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Evaluating HAS and Low-Latency Streaming Algorithms for Enhanced QoE
No full textThe demand for multimedia traffic over the Internet is exponentially growing. HTTP adaptive streaming (HAS) is the leading video delivery system that delivers high-quality video to the end user. The adaptive bitrate (ABR) algorithms running on the HTTP client select the highest feasible video quality by adjusting the quality according to the fluctuating network conditions. Recently, low-latency ABR algorithms have been introduced to reduce the end-to-end latency commonly experienced in HAS. However, a comprehensive study of the low-latency algorithms remains limited. This paper investigates the effectiveness of low-latency streaming algorithms in maintaining a high quality of experience (QoE) while minimizing playback delay. We evaluate these algorithms in the context of both Dynamic Adaptive Streaming over HTTP (DASH) and the Common Media Application Format (CMAF), with a particular focus on the impact of chunked encoding and transfer mechanisms on the QoE. We perform both objective as well as subjective evaluations of low-latency algorithms and compare their performance with traditional DASH-based ABR algorithms across multiple QoE metrics, various network conditions, and diverse content types. The results demonstrate that low-latency algorithms consistently deliver high video quality across various content types and network conditions, whereas the performance of the traditional adaptive bitrate (ABR) algorithms exhibit performance variability under fluctuating network conditions and diverse content characteristics. Although traditional ABR algorithms download higher-quality segments in stable network environments, their effectiveness significantly declines under unstable conditions. Furthermore, the low-latency algorithms maintained high user experience regardless of segment duration. In contrast, the performance of traditional algorithms varied significantly with changes in segment duration. In summary, the results underscore that no single algorithm consistently achieves optimal performance across all experimental conditions. Performance varies depending on network stability, content characteristics, and segment duration, highlighting the need for adaptive strategies that can dynamically respond to varying streaming environments
The Lived Experience of Contemporary Trainee Nursing Associates: The Evolution of the New Role as a Pathway to Nursing. A Phenomenological Study
No full textBackground
In 2016, a new role, the nursing associate, was implemented within the nursing workforce in England to support the shortfall of registered nurses and create a new pathway into nursing. This study aims to explore trainee nursing associates' lived experience to understand if the new role has been accepted and embedded in the nursing workforce.
Design
This study used a qualitative inductive phenomenological design. The Consolidated Criteria for Reporting Qualitative Research checklist (COREQ) was adhered to in developing this paper.
Methods
Semi‐structured focus groups were conducted with participants completing their nursing associate programme in one higher education institute. Four focus groups with 14 participants occurred between June and November 2021. Thematic analysis was completed as described by Braun and Clarke from an inductive phenomenological perspective.
Results
Four themes were identified: (1) new opportunities and knowledge; (2) academic and practice support; (3) pressure within clinical placements; and (4) the need for continued education and training.
Conclusions
The new role has supported widening participation in higher education institutions and an affordable professional nursing pathway. However, challenges, such as a lack of understanding of the nursing associate role, remain in both clinical practice and higher education institutes. The development of the nursing associate role across specialities has commenced, which inevitably causes further confusion.
What is known about this topic?
A new role, the nursing associate, was implemented in England in 2016 to support the shortfall of registered nurses.
Nursing associates are registered with the governing nursing body in England, the Nursing and Midwifery Council.
The implementation of nursing associates has been challenging due to a lack of acceptance and understanding by healthcare professionals and the emerging ambiguity of responsibilities across specialities.
What this paper adds?
The nursing associate role has widened participation in higher education and is an affordable professional nursing pathway.
Challenges remain for trainee nursing associations in both clinical practice and higher education institutions, which need to be addressed.
The development of the nursing associate role across specialities has commenced, which inevitably is causing confusion.
The implications of this paper:
The clinical identity of nursing associates needs to be further developed both across and within specialities.
The responsibilities of nursing associates need to be further developed both across and within specialities
WFL: Edge-Enabled Weighted Federated Learning for Securing Heterogenous Networks
No full textThe operational aspects of the Internet of Things (IoT) are dependent on the security measures deployed to ensure user privacy, protect user data and prevent smart devices from being exploited for malicious activities. Traditional Intrusion Detection Systems often require collaboration from many individual devices in the centralised system for data processing and decision-making. However, centralised systems have some limitations in terms of privacy and scalability. This paper proposes a federated learning-based (FL) distributed framework for detecting and mitigating intrusion while ensuring privacy in IoT networks. The framework integrates two key security components: an intrusion detection module that employs Neural Networks (NN) at the edge device, and centralised aggregation systems that aggregate and coordinate the aggregated model to edge devices. The centralised system computes the global model using a weighted averaging mechanism to accurately represent the relative importance of each device’s local model. of each device’s contribution. This ensures that the global model is the complete representation of the overall data at the collaborating edge nodes. The framework ensures privacy as data remains local to edge devices, and the machine learning models are exchanged to the aggregation server. By supporting heterogeneous data from various sources, the framework demonstrates adaptability to diverse attack patterns and device behaviours. The evaluation is conducted on heterogeneous datasets, including CICIDS2017, UNSW-NB15, and KDD Cup 99 under heterogeneous scenarios, which represent a wide range of intrusion scenarios, such as DDoS, Botnet activities and malicious behaviours. With an increased number of iterations and collaborators, the framework demonstrates improved performance, achieving an average intrusion detection accuracy of 99% across the three datasets. These results highlight the importance of both the number of collaborators and iterations in improving the overall model performance while preserving privacy and minimising communication overhead
The Royal Netherlands Football Association (KNVB) relative age solutions project—part two: an adapted e-Delphi study
No full textIntroduction: Following the lack of widely implemented interventions to mitigate Relative Age Effects (RAEs) in sports, the Royal Netherlands Football Association (KNVB) called on stakeholders to propose relative age solutions in youth soccer (Part One). This initial study yielded 13 lower-order potential solutions, many of which remain hypothetical. Therefore, this study aimed to evaluate these solutions to overcome RAEs in youth soccer using a two-round adapted e-Delphi study.
Methods: Fifteen international experts, including both researchers and practitioners, rated (out of 9) each solution on how likely it is to directly and indirectly mitigate RAEs (Round 1) and how feasible it is to implement (Round 2).
Results: Findings indicated that “rotating cut-off dates” was perceived as the most effective solution to mitigate direct and indirect RAEs (6.2 ± 1.6), although it was not rated particularly feasible (4.6 ± 2.5). In comparison, while “cueing differences in age” was perceived as the most feasible solution (6.7 ± 2.1), it was deemed less useful for mitigating RAEs (5.2 ± 2.3). Taken together, “cueing differences in age” was considered the most viable solution across both rounds (5.8 ± 2.3).
Discussion: Interestingly, highly rated solutions perceived to effectively moderate RAEs were generally expected to be more challenging to implement. Results also showed regular disagreement amongst the international experts, highlighting that creating consensus on possible relative age solutions may be difficult to achieve in youth soccer. Moving forward, the highest rated solutions should be designed, implemented, and evaluated based on their effectiveness and feasibility in practice
Global Burden of Human Metapneumovirus: Bridging Gaps in Prevention, Diagnostics and Treatment
No full textHuman metapneumovirus (hMPV) is a respiratory virus with a significant impact, particularly on the paediatric population, yet it remains an under‐recognized contributor to respiratory infections globally. Recent epidemics in China, India and Europe underscore its clinical burden, with severe cases linked to bronchiolitis, pneumonia and exacerbations of chronic conditions. Despite its impact, hMPV remains underdiagnosed due to limited access to molecular diagnostics and routine surveillance, particularly in low‐ and middle‐income countries. The absence of antivirals or vaccines exacerbates its public health challenge. Advances in multiplex diagnostics and vaccine development offer hope but require sustained global investment. Addressing gaps in prevention, diagnostics and treatment is critical to mitigating hMPV's growing threat and ensuring equitable healthcare outcomes
Assessing the role of education and financial literacy in shaping the success of spaza shops: Case study of Gamalakhe Township, KwaZulu-Natal, South Africa
No full textBackground: This study examined how education influences financial literacy (FL) and subsequent business success among spaza shop owners in Gamalakhe Township, KwaZulu-Natal. Aim: This study aimed to investigate whether the level of education is a significant factor in determining the FL skills needed to run spaza shop businesses by surveying 100 spaza shop owners. Setting: The study setting was Gamalakhe Township, KwaZulu-Natal. Methods: The quantitative data were analysed via correlations and ordinal logistic regression. Results: The findings of descriptive statistics and correlations reveal that education significantly predicts FL levels, which in turn drives improved decision-making and business outcomes. Conclusion: The ordinal logistic regression revealed that education level influences the level of FL. Contribution: The findings emphasise the importance of developing experiential learning and mentorship programmes to address FL gaps among township entrepreneurs
Developing a Smart Wireless Sensor Network for Waste Management in Cities
No full textAs rapid global urbanisation is expected to peak by 2050, the need for smart and sustainable infrastructure to effectively manage and combat urban waste has become increasingly urgent. This study addresses the critical issue of waste management in expanding cities through the proposal of a novel wireless sensor network (WSN) aimed at optimising waste collection and processing by assisting contemporary procedures through the integration of recent advances in networking and unmanned aerial vehicles (UAVs). To investigate the efficacy of our proposals, various assessments and analyses within the Cooja network simulator are conducted. In conjunction with this, a real-life case study of major transport stations within Whitechapel, London is also considered to provide enhanced insight into the network’s effectiveness. The developed model demonstrates the system’s capability of enhancing waste collection services with a minimal average transmission delay of 125 ms experienced across all simulated nodes. Finally, the paper provides all simulation and node source code, encouraging future development and refinement in smart waste management and Internet of Things (IoT) solutions, enabling future integration with artificial intelligence for further optimisations
An intrusion detection model based on Convolutional Kolmogorov-Arnold Networks
No full textThe application of artificial neural networks (ANNs) can be found in numerous fields, including image and speech recognition, natural language processing, and autonomous vehicles. As well, intrusion detection, the subject of this paper, relies heavily on it. Different intrusion detection models have been constructed using ANNs. While ANNs are relatively mature to construct intrusion detection models, some challenges remain. Among the most notorious of these are the bloated models caused by the large number of parameters, and the non-interpretability of the models. Our paper presents Convolutional Kolmogorov-Arnold Networks (CKANs), which are designed to overcome these difficulties and provide an interpretable and accurate intrusion detection model. Kolmogorov-Arnold Networks (KANs) are developed from the Kolmogorov-Arnold representation theorem. Meanwhile, CKAN incorporates a convolutional computational mechanism based on KAN. The model proposed in this paper is constructed by incorporating attention mechanisms into CKAN’s computational logic. The datasets CICIoT2023 and CICIoMT2024 were used for model training and validation. From the results of evaluating the performance indicators of the experiments, the intrusion detection model constructed based on CKANs has an attractive application prospect. As compared with other methods, the model can predict a much higher level of accuracy with significantly fewer parameters. However, it is not superior in terms of memory usage, execution speed and energy consumption
Internet of Things to Internet of Humans: A Perception
No full textAbstract
The use of internet has grown significantly in the last three decades. Various technologies such as social media and networking, cloud systems, Blockchain technology etc., have been developed which have significantly improved the communication between the humans across the globe; and also, between the devices which has led to the development of approaches like the internet of things (IoT). These approaches have been increasingly adopted in various sectors including healthcare, education, retail, financial, and many other sectors. However, the development of the internet and its relevant technologies were mainly focusing on the technical aspects undermining the human-centric aspects which has led to the development of new concept called ‘Internet of Humans (IoH)’. There is a lack of research and understanding relating to the internet of humans, and no clear definition was identified. In this context, this paper tries to explore the concept of the Internet of Humans from different perspectives including advanced and innovative supporting technologies, human-centric factors, and areas of application. Thus, this paper contributes to the development of literature for understanding the internet of humans and proposes future research issues and challenges
