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Disaster identification scheme based on federated learning and Cognitive Internet of Vehicles
No full textThe rate of disaster occurrences has been increasing over the last decade due to the alarming effects of global warming. A major challenge with such disasters is identifying their nature before substantial loss of lives and property occurs. Existing systems often fail to determine the type of disaster until significant damage has been done. This research proposes a novel scheme to identify disasters as they occur, leveraging Federated Learning (FL) and Cognitive Internet of Vehicles (CIoV) since vehicles are a common presence in disaster scenarios. The proposed scheme utilizes various machine learning (ML) and deep learning algorithms to predict disaster types in real-time. Additionally, it introduces a custom federated averaging algorithm to maintain result privacy. The research evaluated the scheme’s performance using a data set of recorded disasters from various countries, training different algorithms to determine optimal results. The results indicate that the proposed scheme can achieve a 90% accuracy in disaster-type identification using deep learning and random forest algorithms
Reinforcing inclusion: Arts teachers' micro-adaptations for students with SEND.
No full textThe article focuses on the qualitative research examining how arts teachers implement micro-adaptations to support the inclusion of students with special educational needs and/or disabilities (SEND) in a boys' secondary school. It highlights the importance of adaptive teaching, which emphasizes meeting diverse student needs through spontaneous, in-the-moment decisions rather than pre-planned tasks. The findings reveal that teachers effectively capitalized on students' strengths and scaffolded skill-based tasks, thereby reinforcing inclusive practices. The study underscores the need for further exploration of teachers' reflective practices to enhance support for students with SEND
Personal Journeys of Transition Beyond the Care System in England: Voices of Care-Experienced Young People from the I-CAN Programme
No full textCare-experienced young people often face considerable challenges due to a personal history of trauma and disruption and have a higher risk of homelessness, mental ill health, and involvement with the criminal justice system. A stubborn trend of achieving fewer qualifications than non-care-experienced peers persists, with greater likelihood of becoming NEET (Not in Education, Employment or Training). Accessible and sustainable pre-employment programmes should be a priority for national initiatives designed to generate improved outcomes for vulnerable youth. The I-CAN (Initiating and Supporting Care Leavers intoApprenticeships in Nursing) programme offers young people in England (aged 18–30) a person-focussed pathway to training and employment. However, robust research is needed to evidence the effectiveness of this type of small-scale and short-term funded programme. The current paper reports qualitative findings from a pilot study exploring the perceptions and experiences of (N = 27) young people who attended the 8-week I-CAN programme delivered at a Higher Education Institution. Data were collected from four focus groups and thematically analysed. The findings captured young people’s personal trajectoriesand exposed underpinning processes as well as unique, shared, and intersectional factors that can either facilitate or impede progression to education, employment and training. Crucially, care-experienced young people are not a homogenous group and capturing their authentic, diverse voices in evaluation research is essential for not only assessing if a programme works but for whom, and why. Furthermore, findings can help to inform meaningful strategies and socially valid interventions to support care-experienced young people navigate the transition ‘cliff edge’
A Semiautomated Approach for Detecting Ambiguities in Software Requirements Using SpanBERT and Named Entity Recognition
No full textAmbiguous user requirements present a challenge in software requirement engineering. A manual approach to handling ambiguity is time‐consuming. Software requirements are essential inputs to software development processes, including architecture and design, implementation, and testing. Requirement ambiguities lead to project cost overruns, delays in project delivery, and poor software product quality. Timely identification and correction of ambiguity can result in better software systems that meet product objectives and satisfy the needs of all stakeholders. This study explores various natural language processing techniques and SpanBERT (a variant of BERT). This research proposes a semiautomated approach for detecting anaphoric, coordination, and missing condition ambiguities in functional requirements. The proposed approach is validated on a new, original dataset containing 425 functional requirements from 16 domains. The ambiguities identified through our approach are compared with those detected manually and by ChatGPT. Our approach outperforms ChatGPT in detecting ambiguities. The proposed approach will aid project managers and requirement engineers in identifying ambiguities in requirement specifications, thereby helping to reduce cost overruns and delays in the software development process caused by requirement ambiguities
AI-Powered Adaptive Disability Prediction and Healthcare Analytics Using Smart Technologies
No full textBackground: By leveraging advanced wireless technologies, Healthcare Industry 5.0 promotes the continuous monitoring of real-time medical acquisition from the physical environment. These systems help identify early diseases by collecting health records from patients’ bodies promptly using biosensors. The dynamic nature of medical devices not only enhances the data analysis in medical services and the prediction of chronic diseases, but also improves remote diagnostics with the latency-aware healthcare system. However, due to scalability and reliability limitations in data processing, most existing healthcare systems pose research challenges in the timely detection of personalized diseases, leading to inconsistent diagnoses, particularly when continuous monitoring is crucial. Methods: This work propose an adaptive and secure framework for disability identification using the Internet of Medical Things (IoMT), integrating edge computing and artificial intelligence. To achieve the shortest response time for medical decisions, the proposed framework explores lightweight edge computing processes that collect physiological and behavioral data using biosensors. Furthermore, it offers a trusted mechanism using decentralized strategies to protect big data analytics from malicious activities and increase authentic access to sensitive medical data. Lastly, it provides personalized healthcare interventions while monitoring healthcare applications using realistic health records, thereby enhancing the system’s ability to identify diseases associated with chronic conditions. Results: The proposed framework is tested using simulations, and the results indicate the high accuracy of the healthcare system in detecting disabilities at the edges, while enhancing the prompt response of the cloud server and guaranteeing the security of medical data through lightweight encryption methods and federated learning techniques. Conclusions: The proposed framework offers a secure and efficient solution for identifying disabilities in healthcare systems by leveraging IoMT, edge computing, and AI. It addresses critical challenges in real-time disease monitoring, enhancing diagnostic accuracy and ensuring the protection of sensitive medical data
Exploring AI-powered Digital Innovations from A Transnational Governance Perspective: Implications for Market Acceptance and Digital Accountability
Get PDFLaunch of a new toolkit for schools to improve support and inclusion for SEND pupils with 22q11.2 Deletion Syndrome
No full textDo pre- and post- rehabilitation influence hospital length of stay and patient recovery following total hip replacement in the UK?:A systematic scoping review and Delphi study
No full textPre-operative rehabilitation is an effective means of reducing Length of Stay (LoS), but little evidence is available on its influence on Total Hip Replacement (THR) patients. Hence, a review of UK-based experimental studies to evaluate the effect of physical therapy on LoS was performed. Subsequently, a narrative synthesis was employed to develop a three-round Delphi study targeting UK clinicians. The review and Delphi results showed that patients had higher satisfaction with education clinics, but nurse-led assessment and physiotherapist education were not superior to standard pre-operative practices. The Delphi highlighted the challenges and benefits of (p)rehabilitation and provided suggestions for THR best practices. In conclusion, the study shows that there is a lack of evidence on the effectiveness of (p)rehabilitation on LoS; further research is recommended into education, (p)rehabilitation and patient self-efficacy interventions to enhance THR patients' pathways.</p
Adaptive Fisher Behavior Alone Can Induce Tipping Points and Stock Collapse: A Synthesis of Bioeconomic Theory That Applies to All Capture Fisheries Under Open Access
No full textLeadership dilemmas in early childhood settings: the perceptions of early childhood leaders and the looking-glass self
Get PDF©2025, Emerald Publishing Limited. This is an author produced version of a paper published in International Journal of Educational Management uploaded in accordance with the publisher’s self- archiving policy. The final published version is available online at the link. Some minor differences between this version and the final published version may remain. We suggest you refer to the final published version should you wish to cite from it. PurposeWith widespread concerns around the funding and availability of care and education during early childhood, this article takes a social psychological approach to exploring some of the dilemmas facing early childhood leaders. Study design/methodology/approachThe study uses an interpretivist, qualitative approach involving semi-structured interviews with six early childhood leaders in England. The participants’ perspectives are analysed using the symbolic interactionist concept of the looking-glass self, neoliberal theory and the systems leadership approach.FindingsThe positioning of leaders is greatly influenced by their self-image and sense of self related to how they perceive the expectations of others about their roles as leaders and the feelings of pride, recognition or disapprobation and marginalization. The looking-glass self brings insights that systems leadership and neoliberal critiques alone do not give, including that dilemmas are the daily fare of leaders in their settings rather than problems that can be neatly solved.OriginalityExploring the perspectives of early childhood leaders through the looking-glass self generates new research agendas for early childhood leadership that contribute to the understanding of agency and structure. Our use of the looking-glass self is original in the field of leadership in that we apply it to the analysis of leadership data as well as deploying it to critique systems leadership and neoliberalism. The practical implications for leadership practice and development include the need for leaders to understand their interactions with others, how that may affect their sense of self, and how this links to macro-issues in the sector