20505 research outputs found
Sort by
Ten Questions on Indoor Greening and Environmental Quality
No full textWhile outdoor urban greening is recognised for its benefits, indoor green infrastructure (iGI) in shaping indoor environmental quality (IEQ) - including air quality, thermal comfort, and bioaerosols - remains underexplored. This ten-question paper identifies key challenges, opportunities, and research gaps in the iGI-IEQ nexus, organised under 10 questions across five thematic clusters: (1) biophysical and technical performance; (2) ecological and microbiological dynamics; (3) human health and wellbeing; (4) equity, access, and socio-economic factors; and (5) implementation and systems integration. Findings indicate that iGI can improve air quality, regulate humidity, and enhance thermal comfort. However, its performance depends strongly on plant density, species selection, and ventilation. Most evidence comes from controlled settings. iGI may offer positive psychological and cognitive benefits, and can reduce health inequalities through affordable indoor interventions. However, significant data scarcity exists for long-term field studies, indoor microbial ecosystem effects, and socio-economic accessibility. Widespread adoption of iGI requires quantification of proven benefit conditions, followed by overcoming technical, operational, and regulatory barriers via adaptive design, digital monitoring, and interdisciplinary collaboration. As a culminating synthesis, this study introduces a newly developed comprehensive matrix that classifies twenty-six indoor greening types across twenty IEQ parameters, incorporating an assessment of current data confidence. This matrix lays a foundational framework for informed decision-making and design guidance. This review offers evidence-based insights for researchers, policymakers, and practitioners to effectively leverage iGI where suitable, in creating healthier, climate-resilient residential and commercial buildings, addressing both immediate IEQ challenges and supporting long-term sustainability objectives.This work was carried out under the framework of the UKRI (EPSRC)-funded GREENIN Micro Network Plus (Grant No. APP55977) as part of its rapid reviews series, with additional support from the UGPN-funded tri-lateral (UK, USA, Brazil) GREENICON projects.PK and co-authors also acknowledge support from the RECLAIM Network Plus (EP/W034034/1; EP/W033984), GP4Streets (UKRI1281), and GreenCities (NE/X002799/1; NE/X002772/1) projects, as well as from CNPq and FAPESP (Process no. 2024/01097-2).Building and Environmen
Investigation of procurement risk management strategies in the post-contract award phase
No full textYates, Nicky - Associate SupervisorThis research empirically investigated how procurement risk management (PRM)
strategies are used to manage risks in the post-contract award phase. Through
three sequential papers, this study adopted multiple methods to gain insights into
the procurement risks, risk management strategies, and risk management tools
and techniques used in the post-contract award phase in manufacturing sector.
Paper 1 is a literature review of the risk management strategies used in the three
procurement phases: pre-contract, selection and contracting, and post-contract
award. The author conducted an SLR of 100 peer-reviewed articles published
between 2000 and 2025. The key findings of this study are twofold. First, it
synthesized four main themes: procurement risks, procurement risk management
tools and techniques, procurement risk mitigation strategies, and factors that
influence the selection of risk mitigation strategies across the three procurement
phases. Second, the findings highlighted that procurement risk management
tools and techniques in the post-contract award phase have been neglected in
the literature compared to the pre-contract, and selection and contracting phases.
Paper 2, an empirical study, adopted a qualitative approach to gain insights into
procurement risk management in the post-contract award phase. The author
interviewed Procurement professionals (23) from 7 manufacturing industries in
the United Kingdom (UK) and highlighted three key findings based on the
interview insights. First, the results identified five risk categories: supplier
performance, contract design, supplier relationship, ethical, and disruption risks.
Second, procurement professionals combined technological tools, such as data
analytics and machine learning, with human engagement techniques, including
site visits and review meetings, to identify and assess risks and plan mitigation
strategies. Third, a combination of preventive and reactive PRM strategies were
implemented in the post-contract award phase.
Paper 3, an empirical study, examined how sociological mechanisms affect
procurement risks and procurement risk management performance during the
post-contract award phase. A quantitative survey was conducted among 313
procurement professionals from the US automotive manufacturing industry. This
study has four key findings. First, combining trust with information sharing,
commitment, and flexibility mitigates the negative effects of switching costs on
procurement risk management performance than using trust alone. Second,
combining trust with information sharing, commitment, and flexibility mitigates the
negative effects of switching costs and negotiation costs on procurement risk
management performance than using trust alone. Third, sociological constructs
are insufficient as PRM mechanisms to mitigate the negative impact of
environmental uncertainties on procurement risk management performance.
Fourth, sociological constructs are insufficient as PRM mechanisms to mitigate
the negative influence of supplier opportunistic behaviours on procurement risk
management performance.
Overall, this thesis makes several key contributions and extends the literature in
the following ways. This SLR study contributes to the existing literature by
aligning the fragmented strands of risk management literature and systematically
synthesizing the procurement risks, the tools and techniques for identifying,
assessing, and mitigating risks, and the risk mitigation strategies in each
procurement phase. Second, it provides a new, empirically based procurement
risk management model that integrates procurement risk identification,
assessment, and mitigation strategies into the post-contract award phase. Third,
it provides new empirical evidence that combining trust with information sharing,
commitment, and flexibility mitigates the negative effects of switching costs and
negotiation costs on procurement risk management performance more effectively
than using trust alone. Fourth, it provides new empirical evidence that combining
trust with information sharing, commitment, and flexibility mitigates the negative
effects of switching costs and negotiation costs on procurement risk management
performance than using trust alone.PhD in Leadership and Managemen
Multi-decadal geodetic mass balance, climate sensitivity, and projected glacier response in the Chandra–Bhaga Basin, Western Indian Himalaya (1971–2100)
No full textGlaciers in the Chandra–Bhaga basin, western Indian Himalaya, are critical to the cryosphere–hydrosphere system, yet their long-term climate responses remain poorly understood due to sparse in-situ data. Our geodetic mass balance assessment reveal substantial ice loss from 1971 to 2022, with glaciers shrinking by 0.72 ± 0.08 km2 a−1 and losing mass at 0.26 ± 0.10 m w.e. a−1. Debris-covered glaciers experienced greater ice loss (0.28 ± 0.10 m w.e. a−1) than clean-ice glaciers (0.20 ± 0.12 m w.e. a−1). CMIP6-based regression indicates modest pre-2000 loss, then average loss rates of −0.5 m w.e. a−1 until ∼2035, after which trajectories diverge depending on SSP scenarios. Temperature sensitivity is strongest in summer (−0.49 m w.e. a−1 °C−1) and weakest in winter (−0.38 m w.e. a−1 °C−1). Precipitation sensitivity is highest for winter and lowest for summer. ERA5 Land reanalysis-based sensitivities show annual temperature has stronger influence than seasonal, with lower magnitudes than CMIP6. Winter precipitation from ERA5 Land reanalysis data show stronger correlation to glacier mass gain compared to CMIP6. These differences emphasize uncertainty over which dataset better represents regional climate, particularly for temperature–mass balance relationships and winter precipitation that largely governs glacier accumulation. Despite this, sensitivities align with broader Himalayan trends. Projections suggest stable winter precipitation, combined with increased summer and annual warming, will accelerate mass loss through the 21st century. This study proves that long-term geodetic data can provide an alternative solution to understand glacier–climate interactions in data-scarce regions such as the Himalaya, enabling reconstructions, forecasts, and targeted adaptation for glacier-dependent communities.Science of The Total Environmen
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 mimics the on-axis structure of the eye, consisting of a lens tube holding a pair of windows surrounded on either side by fluid chambers and sealed at the far end by a diffuse black metallic plug. Sucrose solutions with a range of concentrations were injected into the fluid chambers, providing a linear variation in refractive index of 1.3330 to 1.4416. The instrument was used to simultaneously 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. Although the measurement accuracy is shown to decrease with reduced refractive index contrast, it remains better than 0.6% over all measured components for sucrose concentrations of up to 60.3%, which is close to the saturation limit.Engineering and Physical Sciences Research Council (EP/M010473/1, EP/H02252X/1)Optics Expres
Industry 4.0 technologies adopted by logistics companies for sustainability and competitiveness
No full textDue to the emergence of Industry 4.0 and 5.0, numerous emerging technologies such as artificial intelligence (AI), the Internet of Things (IoT), digital twins, blockchain, and autonomous vehicles are increasingly impacting various aspects of the global logistics industry, which is undergoing significant transformation. These technologies can potentially enhance operational efficiency, reduce costs, improve customer satisfaction, and foster a more responsive and sustainable logistics environment. However, their adoption presents considerable challenges, particularly for third-party logistics (3PL) companies in highly competitive markets like the United Kingdom and China. These challenges include high implementation costs, data security risks, regulatory compliance issues, and the need for the workforce to adapt to new technological environments. This study provides a comparative analysis of the application and impact of these technologies on 3PL companies in the United Kingdom and China. Drawing on insights from logistics experts using the best–worst method, the research identifies best practices and offers strategic recommendations for optimizing technology integration within different market contexts. The research findings indicate that digital twins have the greatest impact on 3PL companies. Additionally, the study explores the varying degrees of influence that different technologies exert on various areas within 3PL companies. This study contributes to a deeper understanding of how emerging technologies can be effectively integrated into the operations of 3PL companies to enhance competitiveness and operational efficiency while also considering regional differences in market conditions, regulatory environments, and technological implementation, paving the way for further in-depth research.Industry 4.0 and Sustainability: Integrating Digital Technologies and Circular Models for a Sustainable Futur
Positive sentiments in early academic literature on DeepSeek: a cross-disciplinary mini review
No full textDeepSeek is a free and self-hostable large language model (LLM) that recently became the most downloaded app across 156 countries. As early academic literature on ChatGPT was predominantly critical of the model, this mini-review is interested in examining how DeepSeek is being evaluated across academic disciplines. The review analyzes available articles with DeepSeek in the title, abstract, or keywords, using the VADER sentiment analysis library. Due to limitations in comparing sentiment across languages, we excluded Chinese literature in our selection. We found that Computer Science, Engineering, and Medicine are the most prominent fields studying DeepSeek, showing an overall positive sentiment. Notably, Computer Science had the highest mean sentiment and the most positive articles. Other fields of interest included Mathematics, Business, and Environmental Science. While there is substantial academic interest in DeepSeek’s practicality and performance, discussions on its political or ethical implications are limited in academic literature. In contrast to ChatGPT, where all early literature carried a negative sentiment, DeepSeek literature is mainly positive. This study enhances our understanding of DeepSeek’s reception in the scientific community and suggests that further research could explore regional perspectives.Frontiers in Artificial Intelligenc
BEARING-FDD: an early detection and diagnosis tool for bearing faults in rotating machinery
No full textRefers to: Explainable and interpretable bearing fault classification and diagnosis under limited data, https://dspace.lib.cranfield.ac.uk/handle/1826/23234This paper presents the design and implementation of a web tool offering an innovative method for detecting, diagnosing and classifying bearing faults in rotating machinery under limited data conditions, providing explainability and interpretability of the results obtained. The tool uses a machine learning model to detect and diagnose bearing faults. A monotonic smoothed stacked autoencoder builds a health indicator without requiring feature extraction, making the tool useful without the need for specialized staff. The tool generates explainability and interpretability reports with a correlation analysis between the health indicator and well-known engineering features and easily interpretable details on the diagnosed faults. The tool includes the option to use preloaded state-of-the-art datasets, while also allowing users to upload their own datasets to analyze vibration data from real industrial equipment.This research was partially funded by the Spanish National Plan of Research, Development, and Innovation under project EDNA (PID2021-124383OB-I00), the European Union, the University of Oviedo and the University of Cranfield.Software Impact
A novel LLM-AI based automation framework for high frequency wireless power transfer design
No full textDesigning high frequency wireless power transfer (WPT) systems typically involve complex modeling, simulation, and prototyping steps that demand significant time and expertise. In this study, we adopted and extended a previously proposed large language model (LLM) based design framework to accelerate design process. Seven customized generative pretrain transformers (GPT) based agents were developed using specific design guidance in WPT design to improve the accuracy of the generated outputs. A total of seven generated designs proposed by each agent were evaluated using the same design prompt. The generated designs were compared against four mathematical models based on design accuracy, completeness, and design time. The most accurate design was validated using PSIM and 3D Ansys simulation. The results demonstrate the potential of LLM-driven workflows to significantly reduce design effort and time while maintaining high reliability in WPT system development.This work is funded by QBYSS (Formerly Energy Research Lab (ERL)) and Cranfield University2025 IEEE 7th International Conference on Computing, Communication and Automation (ICCCA
Data for Modelling and Analysis of Induction Preheating of Moving Filler Wire in Wire-Based Directed Energy Deposition
No full textTemperature; relative magnetic permeability electrical conductivity density heat capacity at constant pressure; thermal conductivity; wire feed speed; wire feed length; current; frequency wire diameter; radial distance; coupling distance; Coil-wire diameter; coil pitch; coil turn; power; energy density magnetic field intensity magnetic flux; current density; skin depthInduction preheating of filler wire is a novel technique that allows precise control of wire temperature before melting by main energy source in a wire-based Directed Energy Deposition (wire-DED). This which can enhance deposition rate and reduce defects. This study investigates the evolution of electromagnetic and thermal fields during induction heating in a moving filler wire through a coil. An electromagnetic-thermal model was developed to determine the magnetic flux, eddy current, temperature, and energy absorption efficiency. A multiphysics model was validated by experiments under diverse process conditions. The model allows understanding of electromagnetic-thermal mechanism for the transient and steady-state distributions of the wire temperature.
In the steady state, the peak temperature is located immediately outside the coil exit end of the inductor coil, and the temperature gradient across the wire diameter is marginal. The sensitivity analysis to establish most important parameters was carried out This study demonstrates an effective modelling approach to induction heating of moving wire and provides critical insights for designing and optimising the induction coil and process for preheating of wires for additive manufacturing and other similar manufacturing processes (e.g. welding and cladding).Innovate U
Multi-fidelity gaussian process for uncertainty quantification in aerodynamic analysis
No full textThis research contributes to the AIAA FD UQ Discussion Group’s Challenge Problem, with an aim to evaluate aerodynamic coefficients and their total uncertainty for an NACA 2412 airfoil. In this research, the problem is tackled with Multi-fidelity Gaussian Process (MFGP), where experimental data and XFOIL predictions are considered as two levels of fidelity. The total uncertainty is quantified as the MFGP’s predicted variance. Three training approaches are implemented and tested repeatedly. According to the results, MFGP can effectively capture the nominal values of the aerodynamic coefficients with very sparse experimental data. However, the total uncertainty is underestimated. This is potentially due to the limitation of the maximum likelihood method for estimating the MFGP’s hyperparameters.The research leading to these results has received funding from the Innovate UK, Aerospace Technology Institute (ATI) in the UK, under the Out of Cycle NExt generation highly efficient air transport (ONEheart) project (Ref no. 10003388).AIAA SCITECH 2026 Foru