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Understanding The Gig Worker's Internal Processes in Digital Labour Ecosystem: A Narrative Literature Review
No full textThis developmental paper presents initial findings from a narrative literature study on the digital ecosystem, gig workers, and the gig economy. Our analysis is based on 24 articles included in the narrative literature review. As a result, we propose a conceptual framework for the digital labour ecosystem, drawing on evidence from current digital ecosystems through a labour perspective. Furthermore, we identify seven key personal resources of gig workers. This paper provides a broader perspective on the digital ecosystem by examining workers' mechanisms. Our findings also highlight how these seven personal resources, specific to gig workers, differ from those of traditional employees
Strengthening the Foundation - The PaCT Workshop Embedding Lived Experience in Nursing Education
No full textWinning presentation in the category of 'Strengthening the Foundation' at the Picker Experience Network Awards 2025Health and Social Care Organisations, Higher Education Institutions, Local Authorit
An empirical study on the impact of digital marketing strategy to consumer decision making and customer communication in Islamic banking sector a case study of the UK
No full textThis study aims to investigate the effects of digital marketing strategy implementation on customer communication and consumer decision making within the Islamic banking industry in the United Kingdom. Earlier theories states that the banking industry's marketing and advertising department requires an effective method to attract a substantial number of customers which is the research problem identified. Hence, it is critical to assess the potential efficacy of marketing strategies that banks can adopt to enhance profitability in this domain. The researcher was motivated to investigate the viability of digital marketing strategies in Islamic institutions operating in the United Kingdom due to the aforementioned factors. However, marketers operating in Islamic institutions have been constrained in their ability to implement digital marketing strategies due to the dearth of research in this particular sector. To ensure an exhaustive dataset for the study, the researcher employed a mixed methodology approach, which facilitated the collection of both qualitative and quantitative data from Islamic institutions in the United Kingdom in particular. The researcher employed a combination of quantitative and qualitative data collection methodological approaches. NVIVO software was employed to analyze a qualitative dataset collected via semi-structured interviews using thematic analysis. In contrast, the researcher employed questionnaires to administer surveys and analyze quantitative data using the IBM SPSS statistical software, adhering to the T-test correlation matrix. The researcher intends to collect the data from respondents affiliated with Islamic banking institutions. Ten marketing executives from financial institutions were invited to participate in the interviews, and an additional 150 clients utilising the services of the Islamic banking sector were surveyed. The findings indicated that the responses exhibited internal consistency, as assessed by Cronbach's alpha. This provides further support for the reliability of the questionnaire utilised in the study. Furthermore, to ascertain the validity of the content, a content validity score and index were formulated based on the input of ten subject matter experts (SMEs). In contrast to the normal distribution of the data, the Mann-Whitney U test was employed to facilitate comparisons. The Mann-Whitney U test exposes deficiencies in the digital marketing strategy's ability to effectively engage with consumers; this is further supported by the Chi-Square results. The findings concluded that digital marketing strategies promoted communication among customers, and improved communication resulted in enhanced engagement, better awareness regarding Islamic laws, and better understanding of the purchasing behaviour of consumers
In-hospital mortality of 121,262 emergency patients according to their National Early Warning Score, alertness and eight physiologic categories on admission to hospital
No full textAim: To determine the in-hospital mortality of eight physiological categories based on shockindex, pulse pressure and ROX index, and to compare each category according to admissionlevel of consciousness and National Early Warning Score.Method: A non-interventional observational study of 122,262, unselected, adult emergencyadmissions between 2014 and 2022.Results: In-hospital mortality increases according to physiological category and whether theadmission NEWS was<3 or ≥3. For NEWS ≥3, patients were more likely to die when not alert.Irrespective of total NEWS, patients with a low ROX index <22 are more likely to die.Conclusion: Patients with the same NEWS value can have different physiologicalderangements. Level of consciousness also provides greater insight than NEWS aloneregarding the risk of in-patient mortality
A Critical Literature Review of Social Mobility in Britain's Further Education Sector
No full textThis article critically reviews the relationship between social mobility and Britain's Further Education (FE) sector, tracing the concept's evolution and the dominance of intergenerational measures in research and policy. It argues that this focus obscures the short-and medium-term impacts of FE, which are better captured through intragenerational approaches. Frameworks such as Bourdieu's forms of capital are noted for their frequent but partial use, often serving as categorisation tools rather than fully applied analytical models. The review highlights structural and policy constraints—including the hierarchy of qualifications, narrowing vocational pathways, and the limitations of proxies such as Free School Meal (FSM) eligibility—that undermine FE's capacity to promote upward mobility for disadvantaged learners. It concludes that persistent data limitations and policy biases towards Higher Education (HE) obscure FE's distinct role, calling for standardised intragenerational measurement frameworks, improved high-frequency longitudinal datasets, and rigorous evaluation of qualification reforms to better understand and enhance FE's contribution to social mobility
Embedding social prescribing into undergraduate health and social care education: implementation and evaluation
No full textSocial Prescribing ProjectUniversity of Greater Manchester Aacdemics and Researcher
Detection and classification of brain tumor using a hybrid learning model in CT scan images
No full textAccurate diagnosis of brain tumors is critical in understanding the prognosis in terms of the type, growth rate, location, removal strategy, and overall well-being of the patients. Among different modalities used for the detection and classification of brain tumors, a computed tomography (CT) scan is often performed as an early-stage procedure for minor symptoms like headaches. Automated procedures based on artificial intelligence (AI) and machine learning (ML) methods are used to detect and classify brain tumors in Computed Tomography (CT) scan images. However, the key challenges in achieving the desired outcome are associated with the model's complexity and generalization. To address these issues, we propose a hybrid model that extracts features from CT images using classical machine learning. Additionally, although MRI is a common modality for brain tumor diagnosis, its high cost and longer acquisition time make CT scans a more practical choice for early-stage screening and widespread clinical use. The proposed framework has different stages, including image acquisition, pre-processing, feature extraction, feature selection, and classification. The hybrid architecture combines features from ResNet50, AlexNet, LBP, HOG, and median intensity, classified using a multilayer perceptron. The selection of the relevant features in our proposed hybrid model was extracted using the SelectKBest algorithm. Thus, it optimizes the proposed model performance. In addition, the proposed model incorporates data augmentation to handle the imbalanced datasets. We employed a scoring function to extract the features. The Classification is ensured using a multilayer perceptron neural network (MLP). Unlike most existing hybrid approaches, which primarily target MRI-based brain tumor classification, our method is specifically designed for CT scan images, addressing their unique noise patterns and lower soft-tissue contrast. To the best of our knowledge, this is the first work to integrate LBP, HOG, median intensity, and deep features from both ResNet50 and AlexNet in a structured fusion pipeline for CT brain tumor classification. The proposed hybrid model is tested on data from numerous sources and achieved an accuracy of 94.82%, precision of 94.52%, specificity of 98.35%, and sensitivity of 94.76% compared to state-of-the-art models. While MRI-based models often report higher accuracies, the proposed model achieves 94.82% on CT scans, within 3-4% of leading MRI-based approaches, demonstrating strong generalization despite the modality difference. The proposed hybrid model, combining hand-crafted and deep learning features, effectively improves brain tumor detection and classification accuracy in CT scans. It has the potential for clinical application, aiding in early and accurate diagnosis. Unlike MRI, which is often time-intensive and costly, CT scans are more accessible and faster to acquire, making them suitable for early-stage screening and emergency diagnostics. This reinforces the practical and clinical value of the proposed model in real-world healthcare settings
Deep learning-based health risk prediction in contact sports using wearable sensor data
No full textThis study presents a deep learning-based approach to predicting physiological health risks in athletes engaged in contact sports using wearable sensor data. Motivated by the need to detect early warning signs of collapse or severe fatigue, this study employs a Long Short-Term Memory (LSTM) neural network to analyse multivariate time-series data. Key physiological signals, including heart rate, body temperature, and motion, were extracted from the PAMAP2 dataset to train and validate the model. The LSTM demonstrated strong predictive performance, achieving an accuracy of 98.3% in identifying potentially dangerous physiological states. In addition to its high classification accuracy, the model effectively captured temporal dependencies in the data, underscoring its suitability for health risk prediction in dynamic, high-intensity sports environments. This study highlights the potential of wearable data and LSTM-based analysis in supporting proactive athlete health management and injury prevention
Dialogic learning in the Age of Generative AI
No full textWhat does it truly mean to learn with a machine, and are machines capable of engaging in dialogic learning? Generative AI models—capable of producing text, images, or other content in response to prompts—are rapidly reshaping educational discourse by introducing 'scalable' forms of personalised learning, while also raising challenges around academic integrity and the need to redefine what critical thinking entails in the context of learning with AI. More recently, features within popular Generative AI models like ChatGPT's " Study and Learn mode " (which guides learners with questions instead of just giving answers) and Google Gemini's ''Learn Your Way'' (which transforms textbooks into interactive, AI-driven study guides) are being marketised on the promise of more conversational, personalised learning experiences that are fine tuned for learning based on education research and principles. Within higher education, the growing presence of these systems demands deeper exploration. Are they genuinely expanding the possibilities for dialogue and feedback, or quietly reshaping the conditions of academic exchange? As practitioners, we must ask not only how these systems work, but also why they are used—and for whom? Do they stimulate enquiry, or do they replace the productive discomfort of genuine dialogue with frictionless interactions that risk remaining superficial (Tang et al, 2024; Wu et al., 2025)
Symmetrically etched plastic optical fiber sensor for the detection of ethylene glycol contamination in water
No full textHuman activities are increasingly contaminating surface and groundwater reserves. Among various pollutants, ethylene glycol (EG) contamination in water is particularly dangerous. At low concentrations it can enter the body undetected and causes serious health problems such as kidney failure and gastrointestinal disorders. This study demonstrates the use of symmetrically etched single-mode plastic optical fiber (POF) sensor model operating at 1550 nm for detecting EG presence in water using COMSOL Multiphysics. The working of the sensor is based on evanescent field interactions with surrounding medium to detect refractive index (RI) changes, while transmission variations through etched POF serving as the sensing metric. Simulations were conducted for aqueous EG solutions ranging from 0 to 0.15 weight fraction, corresponding to RI values ranging between 1.316 and 1.330. The sensor design was optimized by examining the impact of etched cladding diameter and etched length on sensitivity. These parameters were varied from 60 to 7.05 and 1 to 30 μm, respectively. This in turn lead to sensitivity values in the range of 0.39 × 10−3to 99.50 × 10−3Trans. (A.U)/RIU. Highlighting the importance of evanescent field-surrounding interaction for etched POF sensors, these findings revealed that sensitivity has direct relation with the length of etched region and inverse relation with cladding diameter. The maximum sensitivity of 99.50 × 10−3 Trans. (A.U)/RIU was achieved with a 30 μm etched length and 7.05 μm cladding diameter. The proposed POF-based sensor demonstrates strong potential for applications in biomedical engineering, biochemical monitoring, and beverage industry offering a compact and sensitive solution for EG contamination detection in water