19,621 research outputs found

    DĂ©veloppement d’un systĂšme intelligent de reconnaissance automatisĂ©e pour la caractĂ©risation des Ă©tats de surface de la chaussĂ©e en temps rĂ©el par une approche multicapteurs

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    Le rĂŽle d’un service dĂ©diĂ© Ă  l’analyse de la mĂ©tĂ©o routiĂšre est d’émettre des prĂ©visions et des avertissements aux usagers quant Ă  l’état de la chaussĂ©e, permettant ainsi d’anticiper les conditions de circulations dangereuses, notamment en pĂ©riode hivernale. Il est donc important de dĂ©finir l’état de chaussĂ©e en tout temps. L’objectif de ce projet est donc de dĂ©velopper un systĂšme de dĂ©tection multicapteurs automatisĂ©e pour la caractĂ©risation en temps rĂ©el des Ă©tats de surface de la chaussĂ©e (neige, glace, humide, sec). Ce mĂ©moire se focalise donc sur le dĂ©veloppement d’une mĂ©thode de fusion de donnĂ©es images et sons par apprentissage profond basĂ©e sur la thĂ©orie de Dempster-Shafer. Les mesures directes pour l’acquisition des donnĂ©es qui ont servi Ă  l’entrainement du modĂšle de fusion ont Ă©tĂ© effectuĂ©es Ă  l’aide de deux capteurs Ă  faible coĂ»t disponibles dans le commerce. Le premier capteur est une camĂ©ra pour enregistrer des vidĂ©os de la surface de la route. Le second capteur est un microphone pour enregistrer le bruit de l’interaction pneu-chaussĂ©e qui caractĂ©rise chaque Ă©tat de surface. La finalitĂ© de ce systĂšme est de pouvoir fonctionner sur un nano-ordinateur pour l’acquisition, le traitement et la diffusion de l’information en temps rĂ©el afin d’avertir les services d’entretien routier ainsi que les usagers de la route. De façon prĂ©cise, le systĂšme se prĂ©sente comme suit :1) une architecture d’apprentissage profond classifiant chaque Ă©tat de surface Ă  partir des images issues de la vidĂ©o sous forme de probabilitĂ©s ; 2) une architecture d’apprentissage profond classifiant chaque Ă©tat de surface Ă  partir du son sous forme de probabilitĂ©s ; 3) les probabilitĂ©s issues de chaque architecture ont Ă©tĂ© ensuite introduites dans le modĂšle de fusion pour obtenir la dĂ©cision finale. Afin que le systĂšme soit lĂ©ger et moins coĂ»teux, il a Ă©tĂ© dĂ©veloppĂ© Ă  partir d’architectures alliant lĂ©gĂšretĂ© et prĂ©cision Ă  savoir Squeeznet pour les images et M5 pour le son. Lors de la validation, le systĂšme a dĂ©montrĂ© une bonne performance pour la dĂ©tection des Ă©tats surface avec notamment 87,9 % pour la glace noire et 97 % pour la neige fondante

    Responsible E-Waste Value Chains in Africa

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    Population growth, increasing prosperity and changing consumer habits globally are increasing demand for consumer electronics. Further to this, rapid changes in technology, falling prices, increased affordability and consumer appetite for new products have exacerbated e-waste management challenges and seen millions of tons of electronic devices become obsolete. This rapid literature review collates evidence from academic, policy focussed and grey literature on e-waste value chains. The report should be read I conjunction with an earlier report on e-waste management. E-waste is any electrical or electronic equipment, including all components, subassemblies and consumables, which are part of the equipment at the time the equipment becomes waste. When e-waste is collected and treated formally, it normally includes the following steps: Collection, Sorting and disassembly, Size reduction, Separation. The following five pillars of a sustainable e-waste management system have been identified: ‱ Business and finance ‱ Policy and regulation ‱ Technology and skills ‱ Monitoring and control ‱ Marketing and awareness As such, to support the development of a responsible e-waste value chain, the following elements must be addressed. ‱ Understanding how e-waste is currently managed ‱ There is no one-size-fits all solution to building a robust e-waste management system based on extended producer responsibility. ‱ An e-waste system built without a participatory approach is likely to be hampered by a series of issues. ‱ An overarching policy is necessary ‱ The choices made for the sector should be founded on two crucial elements – data from on the ground, and inputs from stakeholders. ‱ Enforcement is incumbent on the government mandate The push towards a circular economy has provided stakeholders across the value chain with an impetus to initiate systemic improvements and invest in infrastructure and awareness raising.FCDO (Foreign, Commonwealth and Development Office

    Sponsorship of individual athletes in relation to the company’s marketing strategy

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    Abstract. Sports marketing is constantly evolving and its importance as a marketing tool is strengthening worldwide. As a result, sponsorship of individual athletes and personal brands have become strategic marketing tools for companies. The marketing strategy of companies should follow and support the overall strategy of the company, therefore marketing measures, such as, sponsorship should also follow and support the overall strategy. Previous research has examined sponsorship from the perspective of value creation as well as from the perspective of how sponsorship affects the corporate image and the benefits of sponsorship for stakeholders. In terms of strategy, sponsorship has been examined from the perspective of what kind of sponsorship strategies companies have. Sponsorship in general and the sponsorship of individual athletes as its sub-theme are still very little studied themes in Finland. Therefore, comprehensive data is required on how sponsorship can be utilized as a strategic tool in marketing. The aim of the study is to investigate how the sponsorship of individual athletes in Finland is linked to the marketing strategy of companies. Interviewing Finnish companies is of primary importance for researching this topic. The research follows an abductive process, which means that the existing literature is studied first, and the empirical analysis is conducted based on the formed theoretical framework. The research is a qualitative multiple-case study, and the empirical data is collected through five semi-structured interviews with company representatives of large and medium-sized enterprises of various business sectors. Based on the data, companies perceive individual athlete sponsorship either as an embedded part of a marketing strategy, as partially embedded marketing measure, or as not in relation to marketing strategy. This is influenced by factors, such as, industry, the size of the sponsorship agreement, the athletes’ own motivation, and the companies’ ability or inability to utilize individual athletes in the company’s marketing communications. Sponsorship of individual athletes was seen as a regionally significant factor from the perspective of corporate social responsibility

    Performance Investigation of the Solar Membrane Distillation Process Using TRNSYS Software

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    Membrane distillation (MD) is a separation process used for water desalination, which operates at low pressures and feeds temperatures. Air gap membrane distillation (AGMD) is the new MD configuration for desalination where both the hot feed side and the cold permeate side are in indirect contact with the two membrane surfaces. The chapter presents a new approach for the numerical study to investigate various solar thermal systems of the MD process. The various MD solar systems are studied numerically using and including both flat plate collectors (the useful thermal energy reaches 3750 kJ/hr with a total area of 4 m2) and photovoltaic panels, each one has an area of 1.6 m2 by using an energy storage battery (12 V, 200 Ah). Therefore, the power load of solar AGMD systems is calculated and compared for the production of 100 L/day of distillate water. It was found that the developed system consumes less energy (1.2 kW) than other systems by percentage reaches 52.64% and with an average distillate water flow reaches 10 kg/h at the feed inlet temperature of AGMD module 52°C. Then, the developed system has been studied using TRNSYS and PVGIS programs on different days during the year in Ain Temouchent weather, Algeria

    Demand Response Applications for the Operation of Smart Natural Gas Systems

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    This chapter discusses different aspects related to the operation of natural gas systems in the framework of the new configuration of energy systems based on the smart grid concept. First of all, different experiences performed worldwide regarding the application of demand response principles to increase the efficiency and operability of natural gas networks are presented. Next, the characteristics of the natural gas system to be configured according to the smart grid architecture are discussed, including the necessary agents for the proper functioning of such infrastructure. After that, the current state of installation of gas smart meters in some European countries is presented, according to the massive rollout process promoted by the European Union. Barriers that prevent the full exploitation of demand response resources related to natural gas systems are presented in the next section. After that, technical constraints which may be solved by using demand response are presented. Finally, last tendencies related to the development of natural gas systems, such as the injection of hydrogen, are considered

    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

    Cost-effective non-destructive testing of biomedical components fabricated using additive manufacturing

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    Biocompatible titanium-alloys can be used to fabricate patient-specific medical components using additive manufacturing (AM). These novel components have the potential to improve clinical outcomes in various medical scenarios. However, AM introduces stability and repeatability concerns, which are potential roadblocks for its widespread use in the medical sector. Micro-CT imaging for non-destructive testing (NDT) is an effective solution for post-manufacturing quality control of these components. Unfortunately, current micro-CT NDT scanners require expensive infrastructure and hardware, which translates into prohibitively expensive routine NDT. Furthermore, the limited dynamic-range of these scanners can cause severe image artifacts that may compromise the diagnostic value of the non-destructive test. Finally, the cone-beam geometry of these scanners makes them susceptible to the adverse effects of scattered radiation, which is another source of artifacts in micro-CT imaging. In this work, we describe the design, fabrication, and implementation of a dedicated, cost-effective micro-CT scanner for NDT of AM-fabricated biomedical components. Our scanner reduces the limitations of costly image-based NDT by optimizing the scanner\u27s geometry and the image acquisition hardware (i.e., X-ray source and detector). Additionally, we describe two novel techniques to reduce image artifacts caused by photon-starvation and scatter radiation in cone-beam micro-CT imaging. Our cost-effective scanner was designed to match the image requirements of medium-size titanium-alloy medical components. We optimized the image acquisition hardware by using an 80 kVp low-cost portable X-ray unit and developing a low-cost lens-coupled X-ray detector. Image artifacts caused by photon-starvation were reduced by implementing dual-exposure high-dynamic-range radiography. For scatter mitigation, we describe the design, manufacturing, and testing of a large-area, highly-focused, two-dimensional, anti-scatter grid. Our results demonstrate that cost-effective NDT using low-cost equipment is feasible for medium-sized, titanium-alloy, AM-fabricated medical components. Our proposed high-dynamic-range strategy improved by 37% the penetration capabilities of an 80 kVp micro-CT imaging system for a total x-ray path length of 19.8 mm. Finally, our novel anti-scatter grid provided a 65% improvement in CT number accuracy and a 48% improvement in low-contrast visualization. Our proposed cost-effective scanner and artifact reduction strategies have the potential to improve patient care by accelerating the widespread use of patient-specific, bio-compatible, AM-manufactured, medical components

    Innate immunity and metabolism in the bovine ovarian follicle

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    Postpartum uterine disease in dairy cows is associated with reduced fertility. One of the first and most prevalent bacteria associated with uterine disease is Escherichia coli. The bacterial endotoxin, lipopolysaccharide (LPS), accumulates in the ovarian follicular fluid of animals with uterine disease. The granulosa cells of the ovarian follicle respond to LPS by secreting pro-inflammatory cytokines, such as interleukin (IL)-1a, IL-1b and IL-8, and oocyte health is perturbed. Dairy cows also experience metabolic energy stress in the postpartum period, which is associated with an increased risk of developing uterine disease and ovarian dysfunction. This thesis explored the crosstalk between innate immunity and metabolic energy stress in bovine granulosa cells and cumulus-oocyte complex. Firstly, we found that glycolysis, AMP-activated protein kinase and the mechanistic target of rapamycin, regulate the innate immune responses to LPS in granulosa cells isolated from bovine ovarian follicles. Activation of AMP-activated protein kinase decreased the LPS-induced secretion of IL-1a, IL-1b, and IL8, and was associated with shortened duration of ERK1/2 and JNK phosphorylation. Next, we found that decreasing the availability of cholesterol or inhibiting cholesterol biosynthesis using short-interfering RNA impaired the LPS-induced secretion of IL-1a and IL-1b by granulosa cells. Furthermore, metabolic energy stress or inhibiting cholesterol biosynthesis in the bovine cumulus-oocyte complex modulated the innate immune responses to LPS, and perturbed meiotic progression during in vitro maturation. Finally, we explored an in vivo model of uterine disease in heifers, using RNAseq to investigate alterations to the transcriptome of the reproductive tract. We found that uterine disease altered the transcriptome of the endometrium, oviduct, granulosa cells and oocyte, several months after bacterial infusion; these changes were most evident in the granulosa cells and oocyte of the ovarian follicle. The findings from this thesis imply that there is crosstalk between innate immunity and metabolism in the bovine ovarian follicle

    Carbon dioxide removal potential from decentralised bioenergy with carbon capture and storage (BECCS) and the relevance of operational choices

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    Bioenergy with carbon capture and storage (BECCS) technology is expected to support net-zero targets by supplying low carbon energy while providing carbon dioxide removal (CDR). BECCS is estimated to deliver 20 to 70 MtCO2 annual negative emissions by 2050 in the UK, despite there are currently no BECCS operating facility. This research is modelling and demonstrating the flexibility, scalability and attainable immediate application of BECCS. The CDR potential for two out of three BECCS pathways considered by the Intergovernmental Panel on Climate Change (IPCC) scenarios were quantified (i) modular-scale CHP process with post-combustion CCS utilising wheat straw and (ii) hydrogen production in a small-scale gasifier with pre-combustion CCS utilising locally sourced waste wood. Process modelling and lifecycle assessment were used, including a whole supply chain analysis. The investigated BECCS pathways could annually remove between −0.8 and −1.4 tCO2e tbiomass−1 depending on operational decisions. Using all the available wheat straw and waste wood in the UK, a joint CDR capacity for both systems could reach about 23% of the UK's CDR minimum target set for BECCS. Policy frameworks prioritising carbon efficiencies can shape those operational decisions and strongly impact on the overall energy and CDR performance of a BECCS system, but not necessarily maximising the trade-offs between biomass use, energy performance and CDR. A combination of different BECCS pathways will be necessary to reach net-zero targets. Decentralised BECCS deployment could support flexible approaches allowing to maximise positive system trade-offs, enable regional biomass utilisation and provide local energy supply to remote areas
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