20505 research outputs found
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Advances in medical image processing for early breast cancer detection: classical techniques and deep learning perspectives
No full textThis article belongs to the Special Issue Signal and Image Processing Applications in Artificial Intelligence, 2nd EditionBreast cancer is the most common malignancy among women and a leading cause of cancer-related mortality, making early and accurate detection essential. This review summarises advances in breast imaging and computational diagnostics across mammography, ultrasound, and magnetic resonance imaging (MRI), highlighting challenges in differentiating benign from malignant lesions and identifying rarer tumour types. Key preprocessing steps—denoising, deblurring, and contrast enhancement—are reviewed as they improve image quality prior to analysis. Classical methods (e.g., thresholding, edge detection, and region growing) are compared with deep learning approaches for segmentation and classification. CNNs, RNNs, and emerging transformer-based models consistently outperform handcrafted pipelines, with representative studies reporting 5–15% gains in AUC/accuracy and deep models achieving AUC > 0.85–0.95 on several benchmarks. The review also discusses dataset constraints, common evaluation metrics (AUC, Dice, sensitivity, specificity), and clinical translation barriers such as interpretability and domain shift. Overall, AI-driven methods show strong potential to enhance early detection and support improved breast cancer outcomes.Electronic
Dataset: The performance of low-coherence and confocal refractometry with reduced index contrast
No full textThis paper looks to evaluate the performance of combined low-coherence and confocal refractometry under conditions of reduced refractive index contrast. The instrument measures the refractive index and thickness of transparent objects using a fibre-based low-coherence interferometer with a line-scan spectrometer. A sample was designed that roughly mimics the structure of the eye, consisting of a lens tube holding window pairs surrounded on either side by fluid chambers and sealed at the far end by diffuse black metallic plugs. Sucrose solutions of increasing concentrations were injected into the fluid chambers providing a linear variation in refractive index from 1.3330 to 1.4416. The instrument was used to measure both the phase and group refractive indices, np and ng, as well as the physical thickness t of the windows and the fluid in the chambers. Measurements show that the accuracy and uncertainty decrease with reduced refractive index contrast but remain better than 0.6% for sucrose concentrations of up to 60.3%, which is close to the saturation limit.Engineering and Physical Sciences Research Council (EPSRC
Case study of air transport in Barbados: a decade of evolution
No full textThe Caribbean island-nation of Barbados represents an intriguing and valuable case study in air transport, with very little scholarly attention to date. This paper aims to investigate the state of air transport in the country over a decade-long period using three full years as reference points starting in 2014, then 2019 before onset of the COVID-19 global pandemic; and finally, 2023. The overall air market is assessed, then major markets by region are detailed; and lastly, the CARICOM air market’s direct links to Barbados are covered. By employing data from these three 12-month time periods over a decade (2014, 2019 & 2023), a clearer sense of the key insights into the country’s air transport industry’s size, scope and prospects can be achieved, while the composition of the air transport industry can also be better ascertained. Added to this, the likely future trajectory of the industry can be more accurately gauged and forecast. The core lessons and major implications from this study, not just for Caribbean nations and territories, but for other similarly sized and situated islands in places like the South Pacific and Indian Ocean, are many and varied. Chiefly, air transport development plays a pivotal role in not only connecting these communities with their neighboring regions and wider world, but in ensuring that island economies can diversify beyond a traditional focus on agriculture and tourism and expand into a wider array of sectors. In this context, the internal/national socio-economic benefits of air transport represent more than just the external links that air transport creates; but rather, national economic development and diversification with air transport acting as a key enabler.Transportation Research Procedi
Near-source wastewater surveillance as a non-invasive tool for disease detection in prisons
No full textNear-source wastewater-based epidemiology (WBE) offers a non-intrusive alternative to clinical testing of whole prison populations. Prisons sit at the centre of high transmission risk but experience limited health-care access and barriers to testing individual prisoners. However, the use of WBE for health protection in prison settings has been limited. To assess its merit during the COVID-19 pandemic, SARS-CoV-2 RNA concentrations were quantified in 680 composite wastewater samples collected from 14 prisons across England and Wales between January and June 2021. Viral RNA was detected in 48% of samples, and wastewater viral loads were found to closely mirror clinical case numbers Lead–lag analysis with adjacent municipal wastewater samples indicated a bidirectional flow between the prisons and their local community: seven prisons exhibited wastewater peaks ahead of their communities, while six lagged, highlighting heterogeneous epidemiological coupling. Marked differences between prisons were apparent in both physicochemical wastewater traits and clinical testing uptake, indicating each institution constitutes a distinct surveillance unit. Collectively, findings here indicate near-source WBE as a rapid, unbiased and scalable tool for disease outbreak detection and for mapping disease flow between prisons and their surrounding communities, advocating its integration into routine health-security frameworks for custodial and other high-density settings.Funding was provided by a UK EPSRC Impact Acceleration Award (EP/R511584/1) and a UK NERC award (NE/V004883/1) in the COVID-19 Urgency Programme. Funds were also provided by the UK Department of Health & Social Care (Grant Reference 2020_086)Scientific Report
Technology exploration of zero-emission regional aircraft: why, what, when and how?
No full textThe paper focuses on the exploration and comparison of zero-emission technology strategies for regional aircraft. While significant progress is made on the development of technologies, systems and aircraft configurations, major challenges and uncertainties mean that various strategies are considered but are difficult to compare as they rely on different technologies, metrics, requirements, maturity levels and sustainability targets. A novel, holistic approach that captures inter-dependencies, synergies and combined impact of technologies is developed to evaluate the feasibility of such aircraft over 2 horizons, quantify performance and emissions through various phases of the life cycle, establish technology bottlenecks and required step changes and classify developments in terms of impact and risk. For at least 30 passengers at 300 nmi, significant advances are required for fuel cells (2 kW/kg), electric machines (13 kW/kg), power distribution ( > 1.5 kVolts), and thermal management systems (3.5 kW/kg and 3.5 kW/kW). These will lead to major mission level ( + 90%) and lifecycle energy penalties (up to + 177%) with a carbon intensity level of 6.5 kgCO2/kgH2 (ex. blue, turquoise, green hydrogen) required to breakeven current CO2 levels. Step changes including superconductivity and high temperature fuel cells, along with aircraft mass and drag reductions are required to increase capacity to pax > 40 and 800 nmi, and achieve energy reductions against existing designs. The energy density of batteries and the need of gas turbines to meet diversion and hold requirements limit full electric variants to 30 passengers at 200 nmi with 480 Wh/kg battery energy density but they can offer an exceptional energy per passenger benefit ( ∼ 40% reduction) against current aircraft.Progress in Aerospace Science
Decarbonisation in Supply Chains
No full textThis thesis examines how decarbonisation contributes to firm-level supply-chain performance. Motivated by the predominance of Scope 3 emissions and fragmented evidence across operations, sustainability, and policy outlets, it consolidates and interprets current knowledge to inform managerial and policy decisions. Using a systematic literature review aligned with Denyer & Tranfield and PRISMA, the review searched Scopus and EBSCO Business Source Complete, applied pre-specified, protocolised eligibility criteria, and appraised study quality with a calibrated rubric. Searches covering 2015–2025 identified 793 records; after de- duplication and screening, the PRISMA flow was 793 → 676 → 150 → 60, yielding 60 studies for synthesis. Descriptive mapping is followed by thematic synthesis through a barriers–opportunities–benefits lens. Findings show that environmental, operational, and financial improvements align when four conditions operate together: (1) decision-grade data routines make in- formation usable at the speed of operations; (2) credible external signals (e.g., prices, rules) are contracted into supplier relationships so incentives persist; (3) collaboration density is designed—tight where interdependence is high, loose elsewhere—to keep governance costs bounded; and (4) practices are embedded in planning and control so benefits accumulate rather than stall in pilots. A pragmatic sequence emerges—establish data routines and align incentives; right-size collaboration; embed in planning and control—with a feedback loop in which realised benefits finance the upkeep of data and governance. Boundary conditions include tier distance and SME heterogeneity, asset specificity and capital cycles, and data sensitivity with assurance needs. The thesis recentres decarbonisation on network governance—treating information as infrastructure, articulating policy–contract complementarity, and formalising collaboration density as a design variable—and offers role-specific actions for lead firms, SMEs, and multinationals. Policy implications emphasise ii consistent, contractable disclosure and assurance ‘without exposure’. Limitations concern database scope, English-only coverage, sectoral skew, and single-coder synthesis; future work should validate mechanisms longitudinally and develop SME-centred enablement and collaboration-design rules.MSc in Procurement and Supply Chain Managemen
Evaluating the Effectiveness of Supply Chain Simulation Exercises in Public Health - A Literature Review Perspective
No full textThis thesis explores how full-scale simulation exercises (FSEs) contribute to the creation, transfer and retention of tacit knowledge in humanitarian operations. Drawing on a systematic literature review of 28 peer-reviewed studies, the analysis applies Knowledge Creation Theory and the SECI model to examine three levels of learning. At the individual level, tacit knowledge is developed through socialisation—informal exchanges, mentorship and shared experiences—and internalisation, where explicit procedures are embodied through practice and reflection. These processes strengthen professional competences, enhance confidence and foster adaptive expertise that endures beyond exercises. At the organisational level, FSEs serve as formal mechanisms for externalising individual insights through debriefings, codified training artefacts and evaluation practices, while knowledge-centred and trust-based cultures support informal sharing across teams. At the system level, FSEs provide collaborative platforms where diverse organisations rehearse joint responses, align practices and build collective memory. Effective knowledge and information management further enables dissemination and coordination across agencies. Overall, the findings show that FSEs are not merely training events but critical knowledge infrastructures that link individual expertise, organisational learning and inter-organisational collaboration. By demonstrating how tacit knowledge can be systematically cultivated and institutionalised, the study contributes to theory on knowledge creation and offers practical guidance for enhancing preparedness and resilience in humanitarian operations.This thesis explores how full-scale simulation exercises (FSEs) contribute to the creation, transfer and retention of tacit knowledge in humanitarian operations. Drawing on a systematic literature review of 28 peer-reviewed studies, the analysis applies Knowledge Creation Theory and the SECI model to examine three levels of learning. At the individual level, tacit knowledge is developed through socialisation—informal exchanges, mentorship and shared experiences—and internalisation, where explicit procedures are embodied through practice and reflection. These processes strengthen professional competences, enhance confidence and foster adaptive expertise that endures beyond exercises. At the organisational level, FSEs serve as formal mechanisms for externalising individual insights through debriefings, codified training artefacts and evaluation practices, while knowledge-centred and trust-based cultures support informal sharing across teams. At the system level, FSEs provide collaborative platforms where diverse organisations rehearse joint responses, align practices and build collective memory. Effective knowledge and information management further enables dissemination and coordination across agencies. Overall, the findings show that FSEs are not merely training events but critical knowledge infrastructures that link individual expertise, organisational learning and inter-organisational collaboration. By demonstrating how tacit knowledge can be systematically cultivated and institutionalised, the study contributes to theory on knowledge creation and offers practical guidance for enhancing preparedness and resilience in humanitarian operations.MSc in Procurement and Supply Chain Managemen
Design of secure communication networks for UAV platform empowered by lightweight authentication protocols
No full textFlying Ad Hoc Networks (FANETs) formed by cooperative Unmanned Aerial Vehicles (UAVs) require formally proven secure and resource-efficient authentication because open wireless channels allow active adversaries to inject commands, replay traffic, and impersonate nodes. Conventional certificate-based mechanisms impose key management overhead and remain vulnerable under device capture, while existing lightweight and Physical Unclonable Function (PUF)-assisted proposals commonly assume stable connectivity, lack formal adversarial verification, or are evaluated only through simulation. This paper presents a lightweight PUF-assisted authentication protocol designed for dynamic multi-hop FANET operation. The scheme provides mutual UAV–Ground Station (GS) authentication and session key establishment and further enables secure UAV–UAV communication using an off-path ticket mechanism that eliminates continuous infrastructure dependence. The protocol is constructed through verification-driven refinement and formally analysed under the Dolev–Yao model, establishing authentication and session key secrecy and resistance to replay and impersonation attacks. Implementation-oriented latency measurements on Raspberry-Pi-class embedded platforms demonstrate that cryptographic processing time can be further reduced with hardware improvements, while the overall end-to-end delay is still largely determined by channel conditions and connection behaviour. Comparative evaluation shows reduced communication cost and broader security coverage relative to existing UAV authentication schemes, indicating practical deployability in large-scale FANET environments.Electronic
Type 2 diabetes prediction without labs: a systems-level neural framework for risk and behavioral network reorganization
No full textSection: Health InformaticsBackground:
Prediction models for Type 2 Diabetes Mellitus (T2DM) often rely on biochemical markers such as glycated hemoglobin, fasting glucose, or lipid profiles. While clinically informative, these indicators typically reflect established dysglycemia, limiting their value for early prevention. In contrast, psychosocial stress, sleep disturbance, tobacco use, and dietary quality represent modifiable, non-clinical factors that can be observed long before metabolic abnormalities are clinically detectable. Yet most studies examine these factors in isolation or as additive lifestyle scores, overlooking how their interdependencies reorganize in the preclinical phase. A systems-level approach is therefore needed to capture how disruptions in behavioral coherence signal emerging vulnerability.
Methods:
This study develops a dual-analytic framework that integrates Cox proportional hazards models with artificial neural network (ANN) coherence analysis. Using longitudinal data from the UK Biobank (n=15,774; follow-up up to 17 years), we identified non-clinical predictors of incident T2DM and examined how behavioral networks reorganize across health states. Predictors were screened through multivariate survival analysis and mapped into ANN-derived influence matrices to quantify stability, direction, and systemic coherence of relationships among diet, sleep, psychosocial states, and demographics.
Results:
Eighteen significant predictors of T2DM onset were identified. Elevated risk was linked to loneliness, psychiatric consultation, emotional distress, insomnia, irregular sleep, tobacco use, and high intake of processed meat, beef, and refined grains. Protective effects were observed for 7–8 h of sleep, oat and muesli consumption, and fermented dairy. ANN analyses revealed a pronounced breakdown of behavioral coherence in T2DM: foods that stabilized mood in healthy individuals became associated with distress, age and BMI lost their anchoring roles, and emotional states emerged as dominant but erratic drivers of diet. These reversals and destabilizations were consistent across model iterations, suggesting robust signatures of preclinical vulnerability.
Conclusion:
T2DM risk is better conceptualized as systemic reorganization within behavioral networks rather than the additive effects of isolated factors. By combining survival models with ANN-derived coherence mapping, this study demonstrates that early prediction is possible from modifiable, everyday behaviors without laboratory measures. The framework highlights leverage points for psychologically informed, personalized prevention strategies.Frontiers in Digital Healt
Mission-centric design optimisation of unmanned aerial vehicles for enhanced operational effectiveness
No full textThis study introduces a mission-centric design optimisation framework for unmanned aerial vehicles (UAVs) to enhance mission performance across diverse operational scenarios. The proposed framework integrates multidisciplinary design optimisation with a wargaming-based simulation environment and leverages deep neural network-based surrogate models to balance key performance metrics, such as aerodynamic efficiency, radar cross section, structural weight and payload capacity. By incorporating automated task assignment, path planning and a probabilistic combat model, the framework evaluates UAV configurations in multi-domain, multi-asset scenarios. The algorithm identifies optimal solutions that maximise mission success while managing trade-offs among survivability, lethality and cost. Simulation results illustrate the framework’s functionality through representative mission scenarios, highlighting how design variables can influence operational effectiveness relative to baseline configurations. Furthermore, the modular design approach enables rapid UAV reconfiguration for evolving mission needs, offering scalable and adaptable solutions. These findings highlight the importance of integrating mission simulation tools with advanced optimisation techniques to address challenges in dynamic, high-threat environments, providing a robust methodology for UAV and fleet design.This research is co-funded by BAE Systems and UK Research & Innovation (UKRI), through the Engineering and Physical Sciences Research Council (EPSRC), under the Industrial Cooperative Awards in Science and Engineering (ICASE) scheme, as part of the research project entitled Towards Trustworthy AI-driven Autonomous Systems: Multidisciplinary Design Optimisation.The Aeronautical Journa