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OCR Exam Reference Language - Blockly:Visual programming environment based on OCR GCSE Computer Science specification
No full textA visual programming environment for OCR GCSE Computer Science's Exam Reference Language (ERL), built with Blockly.Students drag and drop blocks to build programs that mirror OCR ERL syntax. The editor generates live pseudocode, executes programs in the browser, and requires no installationThe editor is not endorsed by OCR. <br/
Profiling Reasons for Non-Attendance in Psychological Assessments in adolescent suicide at-risk group using Natural Language Processing.
No full textSTRIDE: D2.2 Systematic review:Does early childhood education reduce inequalities in educational outcomes for children facing multiple disadvantages: A systematic review on longitudinal and quasi-experimental studies
Full text linkEU AI Act regulation: a study of non-European Union manufacturers' compliance preparedness
No full textPurpose This study investigates the preparedness of manufacturing companies in the UK and Brazil to comply with the European Union's artificial intelligence (AI) Act of 2024. It aims to assess these companies' ability to identify AI-related risks, implement necessary compliance measures and evaluate a newly developed compliance framework designed to enhance regulatory compliance. Design/methodology/approach A mixed-methods approach was adopted. First, 10 AI use case scenarios were identified from the literature related to production processes and products. A survey of 152 members from 87 companies in the UK and Brazil was conducted to gauge baseline readiness. Subsequently, a novel compliance framework was piloted with 11 of these companies. Pre- and post-pilot assessments were analysed to evaluate improvements in risk identification, regulatory knowledge and organisational confidence. Findings The results reveal a significant gap in compliance readiness at baseline and substantial improvements post-intervention. Prior to the pilot, participants on average identified correctly the risk levels in only 40% of scenarios and just 42% demonstrated adequate knowledge of the Act's provisions. After implementing the compliance framework, average risk identification accuracy rose to 86% and regulatory comprehension to 81%, indicating a marked improvement (p &lt; 0.01). Participants' self-reported confidence in managing AI compliance also increased correspondingly. Originality/value This study is among the first to empirically examine AI Act compliance readiness in non-EU manufacturing companies. It provides a novel compliance framework to improve the capacity to manage AI-related regulatory requirements. The study offers valuable insights for manufacturing managers and regulators navigating the interface of technological innovation and regulatory compliance
Investigating the dose-response relationship between music and anxiety reduction: A randomized clinical trial
No full textAnxiety is one of the most frequently reported mental health conditions worldwide, yet access to effective treatments such as medication and cognitive behavioral therapy (CBT) remains limited due to cost, time, and potential side effects. Music-based digital therapeutics, particularly when combined with auditory beat stimulation (ABS), may offer a complementary approach to mainline anxiety treatment by offering acute relief of anxiety symptoms. Prior research suggests that music combined with ABS provides greater anxiety relief than music alone or a pink noise control. This study examined whether this advantage over pink noise could be replicated, as well as whether music with ABS demonstrated a dose-response relationship-operationalized as time spent listening-in the acute relief of anxiety among individuals with moderate trait anxiety who are taking medication to manage their symptoms. We also assessed changes in affect as a secondary outcome. A total of 1,310 participants were recruited via Prolific and completed a pre-screening survey. Of these, 144 eligible participants were randomly assigned to one of four groups: 24-minute pink noise (control group), 12-minute music with ABS, 24-minute music with ABS, or 36-minute music with ABS. Anxiety and affect were measured before and after the intervention using the STICSA and PANAS, respectively. All music with ABS conditions resulted in greater reductions in anxiety and negative affect compared to the control, replicating earlier findings. The largest reduction in negative affect was observed in the 36-minute condition, which was significantly greater than reduction in the 12-minute condition, suggesting a dose-response effect. These findings support music with ABS as a possible addition to existing anxiety treatments, especially when access to common behavioral health interventions is limited. Future studies should aim to increase the generalizability of the findings and further investigate the dose-effect of music on anxiety reduction. This study was retrospectively registered on ISRCTN (ISRCTN47181782). [Abstract copyright: Copyright: © 2026 Mullen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Energy-efficient threat detection in IoT healthcare using AI and blockchain-enhanced fog–cloud architecture
Full text linkIntrusion detection in Internet of Things (IoT) networks, particularly in healthcare settings, poses critical challenges due to latency constraints, limited resources, and the need for trustworthy auditing in distributed environments. Centralized detection models often fail to deliver timely or scalable responses under real-world IoT conditions. This study proposes a hybrid fog–cloud architecture tailored for healthcare-oriented IoT threat detection, incorporating blockchain-based auditability. The architecture utilizes fog- and cloud-level XGBoost classifiers trained on BoT-IoT and ToN-IoT datasets, with SMOTE applied to mitigate class imbalance. A lightweight blockchain module is integrated at the fog layer to log predictions in real-time for tamper-evident traceability. Simulations were performed using 50 fog-predicted events to evaluate performance, energy usage, and blockchain entropy. The system achieved an average block creation time of under 20 ms with minimal CPU and memory overhead. It also demonstrated robustness against tampering, preserving data integrity. The fog-level model achieved competitive metrics (AUC = 1, F1-score = 98.70%, Accuracy = 99.80%) compared to the cloud model, while outperforming it in terms of response latency and localized decision-making. The proposed blockchain-integrated fog–cloud framework enables secure, low-latency, and scalable threat detection for healthcare IoT systems, offering a promising foundation for privacy-aware edge intelligence
The ‘forum effect’: how does participation in online forums affect student retention and academic performance at a distance learning university?
No full textHeterogeneous Graph Neural Networks for Fraud Detection and Explanation in Supply Chain Finance
Full text linkIt is a critical mission for financial service providers to discover fraudulent borrowers in a supply chain. The borrowers’ transactions in anongoing business are inspected to support the providers’ decision on whether to lend the money. Considering multiple participants in a supply chain business, the borrowers may use sophisticated tricks to cheat, making fraud detection challenging. In this work, we propose a multitask learning framework, MultiFraud, for complex fraud detection with reasonable explanation. The heterogeneous information from multi-view around the entities is leveraged in the detection framework based on heterogeneous graph neural networks. MultiFraud enables multiple domains to share embeddings and enhance modeling capabilities for fraud detection. The developed explainer provides comprehensive explanations across multiple graphs. Experimental results on five datasets demonstrate the framework’s effectiveness in fraud detection and explanation across domains.© 2023, Elsevier. This is an author produced version of a paper published in Information Systems uploaded in accordance with the publisher’s self- archiving policy. The final published version (version of record) 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
