58 research outputs found

    Cyclability in bipartite graphs

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    Let G=(X,Y,E)G=(X,Y,E) be a balanced 22-connected bipartite graph and S⊂V(G)S \subset V(G). We will say that SS is cyclable in GG if all vertices of SS belong to a common cycle in GG. We give sufficient degree conditions in a balanced bipartite graph GG and a subset S⊂V(G)S \subset V(G) for the cyclability of the set SS

    Modeling of Sodium-ion Batteries

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    Computational study of electron-transfers and singlet oxygen in aprotic metal-O2 batteries

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    Aprotic metal-oxygen batteries (MOBs), based on the electroreduction of molecular oxygen at a porous cathode, have attracted a vast interest in research, owing to their potential upgrade in terms of energy density and costs over present lithium-ion batteries. Despite their highly promising features, aprotic MOBs based on alkali and alkaline-earth metals still suffer severe limitations in their practical applicability. One of the main unresolved issues, especially with Li-O2 batteries, is represented by the high degree of parasitic reactivity. Singlet oxygen (1O2) is today held responsible for a major contribution to such reactivity, and the disproportionation of the superoxide anion is considered as one of the most likely source of 1O2 in the cell environment. Experimental evidences for electrolyte degradation and evolution of 1O2 have been reported, but the fundamental chemical mechanisms underlying these phenomena are still poorly understood. A valid strategy for contrasting the arise of side-reactions and materials degradation is to use redox mediators (RMs), which allow to recharge the battery with greatly reduced overpotentials. Understanding the con- nection of RM-assisted charging with the production 1O2 is likely to play a key role in the design of fully reversible and efficient practical MOBs in the future. In this thesis, quantum chemical computational methods were used to investigate reactive processes of electron-transfer involving reduced oxygen species in aprotic MOBs. The possibility of reactive pathways leading to the release of 1O2 was addressed in particular. The aim of the thesis was to apply theoretical methods to the modeling of reactive systems, in order to unravel part of the mechanisms which underpin the parasitic chemistry of MOBs. Despite their apparent simplicity, the reaction governing the chemistry of the cells involve a complex interplay of radical species and electronic excited states. For this reason, our approach was to use mainly ab-initio correlated multiconfigurational methods for a high-level description of potential energy surfaces and reaction energies. Owing to the computational costs of the methods, such an approach necessarily entails the resort to simplified models, including the exclusive use of implicit solvent and the neglect of solid phases and interfacial effects

    Determination and measurement of factors which influence propensity to cycle to work

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    2.89% of the UK population cycled for the journey to work as measured by the census in 200I. This percentage is similar to the percentage from the 1991 census and indicates a levelling off in the decline that had been seen in the previous two decades in bicycle use for the journey to work, but does not demonstrate any increase in line with policy aspirations. Choice is a complex issue and related to a wide range of factors including socio-economic variables and the nature of transport infrastructure and the physical geography of an area. As well as the rational and measurable factors, there are many much more complex and subtle factors including the influences of culture and social norms. Changes to behaviour probably take an extended period of time and require a range Qf conditions to be appropriate before a positive choice can be made. Waldman (1977) undertook the last countrywide aggregate study of the variation in use of the bicycle for the journey to work, but a number of the variables he constructed were measured inappropriately, not the least of which was his measure for "danger", which he recommended for further study. It is widely considered that perception of risk from motor traffic is a reason why many people do not currently use the bicycle. This is only one measurable attribute and European bicycle planners consider network coherence, directness, attractiveness and comfort as other equally important issues when designing schemes to promote bicycle use. This research has used primary data collected on perceptions of risk. The particular contribution of the research is in the development of a methodology for the determination of perception of risk for a whole journey, including routes and junctions, and the extension of this methodology to create a measure for risk at an area wide level. Measures that have been found to be significant in relation to the use of the bicycle for the journey to work are car ownership, socio-economic classification, ethnicity, distance to work, condition of the highway pavement, highway network density and population density, hi lIiness, rainfall and mean temperature. In addition the length of bicycle lane, length of bus lane and length of traffic free route have also been found to be important in so far as it influences the perception of risk, which in turn influences the level of bicycle use. The length of route that is signed has also been found to be important. In a sample of four districts for which appropriate data is available, a seven fold increase in route length with cycle facilities, or signed route, would create conditions suitable for an increase in cycle use for the journey to work by a factor of the order of two. An elimination of highways with negative residual life would create conditions suitable for an increase of 10% in the number of bicycle trips for the journey to work

    Study of an off-grid wireless sensors with Li-Ion battery and Giant Magnetostrisctive Material

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    L'abstract è presente nell'allegato / the abstract is in the attachmen

    Leveraging deep reinforcement learning in the smart grid environment

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    L’apprentissage statistique moderne démontre des résultats impressionnants, où les or- dinateurs viennent à atteindre ou même à excéder les standards humains dans certaines applications telles que la vision par ordinateur ou les jeux de stratégie. Pourtant, malgré ces avancées, force est de constater que les applications fiables en déploiement en sont encore à leur état embryonnaire en comparaison aux opportunités qu’elles pourraient apporter. C’est dans cette perspective, avec une emphase mise sur la théorie de décision séquentielle et sur les recherches récentes en apprentissage automatique, que nous démontrons l’applica- tion efficace de ces méthodes sur des cas liés au réseau électrique et à l’optimisation de ses acteurs. Nous considérons ainsi des instances impliquant des unités d’emmagasinement éner- gétique ou des voitures électriques, jusqu’aux contrôles thermiques des bâtiments intelligents. Nous concluons finalement en introduisant une nouvelle approche hybride qui combine les performances modernes de l’apprentissage profond et de l’apprentissage par renforcement au cadre d’application éprouvé de la recherche opérationnelle classique, dans le but de faciliter l’intégration de nouvelles méthodes d’apprentissage statistique sur différentes applications concrètes.While modern statistical learning is achieving impressive results, as computers start exceeding human baselines in some applications like computer vision, or even beating pro- fessional human players at strategy games without any prior knowledge, reliable deployed applications are still in their infancy compared to what these new opportunities could fathom. In this perspective, with a keen focus on sequential decision theory and recent statistical learning research, we demonstrate efficient application of such methods on instances involving the energy grid and the optimization of its actors, from energy storage and electric cars to smart buildings and thermal controls. We conclude by introducing a new hybrid approach combining the modern performance of deep learning and reinforcement learning with the proven application framework of operations research, in the objective of facilitating seamlessly the integration of new statistical learning-oriented methodologies in concrete applications

    The textile toolbox: New design thinking, materials and processes for sustainable fashion textiles: Full research report

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    This report reviews the design research conducted between June 2011 and May 2015 by a team of University of the Arts London (UAL) textile researchers, led by Professor Rebecca Earley, who were part of the Swedish funded multi-disciplinary Mistra Future Fashion (MFF) consortium. The objective of the consortium was to research opportunities to advance a more sustainable and competitive fashion industry. Whilst the environmental impacts of commercial production and consumption have increasingly been the subject of debate in recent years, textile and fashion designers have been considering their responsibilities as creators of sustainable products and systems and have been struggling to find a way to fully comprehend the challenges and to know how to go about tackling them. This team wanted to propose design as the agent for change – working right at the heart of the problem. Basing themselves in organisations and companies of all scales, the research team worked on a process of progressive problem solving with others to propose a new course of action to help their community improve its work practices

    The Textile Toolbox: New Design Thinking, Materials & Processes for Sustainable Fashion Textiles

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    The Textile Toolbox: New Design Thinking, Materials & Processes for Sustainable Fashion Textiles reviews the design research conducted between June 2011 and May 2015 by a team of University of the Arts London (UAL) textile researchers, led by Professor Rebecca Earley, who are part of the Swedish funded multi-disciplinary Mistra Future Fashion (MFF) consortium. The objective of the consortium is to research opportunities to advance a more sustainable and competitive fashion industry

    A Classical Investigation of the Dynamics of MgO Grain Boundaries and an Ab Initio Study of Oxygen Vacancies in Amorphous SiO2

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    The arrangement of atoms in most ceramic materials is not perfect and point defects such as vacancies and interstitials, as well as extended defects like grain boundaries exist. In general these defects dominate the properties and processes that are important for the applications of the material. The capture and emission of charge at point defects can affect the stability of dielectrics such as those used in MOS devices. The presence of grain boundaries is also known to lower both the electric and thermal conductivity of a material. Collectively the diffusion of point defects at grain boundaries play a role in mechanisms such as creep and have also been suggested to be involved in the corrosion of metals. In this thesis simulation techniques were used to investigate properties of defects in amorphous silica and near grain boundaries in MgO. Atomistic methods were used to determine the migration barriers of defects at MgO grain boundaries, the effect of electric field on the stability of the defects, and also the effect of temperature on the structure and stability of the grain boundaries. The nudged elastic band method was used to determine the activation energy for vacancy and interstitial migration at the Σ17 {410}/[001] tilt and the Σ5 twist grain boundaries. At the tilt and the twist grain boundaries it was found that the activation energies for vacancy migration were up to 1.31 eV and 1.41 eV lower than those in bulk MgO respectively. A finite MgO film model was produced to investigate the effect of electric field on point defects at the tilt grain boundary. The electric field was added to the system by sandwiching the MgO between two layers of point charges. It was found that the field anisotropically lowers the activation energies for vacancy migration by up to 0.37 eV with respect to those determined in the absence of the field. Molecular dynamics simulations were used to investigate the effect of temperature on the stability of the tilt grain boundary and two of its metastable structures and also on the twist grain boundary. The twist grain boundary was found to have the highest entropy in the temperature range 300 - 3000 K which suggests that it may be the most commonly occurring grain boundary in MgO. An ab initio study was also carried out on the structure and electronic structure of the neutral oxygen vacancy in amorphous silica in order to investigate mechanisms associated with dielectric breakdown such as negative bias temperature instability. Contrary to published suggestions the positively charged and neutral vacancy defects studied were found to have one electron energy levels below the Si valence band which suggests that these defects do not contribute to threshold voltage shifts
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