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The efficacy of coaches’ delivery of teaching personal and social responsibility in a youth sports club
No full textAdult Suicidality and Therapeutic Engagement:A Systematic Review
Get PDFBACKGROUND: Engagement in mental health care among adults with suicidality remains significantly low, with limited understanding of the factors influencing treatment retention and dropout.AIMS: Exploring the factors influencing therapeutic engagement, such as attendance, active participation and completion of therapeutic tasks among adults with suicidality, could offer valuable insights for suicide prevention and management.METHODS: This systematic review explored quantitative and qualitative studies examining barriers and/or facilitators to treatment engagement. Four main databases have been explored (PubMed, Embase, PsycInfo and Web of Science). Data were analysed and thematically organised with the help of a content analysis.RESULTS: Eighteen studies focusing on adults experiencing various forms of suicidality, including suicidal ideation, behaviours, plans and attempts, have been included. Engagement is mainly perceived through appointment attendance, active participation or a multidimensional combination of factors. Logistical constraints (service availability, time and money) emerge as the most significant barrier to engagement, followed by stigma, treatment-related experiences and beliefs and patient perception factors. No consensus between studies can be isolated regarding the impact of social support, severity of suicidal symptoms and psychiatric comorbidities. Trust in mental health professionals and interventions facilitating active patient participation positively influence treatment continuation.CONCLUSION: This review highlights the need to increase mental health funding to enhance service availability and improve the training of mental health professionals in suicide prevention and management. Developing integrative and empowering interventions fosters an essential climate of trust and hope in therapy that enhances engagement. Future research should prioritise study designs that distinguish between perceived and actual barriers to engagement to develop targeted interventions.</p
Design and Implementation of Real-Time Tomato Plant Growth Monitoring System using Deep Learning based YOLO and Raspberry Pi
Get PDFTomatoes are widely used in India for daily meals and are very sensitive to environmental changes, pests, and diseases, which can have a significant impact on the growth and productivity of the crop. To address these issues, it is important to monitor plant growth. This paper presents the design and implementation of a real-time tomato growth-monitoring system based on a deep learning YOLO model and Raspberry Pi. The system leverages the YOLOv8 architecture for accurate detection and classification of the tomato plant growth stage and anomalies in real time. The Raspberry Pi serves as the central processing unit, integrating camera sensor data and image analysis to provide a cost-effective, portable, and scalable solution. This system incorporates high-resolution cameras to capture real-time images. The proposed system demonstrated high accuracy in detecting various growth stages and facilitating timely interventions. This technology provides an efficient and automated approach to precision agriculture, enabling farmers to optimize resource utilization, improve yields, and reduce the environmental footprint of tomato cultivation. The performance and feasibility of the system were validated through extensive testing in controlled and open-field environments, highlighting its potential for adoption in smart agriculture
Beyond employability: Repositioning industry placements as strategic University–Industry relationships
No full textAlthough industry placements are widely used across UK higher education, their potential as strategic platforms for university-industry (UI) collaboration remains insufficiently explored. Conventionally conceptualised as mechanisms for enhancing student employability, placements are rarely framed as foundational vehicles for knowledge exchange and long-term relationship-building between universities and businesses. This paper reconceptualises industry placements as an organic model of UI engagement, situated within a triadic relationship between students, universities, and industry partners. Drawing on qualitative evidence from multiple placement programmes, we identify organisational, communicative, and structural dynamics that either constrain or enable the transformation of placements into broader strategic partnerships. Key barriers include misaligned institutional priorities and limited stakeholder involvement, whereas enablers include geographical proximity, mutual trust, and proactive academic engagement. We conclude by proposing a framework for embedding placements as integral components of the innovation-employability ecosystem, offering actionable recommendations for universities and industry stakeholders
Evaluating effectiveness of the G-EPIC 5-class intervention in increasing levels of political self-efficacy of disadvantaged students; National Reports from 5 countries
No full textThe Final Combined 5 National Reports on the Effectiveness of the G-EPIC Intervention brings together evidence from five European countries to assess the impact of the Girls’ Empowerment through Politics in Classrooms programme.The report opens with a cross-national synthesis that summarises the main findings, key outcomes, and shared lessons across all participating countries. It then presents five detailed, stand-alone national chapters, each documenting the implementation process and results within its specific context. The United Kingdom report appears first, reflecting its role in developing the original G-EPIC model, followed by reports from Belgium, the Czech Republic, Denmark, and Germany, which contributed through piloting and subsequent upscaling
Impact of a Recipe Kit Scheme (BRITE Box) on Cooking and Food‐Related Behaviours of Children and Families: Exploring Parental/Carer Views
No full textBackground: Dietary intakes in UK children fail to meet national recommendations, especially in low‐income groups. Involving children in food preparation and cooking may enhance acceptability of a wider range of foods, enhance their skills and increase their enjoyment of food. An innovative recipe meal kit scheme, Building Resilience in Today's Environment (BRITE) Box, was developed during the pandemic primarily to address food insecurity (FI). Administered via schools, it offers pre‐weighed ingredients sufficient for a meal for a family of five, plus a child‐focused recipe, weekly during school termtimes. Methods: Qualitative and quantitative exploration of BRITE Box using questionnaires and semi‐structured interviews among parents/carers of children receiving the boxes was conducted at two timepoints a year apart. Results: A total of 154 parents/carers completed questionnaires and 29 were interviewed. Responses indicated multiple benefits of the scheme, including increased confidence in cooking among both children and parents/carers. Both questionnaire responses and interviews suggested improvements in a range of food‐related behaviours, including cooking and eating together and talking more about food. Parents/carers suggested that their children were more willing to eat vegetables and healthy foods and to try new foods and flavours. They also reported greater use of leftovers thereby potentially reducing food waste. Improved behaviours, willingness to try new foods and flavours, reduced food waste and lower stress of trying to think of new and acceptable family meals are likely to have contributed to the positive impact on their mental health reported by BRITE Box parents/carers. Conclusions: Meal kits for children may improve dietary diversity, enhance enjoyment and skills and impact positively on a range of family food‐related behaviours. We argue that BRITE Box has the potential for widespread positive impacts on cooking and food‐related behaviours in children and families, meriting wider study and dissemination as a positive approach to healthy eating in children
Future-Proofing Management Education:Embedding Sustainability & Responsible Leadership into Teaching Practice
Get PDFThis submission presents an innovative pedagogical framework - the Transformative Sustainability Leadership (TSL) - designed to revolutionize how sustainability and responsible leadership are embedded within management education. Operating through three interconnected dimensions: Immersive Challenge-Based Learning Ecosystems, Deep Systems Thinking and Ethical Leadership Development, and Innovation and Impact Measurement Hub, the practice integrates cutting-edge technologies with real-world applications. The framework leverages partnerships with global sustainability leaders, including the UN Global Bodies' AGILE initiative in Nigeria, while incorporating emerging technologies such as VR/AR and blockchain for impact tracking. Early results demonstrate promising outcomes, with 90% of graduates projected to secure sustainability leadership roles and multiple student-led ventures receiving external funding. The practice addresses contemporary challenges in management education by dismantling traditional silos, establishing measurable impacts, and developing leaders capable of driving systemic change. This approach represents a bold reimagining of management education that places sustainability and responsible leadership at its core, while establishing a replicable model for transformation in business education
Intrusion detection in smart grids using artificial intelligence-based ensemble modelling
No full textFor efficient distribution of electric power, the demand for Smart Grids (SGs) has dramatically increased in recent times. However, in SGs, a safe environment against cyber threats is also a concern. This paper proposes a novel Fog-based Artificial Intelligence (AI) framework for SG Networks. It uses Machine Learning (ML) and Deep Learning (DL)-based ensemble models to enhance the accuracy of detecting intrusions in SG networks. This work has two main goals, which include addressing class imbalance in network intrusion detection datasets and building interpretable models for targeted security interventions. It is achieved by using ensemble modeling, such as Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbors (KNN) for ML-based ensemble, while the DL ensembles consist of aggregated neural network models trained using TensorFlow. The paper assess their effectiveness in identifying malicious activities in the SG network traffic. The present study utilizes a large dataset that was custom-designed for SG intrusion detection. Most of the previous studies explored different ML techniques using a single dataset; however, the performance improvement by ensemble modeling has not been explored intensively. Therefore, this paper bridges this research gap by suggesting a novel ML-based ensemble model for intrusion detection using two datasets: CIC-IDS-Collection and a specifically designed Power System Intrusion dataset. This study has made benchmark results demonstrating the effectiveness of the proposed ensemble models for intrusion detection in SGs. Results demonstrated better accuracy, precision, recall, and F1 Scores for the proposed ensemble models over the two datasets. The accuracy, precision, recall, and F1 Scores for the proposed Ensemble model 1 for the CIC-IDS Collection dataset are 98.57%, 98.75%, 99.00%, and 98.25% and for the Power System dataset they are 98.75%, 99.05%, 99.20%, and 99.10%, respectively. Similarly, for the proposed Ensemble model 2 for the CIC-IDS Collection dataset, we have 98.84%, 99.00%, 99.00%, and 99.00% accuracy, precision, recall, and F1 Score values. For the Power System dataset, these values are 99.05%, 99.30%, 99.25%, and 99.27% for the mentioned parameters
