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Acclimatisation of Fusarium langsethiae, F. poae and F. sporotrichioides to elevated CO2: impact on fungal growth and mycotoxin production on oat-based media
Get PDFOats are highly susceptible to infection by Fusarium species, especially F. langsethiae, F. poae and F. sporotrichioides which contaminate the grain with mycotoxins. Climate change is expected to affect fungal colonisation and associated mycotoxin production. The objective of this study was to examine the effect of acclimatisation to elevated CO2 on the growth and mycotoxin production capacity of these fungal species. Strains of F. langsethiae (FL; seven strains), F. poae (FP; two strains) and F. sporotrichioides (FS; one strain) were acclimatised by sub-culturing for 10 generations at either 400 or 1000 ppm CO2 under diurnal temperature conditions. At each sub-culturing, the effect of acclimatisation to elevated CO2 on (a) lag phase prior to growth, (b) growth rate on oat-based media was assessed. Additionally, the production of type A trichothecenes and related toxic secondary metabolites of sub-cultures after 1, 7 and 10 generations were assessed using LC-MS/MS qTRAP. The results showed that Fusarium strains had an increased lag time and growth rate in response to the combined effect of sub-culturing and elevated CO2 levels. T-2 + HT-2 production was affected by elevated CO2 in strain FL4 (7.1-fold increase) and a decrease in strain FL1 (2.0-fold decrease) at the first sub-culturing and FS (1.3-fold decrease) after 7 sub-cultures compared to ambient conditions. The effect of sub-culturing on T-2 + HT-2 production varied depending on the fungal strain. For strain FL4, significantly less T-2 + HT-2 toxins were produced after 10 generations (4.4-fold decrease) as compared to that under elevated CO2 conditions after one sub-culture, and no change was observed under ambient conditions. The FS strain showed significant stimulation of T-2 + HT-2 toxin production after 10 sub-cultured generations (1.1-fold increase) compared to the initial sub-culture of this strain under elevated CO2 conditions. The production of other toxic secondary metabolites was generally not impacted by elevated CO2 conditions or by sub-culture for 10 generations, with the exceptions of FL1 and FP1. FL1 produced significantly more neosolaniol after 10 generations, when compared to those after 1 and 7, regardless of the CO2 conditions. For FP1, elevated CO2 significantly triggered beauvericin production after an initial sub-culture when compared to ambient conditions at the same sub-culture stage (29-fold). FP1 acclimatisation to elevated CO2 led to a decrease of beauvericin production after 10 generations when compared to 1 (6-fold). In contrast, sub-culturing for 10 generations compared to 1 under ambient CO2 conditions resulted in an increase in this toxin (12-fold).Biotechnology and Biological Sciences Research Council (BBSRC): BB/P001432/1International Journal of Food Microbiolog
Fireside performance of different coatings in biomass power plant
Get PDFThe energy sector will need to employ novel strategies to reduce greenhouse gas emissions, such as the increase of steam temperatures/pressures or the use of low carbon fuels (i.e. biomass). Both cause heat exchanger materials’ degradation issues, due to the formation of more/different corrosive deposits, which requires the use of expensive nickel-based materials or coatings. This paper focuses on the behaviour of three different coatings (HVOF NiCrFeSi, laser clad FeCrAl and Laser Clad NiCrFeSi) deposited on TP347HFG, at 700°C (up to 1000 h exposure). Tests were performed using the ‘deposit recoat’ method (KCl deposit) in simulated biomass combustion environments. Cross-sections were analysed using dimensional metrology, to determine distributions of metal loss and internal damage. Intergranular damage and pits were identified using SEM/EDX. A ‘diffusion cell’ behaviour was observed, which led to depletion of alloying elements from the coating and consequent increase in damage. The results suggested a severe degradation of all coatings.European Union funding: RFCS-2015/709954Materials at High Temperature
Conclusion: If on a winter’s night a ship wrecks
No full textContemporary Philosophy for Maritime Archaeology has sought to address two aims, as outlined in the introduction. First, “to help usher the alienated subdiscipline of maritime archaeology into the broader discourse of the humanities” and, second, “to issue a call to action for fellow maritime archaeologists to respond to ecocide more urgently, more cross disciplinarily, and more responsibly with new interventions into old research questions” (Rich and Campbell, this volume). The extent to which these aims are successful – measured by engagement of the field with the ideas proposed in this volume – remains to be seen, but this concluding chapter reviews the contributions and responses to tease out the recurrent themes within the chapters that further these lofty aims
Review of in silico models of cerebral blood flow in health and pathology
Get PDFIn this review, we provide a summary of the state-of-the-art in the in silico modelling of cerebral blood flow and its application in in silico clinical trials. Cerebral blood flow plays a key role in the transport of nutrients, including oxygen and glucose, to brain cells, and the cerebral vasculature is a highly complex, multi-scale, dynamic system that acts to ensure that supply and demand of these nutrients are continuously balanced. It also plays a key role in the transport of other substances, such as rt-PA, to brain tissue. Any dysfunction in cerebral blood flow can rapidly lead to cell death and permanent damage to brain regions, leading to loss of bodily functions and death. The complexity of the cerebral vasculature and the difficulty in obtaining accurate anatomical information combine to make mathematical models of cerebral blood flow key in understanding brain supply, diagnosis of cerebrovascular disease, selection of the optimum intervention, and neurosurgical planning. Similar in silico models have now been widely applied in other body organs, but models of cerebral blood flow lag far behind. The increased availability of experimental data in the last 15 years however has enabled these models to develop more rapidly and this progress is the focus of this review. We thus present a brief review of the cerebral vasculature and the mathematical foundations of cerebral blood flow. We demonstrate how such models can be applied in the context of cerebral diseases and show how this work has recently been expanded to in silico trials for the first time. Most work to date in this context has been performed for ischaemic stroke or cerebral aneurysms, but in-silico models have many other applications in neurodegenerative diseases where mathematical models have a vital role to play in testing hypotheses and providing test beds for clinical interventions.Progress in Biomedical Engineerin
An investigation between the links of sustainable manufacturing practices and innovation
Get PDFWithout a clear sustainability agenda in place, manufacturing companies would typically aim for products with incremental improved performance for their customers or introducing novel and innovative products and services that appeal to their target audience. However, mounting pressures to improve environmental performance indicators (carbon emissions, waste) require companies to think about step change improvements in how they operate and manage energy and resources. Companies trying to make their products with less energy and materials (doing more with less) are likely to face, or must address, operational and systemic changes in order to achieve better outcomes for the environment or ESG targets. Part of the issue at hand is the complexity and effort required to design effective changes with positive environmental outcomes and minimal disruption for on-going operations and manufacturing performance. In this article the authors aim to extend and explore the utility of a novel maturity model for eco-efficiency into innovation processes and practices and better understand the implications for theory and practice.30th CIRP Life Cycle Engineering Conference 2023Procedia CIR
CNN-fusion architecture with visual and thermographic images for object detection
Get PDFMobile robots performing aircraft visual inspection play a vital role in the future automated aircraft maintenance, repair and overhaul (MRO) operations. Autonomous navigation requires understanding the surroundings to automate and enhance the visual inspection process. The current state of neural network (NN) based obstacle detection and collision avoidance techniques are suitable for well-structured objects. However, their ability to distinguish between solid obstacles and low-density moving objects is limited, and their performance degrades in low-light scenarios. Thermal images can be used to complement the low-light visual image limitations in many applications, including inspections. This work proposes a Convolutional Neural Network (CNN) fusion architecture that enables the adaptive fusion of visual and thermographic images. The aim is to enhance autonomous robotic systems’ perception and collision avoidance in dynamic environments. The model has been tested with RGB and thermographic images acquired in Cranfield’s University hangar, which hosts a Boeing 737-400 and TUI hangar. The experimental results prove that the fusion-based CNN framework increases object detection accuracy compared to conventional models.SPIE Defense + Commercial Sensing: Thermosense: Thermal Infrared Applications XLV, 202
Supply chain 4.0: a machine learning-based Bayesian-optimized lightGBM model for predicting supply chain risk
Get PDFIn today’s intricate and dynamic world, Supply Chain Management (SCM) is encountering escalating difficulties in relation to aspects such as disruptions, globalisation and complexity, and demand volatility. Consequently, companies are turning to data-driven technologies such as machine learning to overcome these challenges. Traditional approaches to SCM lack the ability to predict risks accurately due to their computational complexity. In the present research, a hybrid Bayesian-optimized Light Gradient-Boosting Machine (LightGBM) model, which accurately forecasts backorder risk within SCM, has been developed. The methodology employed encompasses the creation of a mathematical classification model and utilises diverse machine learning algorithms to predict the risks associated with backorders in a supply chain. The proposed LightGBM model outperforms other methods and offers computational efficiency, making it a valuable tool for risk prediction in supply chain management.Machine
Challenges to Lean 4.0 in the pharma supply chain sustainability
Get PDFThe Pharma sector is increasingly under pressure to improve the sustainability of supply chains, as consumers and regulators require greater transparency, efficiency, and accountability. Although Lean 4.0 has created a lot of buzz in the organization, the pharmaceutical sector faces challenges in implementing it. The main goal of this literature review is to identify the challenges of Lean 4.0 for the sustainability of the pharma supply chains (PSCs). A series of papers extracted from the most relevant scientific databases, including the Web of Science, Scopus, Google Scholar, and ProQuest was analyzed and synthesized from 2007 to 2022. 31 articles were used in the study. The findings of the study indicate that challenges include financial, staff experience and specialization, ongoing maintenance, resources for generating new skills and experiences, employees, and partners’ resistance to changes in regulations, and cyber-hacking of key information. The results will facilitate future work by practitioners and researchers and make an important contribution to existing knowledge.32nd International Conference on Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems (FAIM 2023)Lecture Notes in Mechanical Engineerin
Formulation and solution technique for agricultural waste collection and transport network design
Get PDFAgricultural waste management in developing countries has become a challenging issue for rural planners due to the lack of an efficient planning tool. In the countries, farmers burnt agricultural waste at fields after each harvesting season to solve the issue. As a result, it has caused air and water pollution in the rural areas of the countries. In this paper, we present a mixed-integer nonlinear programming model for agricultural waste collection and transport network design that aims to stop burning waste and use the waste to produce bio-organic fertilizer. The model supports rural planners to optimally locate waste storages, and to determine the optimal set of routes for a fleet of vehicles to collect and transport the waste from the storages to the bio-organic fertilizer production facility. In the novel location-assignment-routing problem, the overall objective is to minimize total cost of locating storages, collecting waste from fields and planning vehicle routes. A solution technique is developed to linearise the mixed-integer nonlinear programming model into a model in linear form. In addition, a parallel water flow algorithm is developed to solve efficiently the large-sized instances. The efficiency of the proposed model and algorithm is validated and evaluated on the real case study in Trieu Phong district, Quang Tri province, Vietnam, as well as a set of randomly generated large-sized instances. The results show that our solution approach outperforms the general optimisation solver and tabu search algorithm. Our algorithm can find the optimal or near-optimal solutions for the large-sized instances within a reasonable time.European Journal of Operational Researc
Thermal fluid dynamics of the effect of filler wire on deposition rate and bead formation intending plasma arc-based DED
Get PDFThe influence of filler wire configuration, such as size and geometry, on the deposition rate (DR) and bead formation, has been studied in wire arc-based directed energy deposition (WADED), but the fundamental physics underlying its effect on wire melting and melt pool dynamics remains unclear. In this paper, a series of plasma arc-based DED (plasma-DED) experiments were conducted to investigate the impact of five different filler wire configurations on DR and bead dimensions. The coupling behaviours of wire melting, metal transfer and melt pool dynamics under the five filler wire configurations were also simulated numerically using the authors' recently developed wire-feeding model. The calculated wire melting and bead cross-sections are consistent with the experimental images and measurements. The results demonstrate that the filler wire significantly affects the highest DR by altering wire melting and metal transfer behaviours through changes in arc energy absorption. The filler wire with a rhombus geometry which is closer to a Gaussian-like arc distribution than the flat wire was shown to get higher DR and more stable metal transfer. Furthermore, different filler wire configurations lead to distinct melt pool behaviours, including temperature distribution and flow velocity, due to various metal transfer behaviours and arc shading effects. This study sheds light on the fundamental physics underlying the impact of filler wire on wire melting and bead formation for the first time. The methods and findings can guide improving DR and controlling bead shape in the plasma-DED process.Journal of Manufacturing Processe