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.
Unmanned Aerial Vehicles (UAVs) have become essential tools for disaster monitoring and response due to their quick deployment and high-resolution sensing capabilities. However, collecting and sharing UAV data pose significant risks to location privacy, which can threaten both operational security and sensitive information. This study introduce a novel federated learning (FL) framework which combined with multi-hop mix networks and adaptive differential privacy, to safe UAV location data during disaster detection. The framework enables UAVs to collaboratively build a global model without sharing raw data, thereby protecting sensitive positional information from potential adversaries. Simulation shows that the proposed method can reach up to 95% accuracy in the global model, even with highly diverse UAV data, surpassing benchmark results by 5%-12%. The loss function converges from 0.82 to 0.12 over 50 epochs, achieving faster convergence by 20%-40% compared to traditional methods. The adversary's error in estimating position increases to 0.9 under the proposed privacy computation, which shows a 5%-10% improvement. Additionally, model accuracy remains stable across various noise distributions, maintaining 2%-6% higher accuracy. These results show that the proposed technique effectively balances location privacy, model performance and convergence efficiency.
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.
The widespread availability of the Internet and the growing use of smart devices have fueled the rapid expansion of multimodal (image-text) sentiment analysis (MSA), a burgeoning research field. This growth is driven by the massive volume of image-text data generated by these technologies. However, MSA faces significant challenges, notably the misalignment between images and text, where an image may carry multiple interpretations or contradict its paired text. In addition, short textual content often lacks sufficient context, complicating sentiment prediction. These issues are particularly acute in low-resource languages, where annotated image-text corpora are scarce, and Vision-Language Models (VLMs) and Large Language Models (LLMs) exhibit limited performance. This research introduces MulMoSenT, a multimodal image-text sentiment analysis system tailored to tackle these challenges for low-resource languages. The development of MulMoSenT unfolds across four key phases: corpus development, baseline model evaluation and selection, hyperparameter adaptation, and model fine-tuning and inference. The proposed MulMoSenT model achieves a peak accuracy of 84.90%, surpassing all baseline models. Delivers a 37. 83% improvement over VLMs, a 35.28% gain over image-only models, and a 0.71% enhancement over text-only models. Both the dataset and the solution are publicly accessible at: https://github.com/sadia-afroze/MulMoSenT.
