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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
Effects of prenatal exposure to hexafluoropropylene oxide dimer acid on rat and offspring mammary gland development and associated hormone levels
No full textBACKGROUND: This study investigated the effects of prenatal exposure to hexafluoropropylene oxide dimer acid (HFPO-DA), a replacement for perfluorooctanoic acid (PFOA), on mammary gland development of both pregnant rats and their offspring, as well as its influence on related hormone levels.
METHODS: Pregnant Sprague-Dawley (SD) rats were orally exposed to HFPO-DA at doses of 0, 1, 10, and 100 mg/kg/day from gestation day (GD) 0.5 to GD 19.5. On GD 19.5, half of the pregnant rats from each dose group underwent caesarean section, while the other half gave birth naturally. The offspring from the rats that gave birth naturally were raised until they reached postnatal day (PND) 21. The serum levels of progesterone (Pg), estradiol (E2), and prolactin (PRL) in the pregnant rats and their offspring on PND 21 were detected via ELISA (enzyme-linked immunosorbent assay). Changes in mammary glands of pregnant rats, their fetuses, and PND 21 offspring were assessed by haematoxylin and eosin (H&E) staining. The development of mammary gland tissue structures in fetuses and PND 21 offspring was evaluated using whole gland staining. Immunohistochemical staining was used to assess STAT5 expression in the mammary glands of pregnant rats and Ki67 expression in the mammary glands of fetuses and PND 21 offspring.
RESULTS: In pregnant rats, exposure to HFPO-DA significantly increased the PRL levels. The expression of STAT5 was also significantly elevated in mammary epithelial cells. The lobular and alveolar areas expanded dose-dependently, and milk secretion was observed in the high-dose group. In fetuses and PND 21 offspring exposed to HFPO-DA, we observed significant growth of secondary mammary ducts, a substantial increase in ductal coverage area, ductal buds, and primary duct length, as well as an increase in Ki67 expression in mammary epithelial cells.
CONCLUSIONS: Collectively, these findings suggest that HFPO-DA exposure elevates serum PRL levels in pregnant rats, promotes lactation, and stimulates early-stage mammary gland development in offspring.This work was supported by grants from the National Natural Science Foundation of China (Grant No.81903271) and Medical and Health Science and Technology Development Project of Shandong Province (Grant No.202306031042).Chemico-Biological Interaction
Data-driven method to ensure cascade stability of traffic load balancing in O-RAN based networks
No full textLoad balancing in open radio access networks (O-RAN) is critical for ensuring efficient resource utilization, and the user’s experience by evenly distributing network traffic load. Current research mainly focuses on designing load-balancing algorithms to allocate resources while overlooking the cascade stability of load balancing, which is critical to prevent endless handover. The main challenge to analyse the cascade stability lies in the difficulty of establishing an accurate mathematical model to describe the process of load balancing due to its nonlinearity and high-dimensionality. In our previous theoretical work, a simplified general dynamic function was used to analyze the stability. However, it is elusive whether this function is close to the reality of the load balance process. To solve this problem, 1) a data-driven method is proposed to identify the dynamic model of the load balancing process according to the real-time traffic load data collected from the radio units (RUs); 2) the stability condition of load balancing process is established for the identified dynamics model. Based on the identified dynamics model and the stability condition, the RAN Intelligent Controller (RIC) can control RUs to achieve a desired load-balancing state while ensuring cascade stability.The work is supported by EPSRC CHEDDAR: Communications Hub For Empowering Distributed ClouD Computing Applications And Research (EP/X040518/1) (EP/Y037421/1).2025 IEEE 102nd Vehicular Technology Conference (VTC2025-Fall
Collaborative dual-arm robot system for aircraft ground refuelling
No full textThe growing demand for automation, alongside advancements in robotics, sensing, and artificial intelligence, is driving the adoption of professional service robots across multiple sectors. In the aerospace industry, aircraft ground refuelling remains a hazardous, labour-intensive task with significant potential for robotic automation. Despite decades of interest, autonomous refuelling has yet to be realised due to the complexity of the environment, technical challenges in nozzle manipulation, and limitations of legacy robotic systems. This paper presents a novel collaborative closed-loop kinematic chain robot for autonomous fuel nozzle positioning and latching. The system addresses key challenges such as accurate fuel port localisation, precise nozzle manipulation and positioning, and secure connection, all within a semi-structured airport environment. A robot demonstrator was developed as part of an industry-funded project aimed to demonstrate the solution’s potential to enhance safety and improve efficiency in commercial aircraft refuelling.2025 7th International Conference on Control and Robotics (ICCR
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
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