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A trustworthy multiparty authentication architecture for IIoT leveraging IOTA integration
Get PDFThe rapid adoption of developing technologies, particularly the Industrial Internet of Things (IIoT), has raised significant security and privacy concerns. IIoT seeks to enhance industrial operations through the integration of specialised devices, primarily sensors, within the industrial environment. These sensors continuously monitor multiple processes while providing essential data to maintain proper functionality. However, safeguarding IIoT systems is especially complex because of the variety of devices, makers, and networks; the need for collaboration across several security domains; and the constraints of resource- constrained devices. Conventional security systems struggle to adapt, especially when users and services interact dynamically across several IoT networks. To address these issues, this research introduces a novel multi-party authentication architecture that enables secure and dynamic communication among participants from different security groups. The design enables the secure exchange of shared secrets for session authentication while protecting security credentials during resource access. One significant advance is the integration of IOTA distributed ledger technology (DLT) alongside IoT systems to enable multi-party authentication. Specifically, the IOTA Streams protocol is used to enable structured, secure, and scalable data sharing while maintaining data integrity, privacy, and authenticated access. The NuSMV model checker is employed to verify the effectiveness as well as security of the proposed authentication approach. It ensures fulfilment of security requirements and operational functionality
Current research development on food contaminants, future risks, regulatory regime and detection technologies: a systematic literature review
Get PDFFood contaminants pose serious threats to public health, with profound negative impacts on the economy, society, and environment. However, there is a lack of timely and comprehensive reviews on the latest developments in food contaminants and effective measures to prevent contamination, particularly through novel intelligent detection technologies and regulatory regimes. This study addresses this knowledge gap by presenting a timely review of the literature, focusing on current types of food contaminants, advances in detection technologies, emerging risks, and the latest developments in regulatory frameworks. The study reviewed 116 relevant articles published between 2019 and 2024 and conducted a thematic analysis. The food contaminants were classified into three categories: biological, chemical, and physical. The study identified six key drivers of current and future food safety risks: demographic change, economic factors, environmental conditions, geopolitical shifts, consumer priorities, and technological advancements. Findings reveal the uneven understanding of contaminants of emerging concern, future drivers of contaminants of emerging concern, and their impact on the food system, the environment, and human health. These findings highlight the need for future research on systematically identifying and validating the regional differences in food contamination prevention measures and assessing the extent to which these differences impact the effectiveness of prevention, mitigation, and control efforts. The findings also call for more international cooperation in food contamination research and the active involvement of technology partners to facilitate the application of cutting-edge technologies in food contamination detection and control
A conditional GAN and dual-channel hybrid deep feature framework for robust sensor fault detection in WSNs
Get PDFSensor-generated data is vital to the operation of numerous systems and services in the rapidly growing field of the Internet of Things. Wireless Sensor Networks, as an essential setup for these systems, are frequently deployed in large, diverse, and often harsh environments. However, these networks are highly vulnerable to various faults, potentially leading to improper data transmission, reliability, and financial stability of the systems. To address these challenges, we propose a hybrid model for sensor fault detection that integrates a machine learning classifier with the deep learning (DL) model, specifically VGG-16 and ResNet-50. Synthetic samples are generated using a Conditional Generative Adversarial Network and common sensor faults, such as hardover, drift, spike, erratic, and stuck fault are introduced by leveraging a publicly available temperature sensor dataset. Time-series data is transformed into Gramian Angular Field images, from which deep features are extracted using VGG-16 and ResNet-50. These extracted features are then fused to form a hybrid feature pool. Our framework effectively addresses problems related to data imbalance and enhances accuracy. The proposed model outperforms the individual feature sets, VGG-16 (89.22%) and ResNet-50 (84.21%), achieving notable accuracy of 92.55% with the fused feature set, underscoring its potential for robust sensor fault detection
Implementing evidence-based practice in critical care nursing: an ethnographic case study of knowledge use
Get PDFAim: To explore how critical care nurses access, negotiate and apply knowledge in high-pressure clinical environments, focusing on organisational, cultural and leadership factors influencing evidence-based practice implementation in acute hospital settings. Design: A focused ethnographic collective case study was conducted across two contrasting critical care units in England. Methods: Methods included non-participant observation (56 sessions), semi-structured interviews (36 participants) and document review. Spradley's Developmental Research Sequence guided data generation and analysis. Data were collected over an eight-month period (February to September 2022). Findings: Five major themes were identified: sources of knowledge and acquisition strategies; institutional and hierarchical influences on knowledge use; role of experiential knowledge and clinical intuition; challenges to evidence-based practice implementation; and strategies for integrating knowledge into practice. Organisational structures, leadership engagement, mentorship and access to updated digital resources were key enablers of evidence-based practice. Barriers included workload pressures, inconsistent guideline dissemination and hierarchical cultures. Adaptive blending of formal evidence, clinical experience and intuition characterised effective knowledge negotiation at the bedside. Conclusion: Knowledge use in critical care nursing is a dynamic, relational process shaped by leadership, organisational culture and systemic pressures. The availability of evidence alone is insufficient; visible leadership, peer learning, protected educational time and valuing of experiential knowledge are critical to embedding evidence-based practice into routine practice. Implications for Patient Care: Strengthening organisational systems, investing in nurse manager development, expanding simulation-based learning and legitimising experiential knowledge are vital strategies to enhance evidence-based critical care. Impact: This study provides actionable insights for healthcare leaders, educators and policymakers seeking to optimise evidence-based practice adoption in high-acuity clinical environments and improve patient outcomes. Reporting Method: The Consolidated Criteria for Reporting Qualitative Research checklist guided reporting. No Patient or Public Involvement: Patients and the public were not involved in the design, conduct, reporting or dissemination of this research.</p
'You're talking about all these things you're doing … it is just seen as the norm': exploring young people's perspectives on disclosing their own use of harmful behaviour
Get PDFExisting research into adolescent dating abuse demonstrates that young people experiencing harm are most likely to confide in their friends, but far less is known about the disclosure habits of young people instigating harm. This paper explores the findings from a mixed-methods study relating to whom young people would speak to about their own use of behaviour they describe as harmful and why. Data were collected through a mixed-methods survey responded to by 749 young people aged 11–25, and through semistructured interviews with 11 young people aged 17–23. Analysis of this data identified closest friends as the group young people felt most likely to confide in, mirroring the literature on young victims, followed by their partners/the person they are seeing and a therapist/counsellor. Findings showed these individuals were chosen due to the young person feeling comfortable talking to them about this topic, believing their disclosure would remain confidential and feeling they would offer a nonjudgemental response. This paper outlines the need for a systemic response to abuse in young people's relationships, which is centred around improved relationship literacy for young people themselves and across society. It also highlights some practical implications focused on ensuring young people have adequate support and guidance in place to navigate early romantic/dating relationships. These findings offer some direction for where to focus resources and support, as well as some guidance around approaches that may encourage disclosures from young people instigating harm
‘I AM IMPORTANT’: reflections from young people in Kenya and Uganda on the value of participation for children affected by sexual abuse and exploitation
Get PDFThe United Nations Convention on the Rights of the Child gives children a fundamental right to participate in all decisions that affect them. ‘Participation’ is now a common ‘good practice’ principle when supporting, and working with, children and young people. However, practitioners are often unsure of how to facilitate the safe and meaningful collective participation of children and young people with lived experience of sexual abuse and exploitation (bringing them together in groups to inform and influence decision-making or actions that affect them as a specific group). Research indicates that there are myriad potential benefits when young people engage in safe and meaningful participatory processes. This Practice Perspective shares details of a participatory project we initiated with young people who had lived experience of child sexual abuse and exploitation and who had previously engaged in participatory initiatives. It shares their perspectives on the potential benefits of participatory practice for young people and the wider community. 19 young people, aged 17-25, in Kenya and Uganda took part in a series of workshops culminating in the development of a podcast to share their views on the topic
Large vision language model: enhanced-RSCLIP with exemplar-image prompting for uncommon object detection in satellite imagery
Get PDFLarge Vision Language Models (LVLMs) have shown promise in remote sensing applications, yet struggle with “uncommon” objects that lack sufficient public labeled data. This paper presents Enhanced-RSCLIP, a novel dual-prompt architecture that combines text prompting with exemplar-image processing for cattle herd detection in satellite imagery. Our approach introduces a key innovation where an exemplar-image preprocessing module using crop-based or attention-based algorithms extracts focused object features which are fed as a dual stream to a contrastive learning framework that fuses textual descriptions with visual exemplar embeddings. We evaluated our method on a custom dataset of 260 satellite images across UK and Nigerian regions. Enhanced-RSCLIP with crop-based exemplar processing achieved 72% accuracy in cattle detection and 56.2% overall accuracy on cross-domain transfer tasks, significantly outperforming text-only CLIP (31% overall accuracy). The dual-prompt architecture enables effective few-shot learning and cross-regional transfer from data-rich (UK) to data-sparse (Nigeria) environments, demonstrating a 41% improvement over baseline approaches for uncommon object detection in satellite imagery
Collaborating with schools for public health research in England: lessons learned for successful partnerships
Get PDFCarrying out health research with schools can be both challenging and highly rewarding. Here we describe lessons learned from a research partnership lasting over 5 years, initially with 84 primary schools in London and Luton, and extended to 35 secondary schools, during our children health cohort study. This period included school closures and societal disruption during the COVID-19 pandemic, creating additional challenges to ongoing school participation. Our study involved annual health assessment visits to schools to test over 3000 participants and parental self-report questionnaires, to assess the potential benefits of air quality improvements arising from London Ultra Low Emission Zone (introduced in April 2019) on children’s lung development and health. Measures included height, weight, pre- and post- bronchodilator spirometry, physical activity monitoring, cognitive assessment, epigenetic markers of disease risk, SARS-CoV-2 IgE and IgM antibody testing, and heavy metals testing. The average annual participant attrition for our study was 11.6%. The acceptable threshold outlined in the initial protocol was 20%. All schools continued to participate in the study for 5 years. Central to the study success have been: shared agreement on the importance of the research topic; early preparatory work with stakeholders, a parallel engaging and innovative air pollution learning and outreach programme, incentivising school/teacher co-operation and parental questionnaire completion to boost response rates and mitigate non-response bias; and continuity of contact with the accessible and flexible research team. These successes form a template for other health research studies planning long-term engagement with schools
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