13,725 research outputs found

    Differential responses in some quinoa genotypes of a consortium of beneficial endophytic bacteria against bacterial leaf spot disease

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    Many effective plant-microbe interactions lead to biological changes that can stimulate plant growth and production. This study evaluated the effect of the interaction between quinoa (Chenopodium quinoa Willd.) and endophytic bacterial strains on differential responses under biotic stress. Four strains of endophytic bacteria were used to inoculate three quinoa genotypes. Endophytic bacteria, isolated from the endosphere of healthy genotypes of quinoa plants, were used to evaluate their biocontrol activity against Pseudomonas syringae on quinoa plants, which causes leaf spot disease, depending on some different parameters. Quinoa genotype plants were treated with four treatments: pathogenic bacteria only (T1), internal bacteria only (T2), pathogenic bacteria + endogenous bacteria (T3), and untreated as the control (T4). The results indicated that there was a significant difference between chlorophyll content index of infected plants without bioagent (untreated) compared to plants bio-inoculated with endophytic bacteria. The highest mean disease incidence was on the plants without bacterial inoculum (90, 80, and 100%) for quinoa genotypes G1, G2, and G3, respectively. The results showed that there were significant differences in the weight of grains/plant, as the value ranged from 8.1 to 13.3 g when treated with pathogens (T1) compared to the treatment with pathogens and endogenous bacteria (T3), which ranged from 11.7 to 18.6 g/plant. Decreases in total aromatic amino acids appeared due to the pathogen infection, by 6.3, 22.8, and 24.1% (compared to the control) in G1, G2, and G3, respectively. On the other hand, genotype G3 showed the highest response in the levels of total aromatic and total neutral amino acids. The endophytic strains promoted quinoa seedling growth mainly by improving nutrient efficiency. This improvement could not be explained by their ability to induce the production of amino acids, showing that complex interactions might be associated with enhancement of quinoa seedling performance by endophytic bacteria. The endophytic bacterial strains were able to reduce the severity of bacterial leaf spot disease by 30, 40, and 50% in quinoa genotypes G1, G2, and G3, respectively, recording significant differences compared to the negative control. The results indicated that, G1 genotype was superior in different performance indicators (pathogen tolerance index, yield injury %, superiority measure and relative performance) for grain weight/plant under pathogen infection condition when treated with endophyte bacteria. Based on this study, these bacterial strains can be used as a biotechnology tool in quinoa seedling production and biocontrol to diminish the severity of bacterial leaf spot disease

    Certified high-efficiency "large-area" perovskite solar cells module for Fresnel lens-based concentrated photovoltaic

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    This is the author accepted manuscript. The final version is available on open access from Cell Press via the DOI in this recordData availability: All data generated or analysed during this study are included in the Supplementary Information article and its data source. Source data are provided in this paper. All data reported in this paper will be shared by the lead contact upon request.The future of energy generation is well in tune with the critical needs of the global economy, leading to more green innovations and emissions-abatement technologies. Introducing concentrated photovoltaic (CPV) is one of the most promising technologies owing to its high photo-conversion efficiency (PCE). While most researchers use silicon and cadmium telluride for CPV, we investigate the potential in nascent technologies, such as perovskite solar cell (PSC). This work constitutes a preliminary investigation into a ‘large-area’ PSC module under a Fresnel lens (FL) with a ‘refractive optical concentrator-silicon-on-glass’ base to minimise the PV performance and scalability trade-off concerning the PSCs. The FL-PSC system measured the solar current-voltage characteristics in variable lens-to-cell distances and illuminations. A systematic study of the PSC module temperature was monitored using the COMSOL transient heat transfer mechanism. The FL-based technique for ‘large-area’ PSC architecture is an unfolded technology that further facilitates the potential for commercialisation.Engineering and Physical Sciences Research Council (EPSRC)Valais Energy Demonstrators FundEuropean Union Horizon 2020Deputyship for Research & Innovation, Ministry of Education, Saudi Arabi

    Fuentes externas de información para el desarrollo de innovaciones: un análisis de la evidencia en Europa y España

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    p. 181-238La creciente complejidad y dinamismo que caracterizan los entornos actuales han obligado a las empresas a complementar su base interna de conocimientos con otros procedentes del exterior. Así, entre las diversas alternativas existentes, la cooperación con clientes y usuarios en materia de innovación se perfila como una de las más importantes fuentes de ideas innovadoras. Por este motivo, el presente trabajo profundiza en el análisis de distintas fuentes de información utilizadas por empresas europeas y españolas para el desarrollo de innovaciones, resaltando la importancia de las aportaciones realizadas por estos agentes. La investigación se completa con un estudio empírico en el que se compara la influencia de cuatro tipos de cooperación (con clientes, proveedores, universidades y expertos/ firmas consultoras) sobre la intensidad de la actividad innovadora en un conjunto de veinte sectores productivos españoles. De él se desprende que la colaboración con clientes para el desarrollo de innovaciones incide significativamente sobre dicha intensidad y que es un buen indicador de la importancia que la organización otorga a estas actividades.S

    Increased lifetime of Organic Photovoltaics (OPVs) and the impact of degradation, efficiency and costs in the LCOE of Emerging PVs

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    Emerging photovoltaic (PV) technologies such as organic photovoltaics (OPVs) and perovskites (PVKs) have the potential to disrupt the PV market due to their ease of fabrication (compatible with cheap roll-to-roll processing) and installation, as well as their significant efficiency improvements in recent years. However, rapid degradation is still an issue present in many emerging PVs, which must be addressed to enable their commercialisation. This thesis shows an OPV lifetime enhancing technique by adding the insulating polymer PMMA to the active layer, and a novel model for quantifying the impact of degradation (alongside efficiency and cost) upon levelized cost of energy (LCOE) in real world emerging PV installations. The effect of PMMA morphology on the success of a ternary strategy was investigated, leading to device design guidelines. It was found that either increasing the weight percent (wt%) or molecular weight (MW) of PMMA resulted in an increase in the volume of PMMA-rich islands, which provided the OPV protection against water and oxygen ingress. It was also found that adding PMMA can be effective in enhancing the lifetime of different active material combinations, although not to the same extent, and that processing additives can have a negative impact in the devices lifetime. A novel model was developed taking into account realistic degradation profile sourced from a literature review of state-of-the-art OPV and PVK devices. It was found that optimal strategies to improve LCOE depend on the present characteristics of a device, and that panels with a good balance of efficiency and degradation were better than panels with higher efficiency but higher degradation as well. Further, it was found that low-cost locations were more favoured from reductions in the degradation rate and module cost, whilst high-cost locations were more benefited from improvements in initial efficiency, lower discount rates and reductions in install costs

    Predictive Maintenance of Critical Equipment for Floating Liquefied Natural Gas Liquefaction Process

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    Predictive Maintenance of Critical Equipment for Liquefied Natural Gas Liquefaction Process Meeting global energy demand is a massive challenge, especially with the quest of more affinity towards sustainable and cleaner energy. Natural gas is viewed as a bridge fuel to a renewable energy. LNG as a processed form of natural gas is the fastest growing and cleanest form of fossil fuel. Recently, the unprecedented increased in LNG demand, pushes its exploration and processing into offshore as Floating LNG (FLNG). The offshore topsides gas processes and liquefaction has been identified as one of the great challenges of FLNG. Maintaining topside liquefaction process asset such as gas turbine is critical to profitability and reliability, availability of the process facilities. With the setbacks of widely used reactive and preventive time-based maintenances approaches, to meet the optimal reliability and availability requirements of oil and gas operators, this thesis presents a framework driven by AI-based learning approaches for predictive maintenance. The framework is aimed at leveraging the value of condition-based maintenance to minimises the failures and downtimes of critical FLNG equipment (Aeroderivative gas turbine). In this study, gas turbine thermodynamics were introduced, as well as some factors affecting gas turbine modelling. Some important considerations whilst modelling gas turbine system such as modelling objectives, modelling methods, as well as approaches in modelling gas turbines were investigated. These give basis and mathematical background to develop a gas turbine simulated model. The behaviour of simple cycle HDGT was simulated using thermodynamic laws and operational data based on Rowen model. Simulink model is created using experimental data based on Rowen’s model, which is aimed at exploring transient behaviour of an industrial gas turbine. The results show the capability of Simulink model in capture nonlinear dynamics of the gas turbine system, although constraint to be applied for further condition monitoring studies, due to lack of some suitable relevant correlated features required by the model. AI-based models were found to perform well in predicting gas turbines failures. These capabilities were investigated by this thesis and validated using an experimental data obtained from gas turbine engine facility. The dynamic behaviours gas turbines changes when exposed to different varieties of fuel. A diagnostics-based AI models were developed to diagnose different gas turbine engine’s failures associated with exposure to various types of fuels. The capabilities of Principal Component Analysis (PCA) technique have been harnessed to reduce the dimensionality of the dataset and extract good features for the diagnostics model development. Signal processing-based (time-domain, frequency domain, time-frequency domain) techniques have also been used as feature extraction tools, and significantly added more correlations to the dataset and influences the prediction results obtained. Signal processing played a vital role in extracting good features for the diagnostic models when compared PCA. The overall results obtained from both PCA, and signal processing-based models demonstrated the capabilities of neural network-based models in predicting gas turbine’s failures. Further, deep learning-based LSTM model have been developed, which extract features from the time series dataset directly, and hence does not require any feature extraction tool. The LSTM model achieved the highest performance and prediction accuracy, compared to both PCA-based and signal processing-based the models. In summary, it is concluded from this thesis that despite some challenges related to gas turbines Simulink Model for not being integrated fully for gas turbine condition monitoring studies, yet data-driven models have proven strong potentials and excellent performances on gas turbine’s CBM diagnostics. The models developed in this thesis can be used for design and manufacturing purposes on gas turbines applied to FLNG, especially on condition monitoring and fault detection of gas turbines. The result obtained would provide valuable understanding and helpful guidance for researchers and practitioners to implement robust predictive maintenance models that will enhance the reliability and availability of FLNG critical equipment.Petroleum Technology Development Funds (PTDF) Nigeri

    Physical phenomena controlling quiescent flame spread in porous wildland fuel beds

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    Despite well-developed solid surface flame spread theories, we still lack a coherent theory to describe flame spread through porous wildland fuel beds. This porosity results in additional complexity, reducing the thermal conductivity of the fuel bed, but allowing in-bed radiative and convective heat transfer to occur. While previous studies have explored the effect of fuel bed structure on the overall fire behaviour, there remains a need for further investigation of the effect of fuel structure on the underlying physical phenomena controlling flame spread. Through an extensive series of laboratory-based experiments, this thesis provides detailed, physics-based insights for quiescent flame spread through natural porous beds, across a range of structural conditions. Measurements are presented for fuel beds representative of natural field conditions within an area of the fire-prone New Jersey Pinelands National Reserve, which compliment a related series of field experiments conducted as part of a wider research project. Additional systematic investigation across a wider range of fuel conditions identified independent effects of fuel loading and bulk density on the spread rate, flame height and heat release rate. However, neither fuel loading nor bulk density alone provided adequate prediction of the resulting fire behaviour. Drawing on existing structural descriptors (for both natural and engineered fuel beds) an alternative parameter ασδ was proposed. This parameter (incorporating the fuel bed porosity (α), fuel element surface-to-volume ratio (σ), and the fuel bed height (δ)) was strongly correlated with the spread rate. One effect of the fuel bed structure is to influence the heat transfer mechanisms both above and within the porous fuel bed. Existing descriptions of radiation transport through porous fuel beds are often predicated on the assumption of an isotropic fuel bed. However, given their preferential angle of inclination, the pine needle beds in this study may not exhibit isotropic behaviour. Regardless, for the structural conditions investigated, horizontal heat transfer through the fuel bed was identified as the dominant heating mechanism within this quiescent flame spread scenario. However, the significance of heat transfer contributions from the above-bed flame generally increased with increasing ασδ value of the fuel bed. Using direct measurements of the heat flux magnitude and effective heating distance, close agreement was observed between experimentally observed spread rates and a simple thermal model considering only radiative heat transfer through the fuel bed, particularly at lower values of ασδ. Over-predictions occurred at higher ασδ values, or where other heat transfer terms were incorporated, which may highlight the need to include additional heat loss terms. A significant effect of fuel structure on the primary flow regimes, both within and above these porous fuel beds, was also observed, with important implications for the heat transfer and oxygen supply within the fuel bed. Independent effects of fuel loading and bulk density on both the buoyant and buoyancy-driven entrainment flow were observed, with a complex feedback cycle occurring between Heat Release Rate (HRR) and combustion behaviour. Generally, increases in fuel loading resulted in increased HRR, and therefore increased buoyant flow velocity, along with an increase in the velocity of flow entrained towards the combustion region. The complex effects of fuel structure in both the flaming and smouldering combustion phases may necessitate modifications to other common modelling approaches. The widely used Rothermel model under-predicted spread rate for higher bulk density and lower ασδ fuel beds. As previously suggested, an over-sensitivity to fuel bed height was observed, with experimental comparison indicating an under-prediction of reaction intensity at lower fuel heights. These findings have important implications particularly given the continuing widespread use of the Rothermel model, which continues to underpin elements of the BehavePlus fire modelling system and the US National Fire Danger Rating System. The physical insights, and modelling approaches, developed for this low-intensity, quiescent flame spread scenario, are applicable to common prescribed fire activities. It is hoped that this work (alongside complimentary laboratory and field experiments conducted by various authors as part of a wider multi-agency project (SERDP-RC2641)) will contribute to the emerging field of prescribed fire science, and help to address the pressing need for further development of fire prediction and modelling tools

    Full stack development toward a trapped ion logical qubit

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    Quantum error correction is a key step toward the construction of a large-scale quantum computer, by preventing small infidelities in quantum gates from accumulating over the course of an algorithm. Detecting and correcting errors is achieved by using multiple physical qubits to form a smaller number of robust logical qubits. The physical implementation of a logical qubit requires multiple qubits, on which high fidelity gates can be performed. The project aims to realize a logical qubit based on ions confined on a microfabricated surface trap. Each physical qubit will be a microwave dressed state qubit based on 171Yb+ ions. Gates are intended to be realized through RF and microwave radiation in combination with magnetic field gradients. The project vertically integrates software down to hardware compilation layers in order to deliver, in the near future, a fully functional small device demonstrator. This thesis presents novel results on multiple layers of a full stack quantum computer model. On the hardware level a robust quantum gate is studied and ion displacement over the X-junction geometry is demonstrated. The experimental organization is optimized through automation and compressed waveform data transmission. A new quantum assembly language purely dedicated to trapped ion quantum computers is introduced. The demonstrator is aimed at testing implementation of quantum error correction codes while preparing for larger scale iterations.Open Acces

    How to Be a God

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    When it comes to questions concerning the nature of Reality, Philosophers and Theologians have the answers. Philosophers have the answers that can’t be proven right. Theologians have the answers that can’t be proven wrong. Today’s designers of Massively-Multiplayer Online Role-Playing Games create realities for a living. They can’t spend centuries mulling over the issues: they have to face them head-on. Their practical experiences can indicate which theoretical proposals actually work in practice. That’s today’s designers. Tomorrow’s will have a whole new set of questions to answer. The designers of virtual worlds are the literal gods of those realities. Suppose Artificial Intelligence comes through and allows us to create non-player characters as smart as us. What are our responsibilities as gods? How should we, as gods, conduct ourselves? How should we be gods
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