48,751 research outputs found

    Optimised acoustic cavitation for the efficient liquid phase exfoliation of graphene.

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    As there are many different graphene production methods which vary in terms of quality, cost, production rate, yield, scalability, post-processing requirements, flake size distribution, and batch-to-batch repeatability, it has been challenging to develop commercial applications that exploit the extraordinary properties of graphene rather than purely using it as a marketing tool. This thesis reports on the study of ultrasonication, which is an established method to exfoliate graphene in the liquid phase, as well as a pre/post-processing technique in other graphene production methods. Due to a poor understanding of inertial cavitation, which is the fundamental mechanism driving graphene exfoliation during ultrasonication, this production method is commonly characterised by low exfoliation rates, reduced yields and limited scalability/controllability. By optimising the inertial cavitation dose, graphene yields of up to 19 ± 4 % can be exfoliated over just 3 hours of sonication at room temperature. Furthermore, inertial cavitation preferentially exfoliates larger flakes during ultrasonication and the size of the graphene flakes is correlated with, and therefore can be controlled by, inertial cavitation dose. Alongside small-scale exfoliation studies, a scalable graphene sonoreactor proof-of-concept (patent pending), has been designed and tested to generate high exfoliation rates and allow for in-situ size distribution control. More generally it is shown cavitation metrology is critical in developing efficient, controllable and repeatable ultrasonication strategies for the liquid phase exfoliation of 2D nanomaterials, and the many applications and industries in which ultrasonication is employed

    Data-driven resilience assessment for transport infrastructure exposed to multiple hazards by integrating multiscale terrestrial and airborne monitoring systems

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    The exposure of critical transport infrastructure to natural hazards and climate change effects has severe consequences on world economies and societies and, thus, safety and resiliency of transport networks are of paramount importance. The currently available frameworks for quantitative risk and resiliencebased design and assessment have been mainly developed for bridges exposed to earthquakes. However, there is an absence of well-informed exposure, vulnerability, functionality and recovery models, which are the main components in the quantification of resilience. The present paper proposes an integrated framework for the data- driven resilience assessment of transport infrastructure exposed to multiple hazards by using multiscale monitoring data, such as terrestrial and airborne data, as well as open-access crowd data and environmental measurements. Monitoring and early warnings are expected to produce accurate and rapidly informed quantitative risk and resilience assessments for transport infrastructure and to enhance asset management. Therefore, this framework aims to facilitate stakeholders’ decision-making for daily and catastrophic events and to support adaptation and preparedness with preventive and/or retrofitting measures against multiple hazards

    Data-driven resilience assessment for transport nfrastructure exposed to multiple hazards

    No full text
    The exposure of critical transport infrastructure to natural hazards and climate change effects has severe consequences on world economies and societies and, thus, safety and resiliency of transport networks are of paramount importance. The currently available frameworks for quantitative risk and resiliencebased design and assessment have been mainly developed for bridges exposed to earthquakes. However, there is an absence of well-informed exposure, vulnerability, functionality and recovery models, which are the main components in the quantification of resilience. The present paper proposes an integrated framework for the data- driven resilience assessment of transport infrastructure exposed to multiple hazards by using multiscale monitoring data, such as terrestrial and airborne data, as well as open-access crowd data and environmental measurements. Monitoring and early warnings are expected to produce accurate and rapidly informed quantitative risk and resilience assessments for transport infrastructure and to enhance asset management. Therefore, this framework aims to facilitate stakeholders’ decision-making for daily and catastrophic events and to support adaptation and preparedness with preventive and/or retrofitting measures against multiple hazards

    Do Travelers Trust Intelligent Service Robots?

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    This research investigates travelers' trust in intelligent autonomous technologies based on two studies involving self-driving transportation and robot bartenders. Targeting travelers residing in the United States, online questionnaire was distributed to test the relationships between trusting beliefs in intelligent robots, its antecedents, and its outcomes. The results demonstrate that the cognitive trust formation process holds in situations involving intelligent robots as objects of trust. Trust in intelligent machines is influenced by negative attitude toward technology and propensity to trust technology. Surprisingly, the physical form of robots does not affect trust. Finally, trust leads to adoption intention in both studies. The contribution of this research is in elucidating consumer trust in intelligent robots designed for socially-driven interactions in travel settings

    Resilient Monitoring of the Structural Performance of Reinforced Concrete Bridges using Guided Waves

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    Structural Health Monitoring of the deflections of a reinforced concrete bridge deck strengthened with Fibre Reinforced Polymers (FRP) or composite materials can help towards obtaining predictions of fail-ures. Data regarding spam deflection, FRP debondings or failures and concrete crack patterns, acquired by guided waves on interfaces of concrete substrate and FRP measures are used in order to represent the damage propagation of the strengthened bridge deck over time. The failure indexes of the interfaces help towards a strategy for maintenance and asset management based on potential risk that derives from structural data. This resilient strategic monitoring of interfaces is a practical, expedient, long-distant tool to estimate the efficiency of the interfaces (Interface Efficiency Indices-InterFeis) and the risk level of the asset with no disruption of traffic or in nonapproachable areas. The monitoring of the time history of data concerning the structural integ-rity, assesses the structural performance of the bridge against critical loads, combined phenomena, extreme events, climate change or other uncertainties of design or of its life-cycle and can be integrated in bridge design guidelines towards infrastructural safety and resilience of the transportation network, saving valuable time and resources

    Guanxi influences on women intrapreneurship

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    Drawing on the literature examining women intrapreneurship, Chinese guanxi and tourism, this article critically explores a theoretical framework for understanding guanxi influences on women intrapreneurship in a nonwestern and highly patriarchal destination. Through a qualitative analysis of women managers from twenty-four medium and large tourism firms in China, the study provides evidence of Guanxi as socially embedded personal relationships for the exchange of favors, enabling women managers to initiate specific types of women intrapreneurship initiatives in their organizations. The findings reveal how the women managers draw on three forms of guanxi (external, within and back-stage) to display intrapreneurial actions as well as the firm-specific factors that constitute important determinants of women intrapreneurship. The managerial implications for encouraging and supporting women intrapreneurs are critically examined

    Sketch Less for More: On-the-Fly Fine-Grained Sketch Based Image Retrieval

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    Fine-grained sketch-based image retrieval (FG-SBIR) addresses the problem of retrieving a particular photo instance given a user’s query sketch. Its widespread applicability is however hindered by the fact that drawing a sketch takes time, and most people struggle to draw a complete and faithful sketch. In this paper, we reformulate the conventional FG-SBIR framework to tackle these challenges, with the ultimate goal of retrieving the target photo with the least number of strokes possible. We further propose an on-the-fly design that starts retrieving as soon as the user starts drawing. To accomplish this, we devise a reinforcement learning based cross-modal retrieval framework that directly optimizes rank of the ground-truth photo over a complete sketch drawing episode. Additionally, we introduce a novel reward scheme that circumvents the problems related to irrelevant sketch strokes, and thus provides us with a more consistent rank list during the retrieval. We achievesuperiorearly-retrievalefficiencyoverstate-of-theartmethodsandalternativebaselinesontwopubliclyavailable fine-grained sketch retrieval datasets

    On the Fair Management of Close Races in Swimming

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    The sport of swimming faces particular problems of adjudication for close races. Swimming cannot benefit from technology in the same way as athletics and other sports in which photo finishes and visual differentiation is used to determine ranking. Swimming at the highest level relies almost entirely on the electronic timing system and displayed race times, presented to the nearest 0.01s, to determine the ranking of swimmers and to establish if a dead heat has occurred. At swimming events, electronic timing equipment records individual race times to 0.0001s. However, it is shown that any protocol used to display race times to the nearest 0.01s leads to unfairness in the categorization of a race as a dead heat. The key issues are discussed in the context of the controversial Michael Phelps and Milorad Ĉavić 100m butterfly event at the 2008 Beijing Games. An alternative approach for the categorization of a dead heat is proposed which is fairer to swimmers

    Policy Implications of Autonomous Vehicles

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    Investigation of powder flowability at low stresses by DEM modelling

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    Ball indentation is a technique capable of assessing powder flowability down to very low consolidation stresses (≤1 kPa). With this method, powder flowability is determined by measuring the hardness of a powder bed, which allows the unconfined yield strength to be inferred via the constraint factor. The latter is well established for continuum materials, whereas for particulate systems its dependency on stress level and powder properties is not well defined. This work investigates these factors by simulating the ball indentation method using DEM. The constraint factor is shown to be independent of pre-consolidation stress. Constraint factor generally increases with interface energy for relatively cohesionless powders, though not for cohesive powders. An increase in plastic yield stress leads to a decrease in the constraint factor. Increasing the coefficient of interparticle static friction reduces the constraint factor, while increasing the coefficient of inter-particle rolling friction significantly increases the constraint factor
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