting wellness. However, racially diverse groups experience substantially lower adoption and utilisation rates compared to majority populations, revealing underlying challenges that perpetuate healthcare disparities. This narrative review examined barriers affecting adoption and utilisation of digital health apps among racially diverse groups and explored evidence-based strategies to improve engagement and ensure equitable access. A comprehensive literature search was conducted across PubMed, Scopus, Web of Science, and Google Scholar for peer-reviewed articles published between January 2010 and June 2024. Studies examining digital health app usage among racially diverse populations that discussed barriers, facilitators, or strategies for improving adoption were included. Codebook thematic analysis was employed to synthesise findings and identify recurring patterns across 38 included studies. Analysis of 38 studies revealed that digital health app adoption among racially diverse groups is significantly hindered by multifaceted barriers. Socioeconomic constraints, limited digital literacy, cultural and linguistic mismatches, and trust concerns regarding data privacy were the most common barriers. Evidence-based strategies include developing culturally tailored applications, implementing community-based digital literacy training programmes, establishing transparent data privacy practices, and fostering collaborations with trusted community organisations. Four key barrier categories: socioeconomic constraints, digital literacy limitations, cultural and linguistic mismatches, and trust concerns significantly impede digital health app adoption among racially diverse groups. Addressing these requires targeted, multidisciplinary interventions involving app developers, healthcare providers, and policymakers to ensure digital health applications are inclusive and accessible, ultimately reducing healthcare disparities. [Abstract copyright: Copyright © 2025 The Author(s). Published by Elsevier B.V. All rights reserved.
Public awareness of stroke risk factors in high-income countries: A systematic review
Get PDFPurpose
Stroke remains a significant health concern in high-income countries (HICs) and is increasing among younger adults. Although largely preventable, public awareness of stroke risk factors in HICs is not well established. We assessed awareness levels in World Bank-classified HICs and identified associated factors.
Methods
Systematic searches used Ovid MEDLINE, PsycINFO, Academic Search Complete, CINAHL, Cochrane Review Library, Emcare, and ASSIA. Two authors independently screened studies and extracted data. Risk of bias was assessed using Critical Appraisal Skills Programme checklists. Due to heterogeneity, narrative synthesis was conducted. Exploratory analyses including visual mapping and descriptive cross-country comparisons were performed despite methodological heterogeneity. Protocol registered on PROSPERO (CRD42025621931).
Findings
Of 2146 papers screened, 23 met inclusion criteria. Most studies reported low stroke risk factor awareness. Hypertension was most frequently identified, followed by smoking, dyslipidaemia, and diabetes. Sedentary lifestyle, alcohol consumption, ethnicity, and atrial fibrillation were least recognised. Risk of bias assessment revealed sampling and generalisability concerns in most studies. Most reported associations were unadjusted for potential confounders. Higher education was linked to greater awareness. Marked geographical clustering occurred, with 65 % of studies from Middle Eastern countries, predominantly Saudi Arabia.
Discussion
This review uniquely identifies critical evidence gaps including under-representation of diverse populations, lack of standardised awareness metrics, and predominance of unadjusted analyses in HIC stroke risk factor awareness research.
Conclusion
Stroke risk factor awareness gaps are prevalent and may limit prevention efforts. Large-scale, methodologically robust studies across diverse geographical, socioeconomic, and ethnic populations within HICs are urgently needed, as awareness characteristics may vary dramatically even within high-income settings. Targeted education is necessary for primary prevention strategies
Drug Review Sentiment Analysis: Applying Transformer-Based Models for Enhanced Healthcare
Get PDFAnalyzing patient feedback on drug reviews is crucial in the healthcare sector as it determines the efficacy of treatment and patient experiences. Amidst the exponential growth in patient-generated data, the method of sentiment analysis has emerged as a key means of interpreting text-based reviews. In this research, the use of various machine learning and transformer-based approaches to analyze sentiments in drug reviews and gain meaningful insights from patient reviews or opinions is outlined. It juxtaposes traditional machine learning models such as Logistic Regression, Random Forest, and Support Vector Machines with deep neural networks such as Long Short-Term Memory and transformer-based models such as Bidirectional Encoder Representations from Transformers (BERT). Various models' performance is tested using the UC Irvine drug review dataset, and data preprocessing, feature extraction, and cross-validation are used in the study. Transformers, more precisely BERT, perform better than conventional approaches at 0.96 accuracy based on findings, as they can read into intricate patterns of language and contextual hints undetectable by basic models. The research reveals how transformer-based sentiment analysis can enhance healthcare decision-making through better and context-based information
Introduction: The Commonwealth's Korean War at 75
No full text25 June 2025 marks the 75th anniversary of the outbreak of the Korean War. Yet, this brutal three-year conflict involving all the major Cold War combatants remains largely ‘forgotten’ outside the Korean peninsula except amongst historians who have long realised its global importance. The historiography, though, continues to be dominated by works focused on the United States. The role played by the Commonwealth countries involved in the Korean War are comparably few and are overwhelmingly written as discrete national histories. However, for each of these countries the conflagration had a distinctly Commonwealth dimension to it since their troops fought alongside each other and their diplomats coordinated policy together in a way never seen again. This special section, therefore, will fill a significant gap in the historical literature by examining a wide range of aspects of the conflict from a Commonwealth-wide perspective. This introduction will thus outline the Commonwealth-focused topics covered in each article – Prime Ministers’ Meetings, casualty and funerary arrangements, Japan as a forward base, the Battle of Kapyong, the air campaign, and the experience of ordinary soldiers at the 38th parallel – and why collectively this special section represents the most comprehensive study of the Commonwealth’s Korean War