7 research outputs found
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Bridging the BAME Divide: Unveiling the Impacts of Covid-19 on Ethnic Minority Students and Empowering ChangeâA Case Study at the Open University
This study investigates the evolving impact of COVID-19 on the learning experiences and study performance of ethnic minority students enrolled in Level 1 Computing modules at the Open University. A mixed-methods approach combining quantitative data analysis, literature review, and two focus groups was employed to provide fresh insights. Findings from the literature and focus groups highlight persistent challenges faced by ethnic minority students, including economic disadvantage, digital divide, housing instability, employment difficulties, family responsibilities, mental health issues, racism, discrimination, and unconscious bias. Importantly, this study reveals the dynamic nature of these challenges, illustrating how they have evolved throughout the ongoing pandemic. The study underscores the pivotal role of structural and institutional factors in shaping studentsâ ever-changing experiences. In response to these dynamic challenges, recommendations include targeted interventions, policy revisions that reflect the shifting landscape, innovative community-building initiatives, a renewed focus on diversity promotion, enhanced support services, unconscious bias training, and revised tuition strategies. Addressing these dynamic challenges is crucial for fostering equitable educational opportunities and outcomes for ethnic minority students. This research significantly contributes to promoting equality, inclusivity, and a more comprehensive understanding of the ever-evolving experiences of ethnic minority students during the pandemic and beyond
A survey of three-dimensional turbo codes and recent performance enhancements
This paper presents a survey of two techniques intended for improving the performance of conventional turbo codes (TCs). The first part of this work is dedicated to explore a hybrid concatenation structure combining both parallel and serial concatenation based on a three-dimensional (3D) code. The 3D structure, recently introduced by Berrou et al., is able to ensure large asymptotic gains at very low error rates at the expense of an increase in complexity and a loss in the convergence threshold. In order to reduce the loss in the convergence threshold, the authors consider first a time-varying construction of the post-encoded parity. Then, they investigate the association of the 3D TC with high-order modulations according to the bit-interleaved coded modulation approach. The second part of this study deals with irregular TCs. In contrast to 3D TCs, although irregular TCs can achieve performance closer to capacity, their asymptotic performance is very poor. Therefore, the authors propose irregular turbo coding schemes with suitable interleavers in order to improve their distance properties. Finally, a modified encoding procedure, inspired from the 3D TC, makes it possible to obtain irregular TCs which perform better than the corresponding regular codes in both the waterfall and the error floor regions
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Understanding the Acceptance of Artificial Intelligence in Primary Care
AI has made significant advancements in healthcare, yet its applications are limited to secondary care, with little evidence of its use in primary care. Trust has been identified as a significant factor affecting AI usage, but it does not entirely explain why AI is deployed in some NHS sectors and not others. Organizational infrastructure may also contribute to the lack of AI use in primary care.
Macro level stakeholders such as government bodies and health trusts have expressed interest in integrating AI, allocating resources, and providing training for employees to encourage trust and acceptance of AI. Conversely, at the micro-level stakeholders such as general practitioners and patients, have identified factors such as fairness, accountability, transparency, and ethics as having an impact on trust in AI.
Despite their potential influence, meso-level stakeholders such as managers and IT experts have been largely overlooked in AI research. Investigating their perspectives on trust and relationships across organizational levels is crucial for successful implementation of AI in primary care.
We propose a mixed-methods study design based on a conceptual framework that combines the Technology Acceptance Model-3, Unified Theory of Acceptance and Use of Technology-2, and trust attributes. By combining these models, we aim to gain a better understanding of how stakeholders perceive AI both individually and across organisational levels. Using the proposed model, we present our early findings on the enablers and barriers to AI acceptance in UK primary care. Finally, we discuss future directions on how to overcome the identified barriers
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Design of suitable permutations for irregular turbo codes
Proposed is a method based on the Dijkstra's algorithm and an estimation of the distance to search for efficient interleavers, adapted to irregular turbo codes, in order to reduce the flattening effect. Simulations show that a gain of three decades and a half in the error floor is obtained for short block sizes
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Improving irregular turbo codes
The influence of the degree profile on the performance of an irregular turbo code (TC) is analysed using hierarchical EXtrinsic Information Transfer (EXIT) charts. Then, an irregular TC concatenated with a post-encoder of unity rate at its output is proposed to improve the distance properties. Through the example of 3GPP2 interleaving, the gain at low but also at high signal-to-noise ratios is illustrated