Deep learning driven radiographic classification of primary bone tumors using attention augmented hybrid models

Accurate classification of primary bone tumors is necessary for timely diagnosis and effective treatment planning, particularly given the complex radiographic heterogeneity exhibited by tumor subtypes. The present study introduces two novel deep learning models, including a Convolutional Neural Network Transformer (CNNT) hybrid and a Residual Network 50 (ResNet50) model, augmented by a Convolutional Block Attention Module (CBAM) to enhance feature discrimination and contextual understanding in radiographic images. The models are trained and validated on the Bone Tumor X-ray Radiograph Dataset (BTXRD) dataset of 3,746 labeled radiographs containing nine tumor subtypes. To counter the effects of noise and class imbalance, advanced preprocessing methods like Block Matching 3D Filtering (BM3D) and data balancing using the Synthetic Minority Over sampling Technique (SMOTE) are employed. Extensive testing demonstrates that our approaches outperform current state of the art models, such as ResNet50, EfficientNet version B3 (EfficientNet-b3), You Only Look Once version 8 classification (YOLOv8s-cls), and Deep Supervision Network (DS-Net). Specifically, the ResNet50-CBAM architecture achieves an F1-score of 0.9759, an AUC-ROC score of 0.984, mean accuracy of CBAM 97.41% and a Cohen's Kappa score of 0.9718, outperforming existing benchmarks for binary tumor classification. The CNNT model also achieves competitive performance, reaching an F1-score of 0.9595 with an accuracy of 92.56%. Incorporating attention mechanisms and dataset guided preprocessing renders this framework appropriate for practical clinical settings. The findings of this research have significant implications for the healthcare sector by introducing a scalable, interpretable, and highly accurate Artificial Intelligence (AI) based diagnostic system that can support radiologists in timely diagnoses and decision making processes, ultimately contributing to better patient outcomes and alleviating the diagnostic burden in musculoskeletal oncology.</p

Single transmission phase- and frequency-modulated coded excitation for enhanced inspection of thick complex industrial components using a scalable, flexible, lead-free, ultrasonic array

To address the growing challenge of applying ultrasonic non-destructive evaluation to complex industrial components, flexible ultrasonic arrays have emerged as a conformable solution to inspect such geometries, thereby removing the need for custom-designed wedges to conform to surfaces. Flexible lead-based arrays have been used in prior research. They offer high piezoelectric coefficient, however, they pose human health and environmental risks, and fail to comply with global initiatives including the Restriction of Hazardous Substances (RoHS) regulation enacted by the European Union. Although numerous studies on the piezoelectric properties of lead-free materials have been conducted, the uptake of technology and implementation in practice has been slow. In this work, a scalable, RoHS-compliant, flexible ultrasonic array was employed to improve operability in thick convex and concave components. However, the lead-free array exhibits lower piezoelectric coefficient compared to its lead-based counterparts, resulting in reduced signal quality. To tackle this shortcoming, single transmission phase-modulated Barker and frequency-modulated chirp excitation schemes, in conjunction with pulse compression, were employed to improve the signal quality. Subsequently, their impact was studied in terms of imaging quality, through Full Matrix Capture (FMC) acquisition methodology and Total Focusing Method (TFM) imaging, and Signal-to-Noise Ratio (SNR) measurements. A novel SNR method was presented. Existing SNR approaches evaluate image quality by calculating it within a designated area surrounding the target, where the noise level is quantified as the root mean square of the image noise, omitting any indication of the target. In addition to the noise level, artifacts from matched filters and sidelobes require quantitative evaluation. The new SNR technique was proposed to automate the selection of regions when characterising the SNR. The SNR was calculated across regions of varying size, with the region size where the SNR values converged being selected. This technique was utilised in a comparative analysis including a single-cycle pulse excitation, modulated Barker and chirp excitation schemes with equivalent energy levels in simulation and experimentally. The simulated and experimental results showed good agreement, with some discrepancies attributed to imperfections in the experimental conditions. SNR improvement exceeding 2.6 dB was observed experimentally, with the coded excitation techniques showing higher SNR and better image quality without sacrificing acquisition speed. Moreover, sidelobe artifacts were evident in all TFM images, while the coded excitation images further exhibited matched filter processing artifacts. The flexibility of the array was assessed in the subsequent two experiments to determine its effectiveness in improving operability in complex-geometry samples. The convex and concave samples pre-aligned the array to promote a converging and diverging ultrasonic beam, respectively. In all cases, the array demonstrated excellent conformity with the components, and the coded excitation schemes consistently achieved better imaging quality relative to the pulse excitation case

Evaluating the potential of solar PV to reduce energy costs in fuel poor households

Energy costs represent a significant proportion of household incomes and contribute to fuel poverty. Energy demand also contributes to carbon emissions which must decrease to meet net zero targets. Low Carbon Technologies (LCTs), such as solar photovoltaics (PV), offer opportunities to reduce household energy costs and emissions. However, many LCTs have strict spatial requirements that must be considered to identify buildings that can maximise benefits. This study used bespoke and transferable high-resolution solar PV models alongside government retrofit data to assess solar PV’s potential to reduce energy costs in a sample of fuel poor homes. Results showed that households can reduce electricity expenditure by an average of 26% and a maximum of over 41%. Pen Portraits revealed that savings could reduce household expenditure by up to 43%, thus reducing risk of fuel poverty. When combined with energy efficiency measures already installed, solar PV can reduce household expenditure and carbon emissions. Transferable spatial approaches can be used to lower technical barriers to adoption of LCTs while also proposing areas to promote sustainable practices and behaviours. Spatially targeted policies can then be used to allocate budgets, equipment, information and support structures to maximise self-consumption and adoption of renewable technologies

Domestic Cooking and Food Behaviours during the COVID-19 pandemic and the Cost-of-Living Crisis: A Scoping Review

This scoping review examined how the COVID-19 pandemic and cost-of-living crisis have influenced domestic cooking and food-related behaviours. Following PRISMA-ScR, a systematic search across five databases and grey literature sources identified 4955 records. After screening, 98 studies published between 2020 and 2024 were included. Most studies were conducted in the UK (22.4 %) and USA (18.4 %) and employed cross-sectional (94.9 %) and quantitative (73.5 %) methods. The review identified widespread increases in home cooking, with 50–78 % of participants reporting greater cooking frequency. Changes in food shopping were also prominent, including reduced in-person visits (reported by 40–74 % of participants) and increased online grocery use (25–61.8 %). Budgeting behaviours adapted to financial constraints, with many households reducing the quality and quantity of food purchased, substituting fresh with shelf-stable options. Improvements in hand hygiene were widely reported (74–90 %); however, unsafe practices such as consuming expired foods or mishandling leftovers, remained common. Only 4.1 % of studies received a positive quality rating, with frequent use of non-validated tools and self-reported measures. Future research should employ longitudinal designs to assess the sustainability of these behaviours. Structural policy actions are needed to ensure access to affordable, nutritious foods and support sustainable food practices during ongoing economic challenges

Continual learning with a predictive coding based classifier

Continual Learning (CL) is the problem of learning multiple tasks sequentially. Several effective CL algorithms using Deep Neural Networks (DNNs) have been developed. However, the problem of reducing the computational requirements of the CL algorithms has not received enough attention. Computationally efficient training methods are important for CL because these models potentially undergo training throughout their lifetime. There is a need to build efficient CL methods that can be used on a broad range of devices. Predictive Coding (PC) is a hypothesis about information processing in the brain. The underlying principle is that the PC model predicts the activity of adjacent layers and updates the model in parallel using local errors between predicted and actual neuron activities, potentially improving the efficiency of CL. This paper proposes a new Continual Learning method using a Predictive Coding based Classifier (CLPC2). CLPC2 trains a PC-based classifier with replay samples generated using a Variational Autoencoder (VAE) or Diffusion (Dif). The performance of CLPC2 is evaluated in three CL scenarios: Class Incremental Learning (Class-IL), Domain Incremental Learning (Domain-IL) and Task Incremental Learning (Task-IL) using the split MNIST, CIFAR-10, and CIFAR-100 datasets. Compared with existing CL methods, CLPC2 achieves higher average classification accuracy in Class-IL and Domain-IL scenarios on MNIST and CIFAR-10 datasets, while obtaining comparable performance on the more challenging CIFAR-100 dataset. The key advantage of the proposed method is the ability to perform training in the classifier using locally computed errors

Chaos on a lattice: A systematic investigation of coupled map lattice dynamical systems using statistical metrics

This paper contains a large scale systematic survey of coupled map lattice systems, a broad class of dynamical systems. For this study, over 3500 systems were simulated and 11 metrics were computed to describe the behaviour of each system. To the authors’ knowledge, this is the largest-scale study of these types of systems. Four individual investigations were carried out, into the presence of multiple chaotic attractors in systems, the effect of observed dimension choice, the effect of changing the ordering of one-dimensional maps and a large scale survey to identify trends and correlations across systems. The frequency at which systems contain multiple chaotic attractors is estimated to be between 1%–5%. It is also shown that the connectivity of the lattice sites is negatively correlated with the presence of chaos, and also with metrics calculated from the full Lyapunov spectrum, such as the Kaplan–Yorke dimension. The effects of changing the observed dimension is shown to be significant in systems which contain more than one type of one-dimensional map, with substantial variance observed in 50% of systems for some metrics. Map ordering is also found to impact the behaviour of systems in 10%–20% of the systems investigated. The full dataset containing all simulated systems and their computed metrics is made freely available

Artwork SetoMonogatari 9 - From Sunderand to Seto selected for The Graduates exhibition, National Glass Centre, University of Sunderland, 31 Jan-31 July 2026.

This piece is part of an ongoing series informed by art-archaeological research undertaken at abandoned manufacturing sites in Seto, Japan – once the centre of Japan’s post-war ceramic figurine industry. SetoMonogatari is a portmanteau formed from setomono, the historical term for pottery made in Seto, and monogatari, meaning story. Through a process of bricolage, this work explores material, memory and place, reflecting upon this fragile industrial heritage. It also embodies the artist’s own journey and development as a ceramicist over the last 15 years

Perspectives on artificial intelligence use in pharmacy education in Northern Ireland: A qualitative study based on the unified theory of acceptance and use of technology

INTRODUCTION: Artificial intelligence (AI) is transforming healthcare education; however, its integration into pharmacy curricula requires further exploration. This study aimed to explore the acceptance of AI-based technology in pharmacy education through the extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework and identify opportunities for responsible integration via improved AI literacy.METHODS: A qualitative study was conducted at the School of Pharmacy and Pharmaceutical Sciences, Ulster University, Northern Ireland, United Kingdom, in April 2025. Eight MPharm students were recruited through purposive sampling, with two participants from each of the four academic years (Years 1-4) to ensure representation of developmental perspectives. Informed by the extended UTAUT framework, semi-structured interviews were conducted via Microsoft Teams. Thematic analysis was conducted using Braun and Clarke's six-phase approach, with data management performed using NVivo 13.RESULTS: Four major themes emerged from the analysis: (1) Current Understanding and Experience with AI Tools, (2) Perceived Benefits and Opportunities, (3) Challenges and Concerns, and (4) Recommendations for Responsible AI Integration. Students demonstrated a developmental progression in AI use, from basic concept clarification in the first year to complex clinical reasoning applications in senior years. Students primarily discovered AI tools through informal networks rather than formal academic channels. Key benefits included immediate conceptual clarification across multidisciplinary subjects, enhanced academic efficiency, and support for clinical preparation through simulated patient interactions. However, concerns were expressed regarding regional context limitations, accuracy and reliability issues, potential impacts on critical thinking development, uncertainties related to academic integrity, and affordability constraints for premium features.CONCLUSION: The findings reveal that students resourcefully use AI tools to enhance learning but navigate considerable challenges, including information inaccuracies, ethical uncertainties, and concerns about adverse impacts on critical thinking skills. Future recommendations include establishing clear institutional policies, implementing formal AI literacy training, developing UK-centric pharmacy-specific AI tools, and adopting scaffolded curriculum integration approaches.</p

The patient's perspective: A review of results from a radiotherapy patient experience survey (RPES) at the North West Cancer Centre, Northern Ireland, UK

Introduction Patient satisfaction is an important measure of radiotherapy quality and reflects the ethos of the Health and Care Professions Council. The Radiotherapy Patient Experience Survey (RPES), developed in 2012, captures patients’ experiences across the radiotherapy pathway and was redistributed by National Health Service trusts in 2023. This study reports on patient experiences at the North West Cancer Centre (NWCC), one of two cancer centres in Northern Ireland (NI), using the RPES. While specific to NI, the findings can be considered alongside evidence from other UK regions to inform understanding of radiotherapy experiences across different healthcare contexts. Methods From September 2023 to March 2024, the RPES was distributed to patients at the end of radiotherapy. Descriptive statistics were used to analyse quantitative data, and thematic analysis (Braun &amp; Clarke's six-phase framework) guided qualitative analysis. Ethical approval was granted by the affiliated university and Trust Quality Improvement team. Results In total, 245 participants completed the survey, with 56.7 % (n = 139) identifying as female. Overall, participants reported high satisfaction with their radiotherapy experiences, with 93.5 % (n = 229) rating the quality of care received as excellent. Key areas for improvement included provision of information about acute and chronic side effects before and after radiotherapy, as well as underutilisation and perceived outdatedness of website resources. Conclusion Despite national increasing wait times for radiotherapy, results from this single centre survey indicate high patient satisfaction with radiotherapy care in NWCC in NI. This study highlights excellence in care and provides guidance for enhancement of the service. Implications for practice Key actions have been identified to further improve the radiotherapy service in NWCC. The department should conduct a follow-up clinical audit to ensure improvements are implemented effectively

Artwork SetoMonogatari 9 - From Sunderand to Seto selected for The Graduates exhibition, National Glass Centre, University of Sunderland, 31 Jan-31 July 2026.

This piece is part of an ongoing series informed by art-archaeological research undertaken at abandoned manufacturing sites in Seto, Japan – once the centre of Japan’s post-war ceramic figurine industry. SetoMonogatari is a portmanteau formed from setomono, the historical term for pottery made in Seto, and monogatari, meaning story. Through a process of bricolage, this work explores material, memory and place, reflecting upon this fragile industrial heritage. It also embodies the artist’s own journey and development as a ceramicist over the last 15 years