20505 research outputs found
Sort by
Nutrient dynamics and recovery efficiencies in a decentralised faecal sludge and food waste treatment system
Full text linkDecentralised faecal sludge (FS) and food waste (FW) treatment systems like co-composting system offer more practical solutions for waste treatment and nutrient recovery in low- and middle-income countries, yet nutrient recovery and losses across this system remain poorly quantified.This study aimed to assess the flows, losses, and recovery efficiencies of nitrogen (N), phosphorus (P) & potassium (K) with the goal of recommending measures to minimize pollution to water bodies. Raw FS, FW, compost, and effluent samples were collected at each treatment stage over three treatment cycles from August 2021 – 2022 in Somanya, Ghana. A total of 108 composite samples were collected and analysed for N,P & K using standard procedures. The N, P & K losses at each stage of the treatment system were calculated using the mass balance principle and the nutrient flow diagrams were created using the Sankey diagram generator. Results show that, 59-86% N, 8-40% P and 49-81% K were lost at the dewatering stages for all cycles. Losses were lumped together as either gaseous losses, adsorption to media surfaces or percolate. The overall nutrient recovery efficiency of the system was 6–17% N, 20–37% P & 17-24% K in co-compost and treated effluent. Despite high removal efficiencies in the facultative ponds, the final effluent did not meet EU standards. Effluent may become a resource in geographies that have scarce water and less stringent regulations. This study recommends strategies and approaches such as biochar use, percolate/leachate recirculation and covering of compost piles to reduce nutrient losses.We would like to acknowledge the Sue White Fund, Cranfield University and the International Water Management Institute for funding this work.Environmental Technology & Innovatio
How green are my apples - The greenhouse gas emissions and blue water scarcity footprint of fresh apple supply chain
Full text linkThe environmental impact of the UK fresh apple supply chain depends on the sourcing locations. This paper examines the contribution of production, storage, processing, and transport to evaluate the greenhouse gas emissions (GHGE) and blue water scarcity footprint of the main sources of apple supply to the UK (2016 – 2025). Domestic production accounted for 38 % of supply, with imports from the rest of Europe (e.g. France, Italy, Germany, Ireland, The Netherlands, Spain) representing for 37 % and most of the remainder from southern hemisphere countries, such as South Africa (12 %), New Zealand (7 %) and Chile (5 %). Our results revealed that GHGE at the orchard stage for UK, European, and Chilean apples are similar. During postharvest, cold storage is the main contributor for GHGE, which were 40 % lower in northern hemisphere countries compared to maritime shipping stages for the southern hemisphere areas. Transport emissions are affected by international travel distances. South Africa and Spain presented the highest blue water consumption (BWC) as well as blue water scarcity footprint. We found that blue water scarcity footprints are negligible where apple production is rainfed. The results suggest that in order to mitigate GHGE, energy mixes need to be improved as well as cold storage technologies. For water footprint, implementing infrastructural changes is paramount. These results can help as decision making tool to define new sourcing strategies that can minimise environmental impacts. This assessment also highlights limitations in methodology, including inconsistent approaches in GHGE assessment, and underscores the need for standardised methodologies, emphasises the role of externalities, and highlights the importance of considering economic and social factors in assessing environmental trade-offs in apple supply chains.Agricultural Water Managemen
Introducing exaptation theory to social enterprise innovation: case study on the Circular Economy Innovation Communities (CEIC) project
No full textPurpose:
With the orientation for sustainability, circular economy (CE) is promoted through “exaptive events” such as educational workshops. Social enterprises (SEs) play an important role in CE transformation but face significant challenges. Whilst exaptation theory is well-studied in commercial context, less is known about its role in driving SE innovation. Thus, this paper aims to answer the following research question: how can exaptive events facilitate circular economy innovation in the context of social enterprise?
Design/methodology/approach:
This paper conducted an in-depth case study of a CE-themed project in the UK, engaging with the event organisers and SE participants. Data were collected through interview, observation and secondary documentation. Using a structured inductive approach, this paper identified key themes and generated a grounded theoretical model.
Findings:
Four themes are identified as: 1) knowledge sharing, 2) exaptive pools, 3) exaptive relations and 4) challenges to SE sustainability and success. Findings indicate that exaptive events can enhance SE innovation. Also, the connection of exaptive events, tools and relations helps overcome barriers in finance, commercial viability, social value and cross-sector collaboration. Nevertheless, finance and commercial viability inhibit SE’s further effective innovations.
Originality/value:
By offering a new perspective – exaptive events – on SE innovation, this study extends the application of exaptive innovation beyond private-sector product commercialisation to tackling challenges faced by SEs. This study indicates that practitioners and policymakers can leverage exaptive events to promote sustainable development. For SEs, collaboration with public sectors through these events promotes resource sharing, joint innovation and commercialisation opportunities.This study was supported by the European Social Fund (No. WWV 82251).Journal of Enterprising Communities: People and Places in the Global Econom
Optimizing co-composting ratios of cocoa pod husk and poultry manure for cocoa organo-mineral fertilizer formulation in the West African cocoa belt
No full textSection: Waste Management in AgroecosystemsCocoa is a valuable global commodity but its management results in residues such as cocoa pod husk, leaves and placenta which are under-utilised as a resource. This study sought to address this by evaluating the impacts of different mixing ratios of cocoa pod husk (CPH), placenta (PL) and locally sourced poultry manure (PM) on compost quality and which mixing ratio will enhance compost NPK stoichiometry required for cocoa organo-mineral (OMF) fertiliser formulations. Quality of compost within OMF can influence nutrient release and potentially improve soil health in cocoa farms. The composting study considered 8 treatments consisting of different combinations of PM, and CPH at ratios of 1:1, 2:1, 1:2 and 1:3, with and without addition of PL. Treatments with PL recorded the highest (32 %) organic carbon at the end of the composting period. Highest available N (5919 mg kg-1) was observed in treatments with PL and the lowest (1146 mg kg-1) was observed in treatments without PL. Treatments without PL recorded the highest available P (3843 mg kg-1) and treatments with PL yielded the lowest (2167 mg kg-1). Highest (16922 mg kg-1) available K was observed in treatments with PL and the lowest (12263 mg kg-1) observed in treatments without PL. Addition of PL to CPH and PM causes an increase in organic carbon, available N, available K but reduces available P. Compost PM+CPH (2:1) and PM+CPH (1:2) have NPK stoichiometry which aligns with nutrient requirements of young cocoa (<7 years old) and matured cocoa (≥8 years), respectively. This study demonstrates the use of circular economy to improve resource efficiency in cocoa farms. For future studies, it is important to ascertain agronomic effectiveness of the compost PM+CPH (2:1) and PM+CPH (1:2) ratios on the young and matured cocoa trees, respectively.This study was co-funded by the Sue White Fund of Cranfield University and the African Plant Nutrition Institute (APNI) under the remote sensing-supported framework that incentivizes site-specific agronomic management of smallholder cocoa farms (FRAME-Cocoa project) in Ghana.Frontiers in Sustainable Food System
Dynamic Behaviour Classification in Multi-Domain Operations Using ADS-B and Air Traffic Data
No full textHill, Andrew - Industrial Supervisor
Jenkins, Andrew - Industrial SupervisorThe increasing complexity of air traffic in multi-domain operations (MDO) demands real-time monitoring and classification of aircraft behaviour to support safety, efficiency, and situational awareness. Conventional approaches to conformance monitoring and behaviour classification are largely batch-oriented, relying on full-trajectory analysis and neglecting operational context, which limits their applicability during evolving flight operations. This dissertation proposes a dual-pipeline, AI-based framework that integrates unsupervised anomaly detection for conformance monitoring and supervised behaviour classification using Automatic Dependent Surveillance-Broadcast (ADS-B) and air traffic control (ATC) context. An LSTM-Autoencoder (LSTM-AE), incorporated with Notice to Airmen (NOTAMs) constraint, models nominal kinematics and flags deviations via reconstruction error, while a Bi-directional LSTM (Bi-LSTM) classifies behaviour at each time instant. To enable real-time inference, both models operate on overlapping sliding windows, providing early decisions while preserving short-term temporal dependencies. Experiments on ADS-B trajectories from the OpenSky Network show that the proposed framework achieves strong classification performance and the LSTM-AE reliably filters non-nominal patterns via elevated anomaly scores, by incorporating NOTAM-derived constraints improves operational relevance. These results demonstrate the feasibility of real-time, context-aware trajectory monitoring for MDO environments.MSc in Advanced Air Mobility System
Retrieval Augmented Generation (RAG) for Space Mission Design - A Space Mission Design Assistant
No full textEarly-phase space mission design is characterised by vast design tradespaces and significant uncertainty, where effective retrieval of historical knowledge is crucial yet often inefficient, even with the adoption of Model-Based Systems Engineering (MBSE). This paper presents a novel Retrieval-Augmented Generation (RAG) framework to streamline this process by leveraging a Large Language Model (LLM) as an intelligent design assistant. The system integrates a curated knowledge base derived from the European Space Agency (ESA)’s eoPortal archive with a LlamaIndex-based RAG pipeline. The methodology involved developing a high-throughput scraper (achieving a >4×speedup and 99.9% data acquisition success), robust data preprocessing (including Markdown-based structured data conversion), and a comprehensive evaluation via a parameter sweep against a synthetically generated benchmark. Results demonstrate a highly effective system with high retrieval accuracy (Mean Average Precision (MAP@k) ≈0.95) and strong generation quality (F1-score ≈0.75). The optimal configuration (top_k=5, similarity_threshold=0.5, temperature=0.1) provides faithful and relevant answers to complex technical queries within acceptable latency (median 2.0 seconds), verified through qualitative analysis of a Streamlit prototype. This framework significantly enhances data-driven decision-making in early-phase space mission analysis, laying a robust foundation for future, deeper integration with MBSE workflows.MSc in Astronautics and Space Engineerin
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
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
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