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A critical review of conventional and emerging technologies for the detection of contaminants, allergens and adulterants in plant-based milk alternatives
Get PDFThe increasing popularity of plant-based milk alternatives (PBMAs) necessitates effective safety and authentication measures to ensure food product integrity and maintain consumer trust. This review aims to offer a comprehensive overview of potential contaminants, allergens, and adulterants in PBMAs, and the analytical methodologies employed for their detection and quantitation. It details the advantages and limitations of widely employed testing techniques, such as chromatography, spectroscopy, immunoassays and PCR. In addition, it explores recent advancements in portable detection methods based on novel technologies such as CRISPR and biosensor systems that offer new opportunities for rapid and precise analysis. Despite these technological innovations, important challenges remain, particularly in optimizing sample preparation protocols and improving DNA-based methods efficiency. The integration of multiple detection strategies and the development of rapid, cost-effective analytical tools are critical steps towards enhancing both industry compliance and consumer confidence. Furthermore, green analytical methods — such as solvent-free extraction, AI-driven spectroscopy, and sustainable sample preparation techniques — pave the way toward eco-friendly and more efficient PBMA safety testing.Biotechnology and Biological Sciences Research Council (BBSRC)This research was funded by UKRI BBSRC FoodBioSystems Doctoral Training Partnership (DTP), grant number BB/T008776/1.Current Research in Food Scienc
Thermodynamic and combustion characteristics of ultra-high performance engines for motorsport applications
Get PDFTemple, Clive - Associate SupervisorUsing steady-state and transient one-dimensional gas dynamic engine models
developed with AVL Boost™, critical assessment of the performance characteristics
of the current 2014+ Formula One™ engine and of the future 2026 Formula One™
Power Unit are investigated.
For the 2014+ regulations, a Digital Twin, aiming at replicating the trends of the real
engine despite a lack of component-level-detail data, is created and used to
scientifically explain how this engine achieves 50+% brake fuel conversion efficiency
and to rank the contribution of each enabling technologies (high compression ratio,
lean combustion, passive pre-chamber, direct injection, asymmetric valve profiles,
MGU-H and waste gates used as pressure-relief valves).
The impact of the 2026 Formula One™ Power Unit regulations on engine performance
is investigated and highlights that the reduction in fuel flow will not only result in the
obvious reduction in power output but also in in-cylinder pressure which introduces
opportunities for enhanced combustion process and higher air/fuel ratios.
Nevertheless, with the high MGU-K power, both the 2014+ and 2026 Power Units are
predicted to have similar peak output power despite an advantage at low speed for the
2026 regulations thanks to the capacity of electric motor to produce torque at low
speed. Using transient simulations, the impact of the removal of the MGU-H in the
2026 regulations is assessed and an anti-lag solution using the MGU-K called torque
consuming is investigated. It is demonstrated that always operating the engine at full
load during acceleration phases and using the MGU-K to absorb the excess power
compared to the power demand / to control the amount of power delivered to the
wheels helps to reduce turbo lag, improve engine efficiency, and reduce the need for
the MGU-K to torque fill, but at the expense of a higher fuel consumption.PhD in Transport System
Dynamic distributional effects of fiscal consolidation: a sample of 16 OECD countries
Get PDFWe explore the long-term distributional consequences of fiscal adjustment episodes and the dynamic consequences of fiscal consolidation for countries with large sized consolidations vis-a-vis countries with small sized consolidations. In this direction, panel ARDL and impulse response functions using local projections are adopted for a panel of 16 OECD countries covering the period 1980 to 2019 based on a newly updated fiscal adjustment dataset, compiled by Gustavo Adler et al. (2024). The evidence suggests that adverse income disparities which tend to arise upon implementation of fiscal adjustments are dynamic and persist through the long run. While baseline results for the Gini suggest that long-term inequality levels hold at approximately the same as peak levels (by the 7th period), inequality measured by the bottom 40 income share appear to exhibit peak levels at the 14th period, suggesting a more persistent impact. Disaggregating impact by adjustment size, evidence is also offered for small-sized adjustment and large-sized adjustment countries showing that small-sized adjustments lead to gradual but prolonged inequality effects, while large-sized adjustments generate steeper but shorter-lived inequality increases.Panoeconomicu
Visualising volatility, uncertainty, complexity, and ambiguity (VUCA)
Get PDFCollins, Matt - Associate Supervisor
Ünal, Enes - Associate SupervisorOrganisations continue to face a range of existential threats from increasing turbulence in their operating environment. The nature of the turbulence is multifaceted, from global pandemics and financial crashes to military conflicts and climate changes, requiring strategic decision makers to craft tailored responses to ensure not just survival, but ideally to take advantage of the emerging situation for competitive advantage. Achieving these aims however is not straightforward, for in order to respond, data and insight is required, along with new decision-making tools, to enable improved understanding and visualisation.
VUCA, an acronym for volatility, uncertainty, complexity, and ambiguity, has emerged as a way to frame this turbulent environment helping make sense of it. VUCA events, such as the COVID-19 pandemic, can form quickly and have far reaching impacts across organisations and whole economies. A growing body of research continues to examine how organisations improve, adapt and change internally to respond to VUCA and the capabilities they need to survive and thrive.
This thesis examines the other side of the equation, seeking to make sense of VUCA events forming in the turbulent environment. Crafting a response would be greatly aided by a more robust understanding of the VUCA events themselves, an area under researched as of today, with VUCA often used as shorthand to encapsulate challenging operating conditions, but with little exploration to understand what signals it provides.
In particular, this research aims to show VUCA visualised for arguably the first time. Through hundreds of individual surveys generating thousands of data points, this research explored how VUCA was experienced, its epistemological roots, and developed a visual decision-making toolset (Figure 1) to understand when external turbulence is occurring, disseminating this to hundreds of peers through lectures and industry presentations.
Figure 1 – VUCA Scorecard and BreachMaPhD in Desig
Hydrogen bond enhanced electrochemical hydrogenation of benzoic acid over a bimetallic catalyst
No full textElectrochemical hydrogenation (ECH) is a sustainable alternative to traditional hydrogenation methods, offering selective reduction of organic compounds under mild conditions. This study investigates the co-hydrogenation of benzoic acid (BA) and phenol on a platinum-ruthenium on activated carbon cloth (PtRu/ACC) catalyst, with a focus on the synergistic effects arising from hydrogen bonding. Density Functional Theory (DFT) calculations reveal that the formation of a hydrogen-bonded complex between BA and phenol facilitates adsorption energy and lowers activation barrier energies compared to BA alone. Experimental results demonstrate that a 20 mM BA and 5 mM phenol mixture achieves the highest conversion rate (87.33%) and faradaic efficiency (63%), significantly outperforming single-compound systems. Notably, co-hydrogenation facilitates the reduction of BA to cyclohexanemethanol, a valuable product for biofuel applications, which has reduced corrosiveness and improved energy density. These findings underscore the potential for optimising multi-compound ECH systems through targeted catalyst design and reagent concentration tuning, thus advancing the development of efficient strategies for bio-oil upgrading and sustainable chemical production.Engineering and Physical Sciences Research Council (EPSRC)The authors wish to thank UK EPSRC (EP/T518104/1) for supporting the work published in the paper through an EPSRC Doctoral Training Partnership Funding.Sustainable Energy & Fuel
Large language models powered system safety assessment: applying STPA and FRAM
Get PDFThe advancement of large language models (LLMs) shows immense promise in many domains. However, their reliability is still questionable. This study aims to comparatively examine the performance of ChatGPT and Gemini in conducting a stand-alone systems-based risk assessment using System-Theoretic Process Analysis (STPA) and the Functional Resonance Analysis Method (FRAM). Our findings revealed that both LLMs demonstrated weaknesses in their analyses, with ChatGPT generally outperforming Gemini regarding response comprehensiveness and adhering to the prompted format. Specifically, LLMs failed to use systems thinking in their stand-alone applications and failed to follow up on previous prompt outputs. While LLMs can provide substantial amounts of information quickly, the effectiveness of LLMs in system safety assessment is contingent on addressing their limitations and implementing strategies to improve their capabilities.Safety Scienc
Influence of nitrogen, phosphorus and sulfur concentration on bioplastic and FAMEs production in Scenedesmus sp.
Get PDFMicroalgae can fix CO2 into valuable molecules; these products are expected to sustainably replace petroleum-derived plastics and oils. The biorefinery strategies are implemented to simultaneously generate high-value products by extracting a fraction of the microalgae. This research evaluated the growth kinetics of Scenedesmus sp. under different macronutrients such as nitrogen, phosphorus, and sulfur (low, medium and high concentration). The correlation of cell growth behavior with biochemical characterization was determined with emphasis on the potential of the microalgae to use as raw material for bioplastic production. The results showed. that a high sulfur concentration has influenced and increased the cell growing reaching 1.42 g L^−1 at 15th cultivation day compared with the nitrogen and phosphorus variation. The content of carbohydrates in the biomass was enhanced under limitation of nitrogen and phosphorus. The same statement was proved for the PHA accumulation reaching 9.5 ± 0.83 % and 6.4 ± 0.32 % DW (dry weight) respectively. Deprivation of nitrogen, phosphorous and sulfur improved the microalgae culture for lipids production. In the present study the higher lipid content (273 mg g^−1) was observed under phosphorus limitation. The monitoring of FAMES considering former condition in the medium revealed high content of palmitic [30 ± 1.5 %TFA], stearic (C18:0) [1.8 ± 0.11 %TFA], and oleic acid. Therefore, these results lead to observing the impacts of macronutrients (N, P, S) on the microalgae growth and determined the better conditions for the accumulation of macromolecules that can be extracted under biorefinery concepts valorizing the biomass for bioplastic production and other biomolecules for future industrial applications.Consejo Nacional de Humanidades, Ciencias y Tecnologías (ACONHCYT) supported partially this study under PhD scholarship to IYL-P[CVU: 859227] and Sistema Nacional de Investigadores [SNI] program awarded to EMM-M [CVU: 230784], GM-M [CVU: 490688], JRR [CVU:445590] and RP-S [CVU: 35753].TEC funded this work by the project "Exploring and optimizing CO2 bio fixation process for microalgae lipids production to formulate green metalworking fluids - for cleaner manufacturing processes" (ID: I023 - IAMSM002 - C4-T2 - E). Tec Challenge-based projects call 2022.Heliyo
An advanced performance-based method for soft and abrupt fault diagnosis of industrial gas turbines
Get PDFIntegrating gas turbines with intermittent renewable energy must operate for prolonged periods under transient conditions. Existing research on fault diagnosis in such systems has concentrated on the primary rotating components in steady-state conditions. There is a gap in investigating the interplay between shaft bearing failure and performance metrics, as well as fault identification under transient conditions. This study aims to identify faults not only in the main rotating components but also in the shaft bearings under transient conditions. Firstly, the performance model and fault propagation model of gas turbines are established, and the influence of bearing fault on the whole engine performance is analysed. Then, the fault diagnosis method is determined and the dynamic effects are compensated in fault identification at each time interval. Finally, the steady-state and transient fault diagnosis are carried out considering the constant and sudden faults for the main rotating components and bearings. The average run time and maximum error during the engine life cycle are 0.1064 s and 0.0086 %. For the proposed dynamic effects compensation method, the average computation time and peak error at every moment are 0.1152 s and 0.0143 %, clearly superior to the benchmark method. These results provide evidence that the proposed method can correctly diagnose the fault of the main rotating components and shaft bearings under transient conditions. Therefore, the findings mark an advancement in real-time fault diagnostic techniques, ultimately enhancing engine availability while upholding secure and affordable energy production.The authors acknowledge the support of the Propulsion and Space Research Center (PSRC) of the Technology Innovation Institute (TII) in Abu Dhabi, United Arab Emirates.Energ
A reliability-oriented framework for the preservation of historical railway assets under regulatory and material uncertainty
Get PDFPreserving historical railway assets presents a complex systems challenge, in which uncertainties in material performance, structural degradation, and regulatory requirements directly impact long-term reliability and operational continuity. Traditional maintenance practices often limit the use of modern materials, introducing inefficiencies, increased lifecycle costs, and higher failure risk due to material ageing and environmental exposure. This study proposes a reliability-informed preservation framework that supports the integration of contemporary materials into historical railway infrastructure while accounting for legal, material, and procedural uncertainties. The framework is validated through two industrial case studies, each reflecting different regulatory and operational constraints. The first case demonstrates the successful substitution of timber with certified PVC cladding on a non-listed signal box, achieving improved durability, reduced maintenance intervals, and enhanced system reliability. The second case explores an unsuccessful attempt to replace decayed timber gables with aluminium, in which late-stage planning misalignment, underestimated risks, and uncertainty in approval outcomes led to a significant cost increase and reduced reliability regarding delivery. By systematically applying and evaluating the framework under real-world conditions, this research contributes to engineering asset management by introducing a structured method for mitigating regulatory and material uncertainties.Applied Science
CFAR detection in heterogeneous K-distributed sea-clutter background
Get PDFDetection of targets at sea is challenging due to unwanted echo returns from the sea surface, i.e. sea clutter returns. To account for the undesired effects due to sea clutter at the receiver, and to control the probability of detection and false alarm, the K-distribution has often been used to provide a promising statistical fit to real clutter data. However, controlling the performance of the receiver becomes very complicated in heterogeneous clutter, that is when there is a sudden transition from one clutter region to another with a change in shape and/or scale distribution parameters. A possible solution to this is to use some prior information on the sea clutter characteristics to generate clutter maps that inform adaptive detection solutions. This prior information can be obtained by the radar in real time (or close to real time) using oceanographic models, statistical clustering, or potentially Artificial Intelligence.This paper presents our first step in this direction by investigating detection in heterogeneous fully correlated K-distributed sea clutter. A transition line between homogeneous clutter regions is estimated using the statistical parameters of the K-distribution, to avoid polluting the training windows of a Constant False Alarm Rate (CFAR) detector with non-representative data. The transition cells assist to resolve the heterogeneous clutter into small homogeneous clutter regions and for every homogeneous region a CFAR detector is designed according to the K-distribution shape parameter. Results are obtained and presented for simulated data as well as for real sea clutter data provided by Hensoldt UK.The authors thank Hensoldt UK and Cranfield University for jointly funding this PhD programme under the Cranfield Industrial Partnership PhD Scholarships Scheme (CIPPS)2024 International Radar Conference (RADAR