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How do 9–10-year-olds conceptualise, engage in, and navigate banter within primary education? A figurational analysis
Get PDFDespite growing research concerning banter in educational settings, this article is the first to examine how primary-school aged children conceptualise, engage in, and navigate banter in England. This focus is important given this impressionable phase of childhood development and teachers and policymakers concerns regarding possible links between banter and bullying. Key findings from eight focus groups with 32 children (aged 9-10 years) are thematically analysed using theoretical concepts of individual civilizing process, habitus, and figuration. Whilst being able to differentiate ‘good’ from ‘bad’ banter, pupils conceptualised banter in a prosocial manner, reported regularly engaging in banter for enjoyment and social bonding purposes. Furthermore, pupils navigated banter by appraising content, relationships between those involved, and how comments were received. The figurational dynamics within the school day meant that banter most often took place within breaktimes, whereby pupils mostly engaged with like-minded same-sex peers. To differentiate good from bad banter and navigate such banter, pupils had to exhibit relatively sophisticated cognitive, emotional, and social intelligence. To substantiate and develop our findings, ethnographic research is needed to gather observations of pupils’ (and possibly teachers’) engagement in banter and the extent that banter is self-regulated and/or socially constrained by peers and teachers
Capturing the process of knowledge creation: creative approaches for disrupting good form in PhD theses and duoethnography
Get PDFThis duoethnography explores our use of creative writing in our education doctoral theses by taking an emergent and experimental approach, including written and verbal dialogues and knitted narrative. Duoethnographies should be transparent in their processes and open to different interpretations. However, IMRaD (Introduction, Methodology, Results and Discussion) as a guiding structure in the writing of duoethnographies elides underpinning processes and closes down the potential for meaning-making. Our innovative approach to duoethnography enables us to arrive at new understandings of the relationships between our identities, our writing and our ethical practices. We also reflect on how written and verbal dialogues offer different affordances for reflection and transformation in duoethnographies. By deliberately presenting our duoethnography as disrupting IMRaD, we show other ethnographers how they can become more transparent about processes and open up the potential for multiple interpretation
AI-Driven Advancements in Orthodontics for Precision and Patient Outcomes
Get PDFArtificial Intelligence (AI) is rapidly transforming orthodontic care by providing personalized treatment plans that enhance precision and efficiency. This narrative review explores the current applications of AI in orthodontics, particularly its role in predicting tooth movement, fabricating custom aligners, optimizing treatment times, and offering real-time patient monitoring. AI’s ability to analyze large datasets of dental records, X-rays, and 3D scans allows for highly individualized treatment plans, improving both clinical outcomes and patient satisfaction. AI-driven aligners and braces are designed to apply optimal forces to teeth, reducing treatment time and discomfort. Additionally, AI-powered remote monitoring tools enable patients to check their progress from home, decreasing the need for in-person visits and making orthodontic care more accessible. The review also highlights future prospects, such as the integration of AI with robotics for performing orthodontic procedures, predictive orthodontics for early intervention, and the use of 3D printing technologies to fabricate orthodontic devices in real-time. While AI offers tremendous potential, challenges remain in areas such as data privacy, algorithmic bias, and the cost of adopting AI technologies. However, as AI continues to evolve, its capacity to revolutionize orthodontic care will likely lead to more streamlined, patient-centered, and effective treatments. This review underscores the transformative role of AI in modern orthodontics and its promising future in advancing dental care
Sustainable Green Marketing Strategies for a Circular Economy in Africa: A Conclusion
No full textThe status of green marketing implementation and achievements in Africa point to a bleak future. There is general consensus in extant literature that the African continent is lagging behind in attaining environmental sustainability targets (Asongu & Odhiambo, 2021; Aluko et al., 2023). Empirical evidence also suggests that the African continent is warming more rapidly as compared to other continents (Avom et al., 2020; Aluko et al., 2023). This, according to Avom et al. (2020), is manifested by the escalation of climate change-induced grand problems such as droughts, floods, poverty, forced migration, health pandemics and social inequalities. Moreover, the growing rate of unemployment characterised by de-industrialisation and rapid informalisation of African economies is at odds with the green marketing promise of green economic growth (Asongu & Odhiambo, 2021). This further raises doubt about the potential of green marketing as a driver of economy growth in Africa
Translation through music, speaking through music, The Shape of Water
Get PDFTaking a multimodal example, that of the film The Shape of Water (2017), this article explores the current research on translation and music, then examines the role of music in this film to illustrate how translation between modes is possible through the notion of metaphor. The multimodal context is complex and offers a way to tackle how research into musical meaning has the potential to apply translation processes to support understanding in a manner that is original. Music is the key agent in expressing emotion through metaphor. The article offers a case for how translation studies can and should be used more widely within the arts and humanities, through using this particular film example
An Exploration of Dog‐Related Policy Through a Legal Animal Geographies Lens
Get PDFThis article employs a legal animal geographies lens to redefine dogs as more‐than‐property, focusing on the UK’s legislative shift from the Theft Act 1968 to the Pet Abduction Act 2024, recognising their sentience. We explore how evolving societal values reshape legal frameworks, emphasising dogs’ emotional and social significance in human‐dog relations. The study examines three legislative approaches ‐ controlling out‐of‐place animals, regulating animal materiality, and protecting against harm ‐ revealing their spatial and political dimensions. By analysing geographies of UK dog theft, we highlight patterns, victim experiences, and the property‐companion divide. This shift challenges anthropocentric and speciesist legal systems, offering a model for multispecies justice with global policy impact. We envision future research, including non‐western and indigenous perspectives, to advance ethical human‐animal governance. Bridging animal and legal geographies, this study provides critical insights for students, researchers, and policymakers to understand and reform human‐animal relations worldwide, advocating for ethical, evidence‐based policies
Stacked Ensemble Learning for Classification of Parkinson's Disease Using Telemonitoring Vocal Features.
Get PDFBackground: Parkinson's disease (PD) is a progressive neurodegenerative condition that impairs motor and non-motor functions. Early and accurate diagnosis is critical for effective management and care. Leveraging machine learning (ML) techniques, this study aimed to develop a robust prediction system for PD using a stacked ensemble learning approach, addressing challenges such as imbalanced datasets and feature optimization. Methods: An open-access PD dataset comprising 22 vocal attributes and 195 instances from 31 subjects was utilized. To prevent data leakage, subjects were divided into training (22 subjects) and testing (9 subjects) groups, ensuring no subject appeared in both sets. Preprocessing included data cleaning and normalization via min-max scaling. The synthetic minority oversampling technique (SMOTE) was applied exclusively to the training set to address class imbalance. Feature selection techniques-forward search, gain ratio, and Kruskal-Wallis test-were employed using subject-wise cross-validation to identify significant attributes. The developed system combined support vector machine (SVM), random forest (RF), K-nearest neighbor (KNN), and decision tree (DT) as base classifiers, with logistic regression (LR) as the meta-classifier in a stacked ensemble learning framework. Performance was evaluated using both recording-wise and subject-wise metrics to ensure clinical relevance. Results: The stacked ensemble learning model achieved realistic performance with a recording-wise accuracy of 84.7% and subject-wise accuracy of 77.8% on completely unseen subjects, outperforming individual classifiers including KNN (81.4%), RF (79.7%), and SVM (76.3%). Cross-validation within the training set showed 89.2% accuracy, with the performance difference highlighting the importance of proper validation methodology. Feature selection results showed that using the top 10 features ranked by gain ratio provided optimal balance between performance and clinical interpretability. The system's methodological robustness was validated through rigorous subject-wise evaluation, demonstrating the critical impact of validation methodology on reported performance. Conclusions: By implementing subject-wise validation and preventing data leakage, this study demonstrates that proper validation yields substantially different (and more realistic) results compared to flawed recording-wise approaches. The findings underscore the critical importance of validation methodology in healthcare ML applications and provide a template for methodologically sound PD classification research. Future research should focus on validating the model with larger, multi-center datasets and implementing standardized validation protocols to enhance clinical applicability
Enhancing leukemia detection in medical imaging using deep transfer learning
Get PDFBackground
Acute Lymphoblastic Leukemia (ALL) is the most common pediatric cancer, requiring early detection to save lives and reduce the financial burden of advanced-stage treatment. While traditional diagnostic methods are time-consuming and resource-intensive, deep transfer learning offers a computationally efficient alternative for medical image classification.
Method
This study employed two widely recognized transfer learning algorithms, VGG-19 and EfficientNet-B3, to detect ALL using a publicly available dataset of 10,661 images from 118 patients. Data preprocessing included resizing, augmentation, and normalization. The models were trained for 100 epochs, with batch sizes of 30 for VGG-19 and 32 for EfficientNet-B3. Evaluation metrics such as accuracy, precision, recall, and F1 score were used to assess model performance. Statistical significance testing was performed using paired t-tests (p < 0.05). Comparative analysis was performed with existing studies to validate the findings.
Results
EfficientNet-B3 significantly outperformed VGG-19, achieving an average accuracy of 96 % compared to 80 % for VGG-19 (p < 0.001). EfficientNet-B3 demonstrated superior performance in handling class imbalance, with the minority class (Hem) achieving precision, recall, and F1 scores of 97 %, 89 %, and 93 %, respectively. VGG-19 struggled with the minority class, achieving lower recall (51 %) and F1 score (62 %). However, dataset limitations including single-source origin may affect generalizability.
Conclusion
This study highlights the effectiveness of EfficientNet-B3 as a reliable tool for early ALL detection, offering high accuracy and computational efficiency. Clinical implementation requires addressing computational constraints and integration challenges. Future research could integrate multimodal datasets to identify risk factors and further improve diagnostic accuracy
Implementation and evaluation of a supervised exercise programme for people with claudication in York, England
No full textBackground
Supervised exercise therapy (SET) has been shown to improve claudication symptoms in patients with peripheral artery disease (PAD), and it is recommended as a first-line treatment in national and international guidelines. Despite this, supervised exercise programmes have not been widely implemented in many countries. This quality improvement project aimed to implement and evaluate an exercise service for people with claudication in York, England.
Methods
The York Claudication Exercise Service was launched in October 2023. Eligible patients were referred from vascular clinics at York Hospital. The service provided each participant with two, 1-hour exercise sessions per week over a 12-week programme. Standardised assessments were performed before and after the programme. Routinely assessed outcomes (e.g., recruitment, attendance, satisfaction, and treadmill walking distances) were used to evaluate the service over the first 12 months. Descriptive statistics were used to explore feasibility, acceptability, fidelity, and preliminary effects. A cost-comparison analysis was also conducted before and after the exercise service was implemented.
Results
By May 2024, 65 eligible patients had been referred, with 29 patients (44.6 %) commencing the exercise sessions. The exercise programme was delivered as intended and the median number of sessions attended was 19 (out of 24). At service exit, 13 (59.1 %) out of 22 participants reported an improvement in their claudication symptoms and were discharged to primary care. The mean (95 % CI) increase in pain-free walking distance was 110 m (39 to 182). All but one participant rated the service as ‘good’ or ‘excellent’. Economic modelling estimated that the programme would result in an annual cost-saving of £223.21 per person, or £366.40 per person using estimated costs for a future delivery model.
Conclusions
The service was successfully implemented within the existing care pathway. The evaluation indicated a high level of patient satisfaction, improvement in claudication symptoms and prevention of unnecessary referrals for vascular imaging and revascularisation. Agreements have been obtained to continue the service for at least 2 more years. During this period, sustainability funding will be sought, and the service will be adapted to improve access and uptake
Theoretical Foundations of GenAI-Informed Teacher Pedagogy
Get PDFThe integration of Generative Artificial Intelligence (GenAI) into educational pedagogy represents a transformative shift in the dynamics of teaching and learning. To guide this transition, this paper introduces the Ped-AI-gogy Informed Model (PIM), which combines established educational frameworks: the Technology Acceptance Model (TAM), the SAMR model and Technological Pedagogical Content Knowledge (TPACK), into a cohesive approach for GenAI integration. This model provides a progressive pathway for educators, moving from initial awareness to active advocacy, while addressing the complexities of technology adoption, pedagogical change and shifting educator-learner
relationships.
In addition, this paper develops the theoretical foundation of “ped-AI-gogy”, a concept that fuses pedagogy with AI to reimagine teaching practices in anincreasingly digital landscape. By situating this integration within a posthumanist perspective, the authors advocate for a collaborative, symbiotic relationship among educators, students and GenAI tools. Finally, the paper critiques traditional human-centred educational paradigms and calls for adaptive learning models that harness GenAI
potential to enhance both teaching and learner agency