20505 research outputs found
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Biocontrol of ochratoxigenic fungi by endogenous lactic acid bacteria and yeasts from ivorian robusta coffee in the context of climate change
No full textVerheecke-Vaessen, Carol - Associate Supervisor
Fontana, Angelique - Associate Supervisor
Strub, Caroline - Associate SupervisorThis doctoral research delves into the innovative domain of biocontrol strategies
targeting mycotoxigenic fungi in the context of climate change. Focusing on
Ivorian coffee, a vital economic and agricultural commodity, the study explores
the potential of indigenous lactic acid bacteria (LAB) and yeasts as biocontrol
agents. Mycotoxins, toxic secondary metabolites produced by fungi, pose
significant health risks and economic losses. As climate change amplifies the
proliferation of mycotoxigenic fungi, the demand for sustainable and eco-friendly
interventions intensifies. The research encompasses comprehensive isolation,
identification, and characterization of LAB and yeasts from Ivorian coffee,
evaluating their antagonistic properties against mycotoxigenic fungi.
Furthermore, the study elucidates the mechanisms underlying the biocontrol
activity, shedding light on how these microorganisms mitigate mycotoxin
contamination. This research is pivotal in the pursuit of climate-resilient strategies
for mycotoxin management, contributing to both food safety and agricultural
sustainability.PhD in Environment and Agrifoo
Efficiency enhancement of a unidirectional impulse turbine for dual-chamber OWC wave energy converters
No full textTo address the efficiency limitations of conventional bidirectional turbines in oscillating water column (OWC) wave energy conversion systems, this study proposes a novel dual-chamber OWC configuration coupled with unidirectional impulse turbines. A steady-state Computational Fluid Dynamics (CFD) model based on viscous fluid theory was established and validated against experimental data. Using this model, a comprehensive parametric optimization was performed on rotor blade number, guide vane number, and blade installation angles to enhance aerodynamic performance. The optimized unidirectional turbine achieved a 59.89 % increase in average efficiency and a 67.97 % improvement in peak efficiency compared to a reference bidirectional turbine. Furthermore, the total number of rotor blades and guide vanes was reduced by 26.67 % and 42.31 %, respectively, significantly lowering material requirements and manufacturing costs. Flow field analyses revealed improved pressure distribution, reduced separation zones, and enhanced wake uniformity. This study demonstrates the potential of integrating unidirectional turbines into dual-chamber OWC systems to improve energy conversion performance and reduce structural complexity. The findings provide valuable design insights for wave energy converters. Future work will extend to transient simulations and experimental validation under oscillatory flow conditions.This work is supported by National Natural Science Foundation of China (Grant No. 52571326, 52201349), Guangdong Basic and Applied Basic Research Foundation (Grant No. 2023A1515012224), Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai).Energ
Enhancing aviation safety with artificial intelligence: a systematic literature review on recent advances, challenges and future perspectives
No full textThe global air traffic is projected to grow significantly in the coming decades, leading to denser airspace and higher operational complexities. Therefore, academic and practitioners are now unleashing the potential of artificial intelligence (AI), particularly the recent advances in large language models (LLM), computer vision, and speech recognition in enhancing aviation safety through advanced cockpit design, AI assistants, human performance monitoring, and supporting air accident investigations. These applications demonstrate a significant promise in enhancing aviation safety. Nevertheless, there are still challenges in applying safe and reliable AI in supporting these safety–critical domains. Indeed, many aviation safety issues, such as accident analysis, human factors, and preventive system designs, are interconnected instead of standalone issues. This systematic literature review explores the recent advances, challenges, and future perspectives on leveraging AI to enhance aviation safety from a macro perspective. Therefore, a framework is established to review relevant studies. First, we identify the relevant literature from initial search, inspection, and screening. After that, we analyse the domains applied and the models leveraged in aviation safety enhancement on the 175 selected studies using content analysis. Then, thematic analysis is applied to reveal the challenges of applying safe and reliable AI in aviation safety. Given the challenges identified, this review recommends future work to incorporate explainable AI, develop AI certification frameworks, design based on hybrid intelligence, and adopt diversified dataset for generalisation.The research is supported by Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong SAR.Advanced Engineering Informatic
RAG-based user profiling for precision planning in mixed-precision over-the-air federated learning
No full textMixed-precision computing, a widely applied technique in AI, offers a larger trade-off space between accuracy and efficiency. The recent purposed Mixed-Precision Over-theAir Federated Learning (MP-OTA-FL) enables clients to operate at appropriate precision levels based on their heterogeneous hardware, taking advantages of the larger trade-off space while covering the quantization overheads of the mixed-precision modulation scheme with the OTA aggregation process. A key to further exploring the potential of the MP-OTA-FL framework is the optimization of client precision levels. The choice of precision level hinges on multifaceted factors including hardware capability, potential client contribution, and user satisfaction, among which factors can be difficult to define or quantify. In this paper, we propose a precision planning framework that integrates Retrieval-Augmented Generation (RAG) LLMs and dynamic client profiling to optimize satisfaction and contributions. This includes a hybrid interface for gathering device/user insights and an RAG database storing historical quantization decisions with feedback. Experiments show that our method boosts satisfaction, energy savings, and global model accuracy in MP-OTA-FL systems.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
A conceptual framework for scaling up emergent predictions from mechanistic individual-based models
No full textEnvironmental decision-makers need robust, landscape-level predictions of population responses to inform management decisions before implementation. Mechanistic individual-based models (IBMs) can capture how individual behaviour and interactions in heterogeneous environments generate emergent population dynamics, but high computational costs typically restrict applications to small spatial extents. To address this limitation, we synthesise spatial modelling strategies across subfields of ecology and introduce the Spatial Threshold of Emergent Behaviour Stabilisation (STEBS) framework. STEBS capitalises on the biological realism of mechanistic IBMs through an in silico modelling experiment to quantify the Critical Emergence Threshold (CET) — the smallest spatial extent at which emergent system behaviour stabilises. The CET provides a biologically meaningful and computationally efficient scale for developing meta-models that relate environmental variables to emergent population patterns, which can then be extrapolated across unsimulated regions to predict landscape-level dynamics. This approach enables tractable scaling up of mechanistic IBMs, while retaining their biological realism. STEBS therefore offers a systematic pathway for applying IBMs to real-world environmental challenges, enhancing the evidence base for policy and management under accelerating global change. Future development of STEBS into an operational and transferable tool will require empirical validation across diverse species, landscapes and IBM structures, alongside evaluation of whether the upfront investment required to estimate CETs improves predictive efficiency compared to brute-force scaling approaches.This work is supported by UKRI NERC (grant no. NE/W003031/1) and UKRI BBSRC FoodBioSystems Doctoral Training Partnership (grant no. BB/T008776/1).Individual-based Ecolog
How international physical presence and infrastructure differences moderate the link between digital internationalization and MNE performance
No full textWhile prior work has predominantly studied the performance implications of multinational enterprise (MNE) physical internationalization, research on how MNEs perform when simultaneously coordinating international digital channels and physical presence remains scarce. This challenge is particularly acute in retail, where the strategic convergence of born-digital retailers expanding physically and traditional retailers going digital creates new cross-domain challenges. Nonetheless, the impact of international physical presence and differences in home country physical infrastructure relative to host countries on MNE performance remains unexplored. Drawing on the integration-responsiveness (IR) framework, we suggest that a non-linear, U-shaped pattern governs the relationship between digital internationalization and performance for these retail MNEs, because the costs of integration and responsiveness are dominant at lower levels of internationalization while their advantages become more pronounced with increased internationalization. Further, we argue that the digital internationalization and MNE performance relationship steepens (a) with a higher international physical presence and (b) for firms originating from home countries with superior physical infrastructure relative to their host countries. Utilizing an 11-year panel of some of the largest retail MNEs, our research contributes to international strategy literature by extending the IR framework to a multidomain digital and physical context, stressing the strategic importance of firm- and country-level physical resources and infrastructure in digital internationalization.Long Range Plannin
Scalable and generalizable path planning for robotic navigation using transformer-based heuristic learning
No full textEfficient and scalable path planning is a critical challenge for autonomous robotic systems, particularly in complex real-world scenarios. Traditional heuristic search algorithms like A* often struggle with scalability and adaptability in such environments. To address these limitations, we improve a search framework that integrates learned, instance-specific heuristics with conventional pathfinding techniques. Leveraging autoencoder transformer networks, we predict two key heuristic functions—Correction Factor (CF) and Path Probability Map (PPM)—trained on diverse datasets—the Motion Planning (MP) and Tiled-MP datasets—to cover a wide range of path planning scenarios. When integrated with Weighted A* (WA*) algorithm, this approach optimally solves 88% of MP instances, with paths averaging less than 0.7% longer than optimal, and requiring nearly five times fewer node expansions. The framework demonstrates the advantages of heuristic learning in handling larger path planning problems, with inference time accounting for just 10% of the total search duration. It solves nearly half of the most complex instances optimally, showcasing strong scalability for real-time robotics applications. The framework performs well in unseen environments, solving over 25% of new problems perfectly, finding near-optimal solutions with paths less than 7% longer than optimal, and requiring fewer than two-thirds of the typical expansions. Our framework outperforms learnable planners in both scalability and generalization.Information Science
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