Ulster University (Irish: Ollscoil Uladh, Ulster Scots: Ulstèr Universitie or Ulstèr Varsitie), legally the University of Ulster, is a multi-campus public university located in Northern Ireland. It is often referred to informally and unofficially as Ulster, or by the abbreviation UU. It is the largest university in Northern Ireland and the second-largest university on the island of Ireland, after the federal National University of Ireland. Established in 1968 as the New University of Ulster, it merged with Ulster Polytechnic in 1984, incorporating its four Northern Irish campuses under the University of Ulster banner. The university incorporated its four campuses in 1984; located in Belfast, Coleraine, Derry (Magee College), and Jordanstown. The university has branch campuses in both London and Birmingham, and an extensive distance learning provision. The university rebranded as Ulster University from October 2014 and this included a revised visual identity. It has one of the highest further study and employment rates in the UK, with over 92% of graduates being in work or further study six months after graduation. The university is a member of the Association of Commonwealth Universities, the European University Association, Universities Ireland and Universities UK.
Pupils need to develop information literacy (IL) skills in schools in order to be active members of a skilled workforce, for lifelong learning and digital citizenship. However, there has been little focus on the extent to which this happens in a classroom setting and on information competencies of teachers. As part of a broader study of teachers’ knowledge and perceptions of IL, librarians in schools in Northern Ireland were interviewed. Findings reveal low levels of collaboration with teachers. Recommendations are made regarding how to overcome challenges involved in developing teachers’ IL so that they can better support learners.
The integration of reconfigurable intelligent surfaces (RISs) and non-orthogonal multiple access (NOMA) is considered a promising technique to enhance spectral efficiency and connectivity in future 6G networks. Accurate channel estimation remains a critical challenge in RIS-NOMA systems due to the increased complexity introduced by the combination of RIS and NOMA technologies. While quantum machine learning (QML) has demonstrated potential in wireless communications, its application in channel estimation remains underexplored. This paper investigates the effectiveness of a hybrid quantum-classical machine learning (ML) model for channel estimation in RIS-NOMA systems. We propose a hybrid architecture that integrates convolutional neural networks (CNNs) with quantum long short-term memory (QLSTM) networks, where CNNs perform spatial feature extraction while QLSTMs capture temporal dependencies in the time-varying channel. Extensive simulations are conducted to evaluate the performance of the model under various network configurations, considering different power allocation factors, the number of RIS elements, and signal-to-noise ratios (SNRs). The performance of the proposed model is benchmarked against both pure quantum and classical ML models, including a quantum neural network (QNN), a CNN, a long short-term memory (LSTM) model, a bidirectional LSTM (BiLSTM) model, and a CNN-LSTM model. The results demonstrate that the proposed CNN-QLSTM model outperforms all baseline methods in terms of root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). These findings highlight the potential of quantum-enhanced ML for channel estimation in next-generation communication networks.
Public Inquiry is an established process for effective testing and scrutiny of plans in terrestrial planning and is regarded as a means of providing credibility and accountability. Independent Investigation is its marine equivalent and was included as a provision in the UK marine planning regime and subsequent legislation since its inception. However, it has been noticeably absent in practice. This paper investigates the reasons behind this situation within the context of the proposed and actual role of II in the marine planning process in the UK. It additionally considers the future use of II in enhancing the quality and effectiveness of the marine planning system. This paper concludes that as the use of the marine resource of the UK becomes increasingly contested and controversial, II could be utilized to enhance marine plans and marine planning decisions and thus warrants further investigation.
In industrial product manufacturing, the automated detection and localisation of surface defects are of significant importance for ensuring quality control. However, existing computer vision-based defect detection methods struggle to achieve both lightweight design and high accuracy on resource-constrained embedded platforms, which limits their application in practical industrial detection environments. To address this issue, we propose DSGNet, a lightweight surface defect segmentation model, which serves as a core defect detection and localisation method for industrial inspection systems. The proposed model adopts an asymmetric encoder-decoder structure to simplify the overall architecture. We designed an efficient feature extraction network by using four lightweight feature extraction units based on efficient convolutions. Furthermore, we introduce a hierarchical adaptive upsampling fusion (HAFU) mechanism and a lightweight bidirectional multiscale strip attention (LBMSA) feature refinement module to effectively fuse and refine the multilevel features extracted from the encoder. We conducted comprehensive evaluations of DSGNet on three typical surface defect datasets: Neu-Seg, MSD and MT. While maintaining an extremely low complexity with only 0.49 M parameters, DSGNet achieved impressive mIoU scores of 83.39%, 91.61% and 80.72% on three datasets, respectively. These results indicate that DSGNet is a promising solution that balances lightweight design and detection accuracy for industrial real-time detection systems, demonstrating strong potential for practical deployment. Our code is available at https://github.com/young-zyy/DSGNet.
Integrating AI inference into wireless sensing edge networks presents notable challenges due to limited resources, changing environments, and diverse devices. In this study, we proposed a novel resource allocation framework that enhances energy efficiency, reduces latency, and ensures fairness across distributed edge nodes for AI inference. The framework models a multi-objective optimization problem that reflects the interdependence of computation, communication, and energy at each device. We also develop a decentralized algorithm based on dual decomposition and projected gradient ascent, by using local data. The extensive simulations demonstrate that our proposed method reduces the average inference latency by 31.4% and energy consumption by 27.8% compared to the greedy and round-robin techniques. The system utility is improved by up to 59.2%, and fairness, measured using Jain’s index, remains within 8% of the ideal. Additionally, throughput analysis further confirms that our approach gains up to 49 tasks/sec, outperforming existing strategies by more than 40%. These findings show that the resource-aware AI inference approach is scalable, energy-efficient, and appropriate for real-time use in multi-user wireless edge networks.
