943 resultados para AMBIPOLAR DIFFUSION
Resumo:
We introduce a diffusion-based algorithm in which multiple agents cooperate to predict a common and global statevalue function by sharing local estimates and local gradient information among neighbors. Our algorithm is a fully distributed implementation of the gradient temporal difference with linear function approximation, to make it applicable to multiagent settings. Simulations illustrate the benefit of cooperation in learning, as made possible by the proposed algorithm.
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In this PhD Thesis proposal, the principles of diffusion MRI (dMRI) in its application to the human brain mapping of connectivity are reviewed. The background section covers the fundamentals of dMRI, with special focus on those related to the distortions caused by susceptibility inhomogeneity across tissues. Also, a deep survey of available correction methodologies for this common artifact of dMRI is presented. Two methodological approaches to improved correction are introduced. Finally, the PhD proposal describes its objectives, the research plan, and the necessary resources.
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Introduction Diffusion weighted Imaging (DWI) techniques are able to measure, in vivo and non-invasively, the diffusivity of water molecules inside the human brain. DWI has been applied on cerebral ischemia, brain maturation, epilepsy, multiple sclerosis, etc. [1]. Nowadays, there is a very high availability of these images. DWI allows the identification of brain tissues, so its accurate segmentation is a common initial step for the referred applications. Materials and Methods We present a validation study on automated segmentation of DWI based on the Gaussian mixture and hidden Markov random field models. This methodology is widely solved with iterative conditional modes algorithm, but some studies suggest [2] that graph-cuts (GC) algorithms improve the results when initialization is not close to the final solution. We implemented a segmentation tool integrating ITK with a GC algorithm [3], and a validation software using fuzzy overlap measures [4]. Results Segmentation accuracy of each tool is tested against a gold-standard segmentation obtained from a T1 MPRAGE magnetic resonance image of the same subject, registered to the DWI space. The proposed software shows meaningful improvements by using the GC energy minimization approach on DTI and DSI (Diffusion Spectrum Imaging) data. Conclusions The brain tissues segmentation on DWI is a fundamental step on many applications. Accuracy and robustness improvements are achieved with the proposed software, with high impact on the application’s final result.
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Connectivity analysis on diffusion MRI data of the whole-brain suffers from distortions caused by the standard echo-planar imaging acquisition strategies. These images show characteristic geometrical deformations and signal destruction that are an important drawback limiting the success of tractography algorithms. Several retrospective correction techniques are readily available. In this work, we use a digital phantom designed for the evaluation of connectivity pipelines. We subject the phantom to a “theoretically correct” and plausible deformation that resembles the artifact under investigation. We correct data back, with three standard methodologies (namely fieldmap-based, reversed encoding-based, and registration- based). Finally, we rank the methods based on their geometrical accuracy, the dropout compensation, and their impact on the resulting connectivity matrices.
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Innovation studies have been interest of not only the scholars from various fields such as economics, management and sociology but also industrial practitioners and policy makers. In this vast and fruitful field, the theory of diffusion of innovations, which has been driven by a sociological approach, has played a vital role in our understanding of the mechanisms behind industrial change. In this paper, our aim is to give a state of art review of diffusion of innovation models in a structural and conceptual way with special reference to photovoltaic. We argue firstly, as an underlying background, how diffusion of innovations theory differs from other innovation studies. Secondly we give a brief taxonomical review of modelling methodologies together with comparative discussions. And finally we put the wealth of modelling in the context of photovoltaic diffusion and suggest some future directions.
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In some countries photovoltaic (PV) technology has already achieved a stage of development at which it can compete with conventional electricity sources. Germany provides a good illustration of this where PV market has reached a mature stage. As a manifest of this, the German government has recently reduced subsidies for households and industry by decreasing the feed in tariff for PV. This development raises fundamental questions: could the PV industry survive? Will consumers be motivated to continue to adopt PV when feed-in tariff diminish? The point of departure for the relevant literature on diffusion of PV has been on the effect of subsidies but little attention has paid to consumer motives when the policy support is scaled down. This paper introduces an in-depth analysis on understanding the consumer motives for adopting photovoltaic applications. Anchored in an extensive exploratory case study on PV consumers and PV system providers, this study aims to provide an encompassing explanation of diffusion of PV by revealing the link between consumer motives and the impact of policy.
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In this work, we introduce the Object Kinetic Monte Carlo (OKMC) simulator MMonCa and simulate the defect evolution in three different materials. We start by explaining the theory of OKMC and showing some details of how such theory is implemented by creating generic structures and algorithms in the objects that we want to simulate. Then we successfully reproduce simulated results for defect evolution in iron, silicon and tungsten using our simulator and compare with available experimental data and similar simulations. The comparisons validate MMonCa showing that it is powerful and flexible enough to be customized and used to study the damage evolution of defects in a wide range of solid materials.
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In recent years, the importance of the management of eco-innovations has been growing, more in practice than in academia. However, although in the literature there are already some evidences focussed on management of eco-innovations, there is no comprehensive review on the knowledge base of diffusion of eco-innovations. This paper provides a current overview of the existing body of literature, identifying the most active scholars and relevant publications in this field, and deepening in the major disciplines and research streams. Results show that the theory of diffusion of innovations which provided the philosophical underpinnings of how innovations are diffused is not the main knowledge base to explain the diffusion of eco-innovations. Lead market hypothesis, sustainable transitions and the ecological modernization appear as the initial base of the cognitive platform that can contribute to the understanding of diffusion of eco-innovations.
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We analyse a class of estimators of the generalized diffusion coefficient for fractional Brownian motion Bt of known Hurst index H, based on weighted functionals of the single time square displacement. We show that for a certain choice of the weight function these functionals possess an ergodic property and thus provide the true, ensemble-averaged, generalized diffusion coefficient to any necessary precision from a single trajectory data, but at expense of a progressively higher experimental resolution. Convergence is fastest around H ? 0.30, a value in the subdiffusive regime.
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Wide experimental evidence of the phosphorus diffusion gettering beneficial effect on solar grade silicon is found by measuring electron effective lifetime and interstitial iron concentration in as-grown and post processed samples from two ingots of upgraded metallurgical grade silicon produced by Ferrosolar. Results after two different P-diffusion processes are compared: P emitter diffusion at 850ºC followed by fast cool-down (called “standard process”) or followed by slow cool-down (called “extended process”). It is shown that final lifetimes of this low cost material are in the range of those obtained with conventional material. The extended process can be beneficial for wafers with specific initial distribution and concentration of iron, e.g. materials with high concentration of big Fe precipitates, while for other cases the standard process is enough efficient. An analysis based on the comparison of measured lifetime and dissolved iron concentration with theoretical calculations helps to infer the initial iron distribution and concentration, and according to that, choose the more effective type of gettering.
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In prokaryotes, nickel is an essential element participating in the structure of enzymes involved in multiple cellular processes. Nickel transport is a challenge for microorganisms since, although essential, high levels of this metal inside the cell are toxic. For this reason, bacteria have developed high-affinity nickel transporters as well as nickel-specific detoxification systems. Ultramafic soils, and soils contaminated with heavy metals are excellent sources of nickel resistant bacteria. Molecular analysis of strains isolated in the habitats has revealed novel genetic systems involved in adaptation to such hostile conditions.
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Nickel, like other transition metals, can be toxic to cells even at moderate concentration (low microM range) by displacing essential metals from their native binding sites or by generating reactive oxygen species that cause oxidative DNA damage. For this reason, cells have evolved mechanisms to deal with excess nickel. Efflux systems include members of the Resistance-Nodulation-cell Division (RND) protein family, P-type ATPases, cation diffusion facilitators (CDF) and other resistance factors. Nickel-specific exporters have been characterized in Cupravidus metallidurans, Helicobacter pylori, Achromobacter xylosoxidans, Serratia marcenses and Escherichia coli.
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Four periodically time-varying methane–air laminar coflow jet diffusion flames, each forced by pulsating the fuel jet's exit velocity Uj sinusoidally with a different modulation frequency wj and with a 50% amplitude variation, have been computed. Combustion of methane has been modeled by using a chemical mechanism with 15 species and 42 reactions, and the solution of the unsteady Navier–Stokes equations has been obtained numerically by using a modified vorticity-velocity formulation in the limit of low Mach number. The effect of wj on temperature and chemistry has been studied in detail. Three different regimes are found depending on the flame's Strouhal number S=awj/Uj, with a denoting the fuel jet radius. For small Strouhal number (S=0.1), the modulation introduces a perturbation that travels very far downstream, and certain variables oscillate at the frequency imposed by the fuel jet modulation. As the Strouhal number grows, the nondimensional frequency approaches the natural frequency of oscillation of the flickering flame (S≃0.2). A coupling with the pulsation frequency enhances the effect of the imposed modulation and a vigorous pinch-off is observed for S=0.25 and S=0.5. Larger values of S confine the oscillation to the jet's near-exit region, and the effects of the pulsation are reduced to small wiggles in the temperature and concentration values. Temperature and species mass fractions change appreciably near the jet centerline, where variations of over 2% for the temperature and 15% and 40% for the CO and OH mass fractions, respectively, are found. Transverse to the jet movement, however, the variations almost disappear at radial distances on the order of the fuel jet radius, indicating a fast damping of the oscillation in the spanwise direction.
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In this paper we consider a general system of reaction-diffusion equations and introduce a comparison method to obtain qualitative properties of its solutions. The comparison method is applied to study the stability of homogeneous steady states and the asymptotic behavior of the solutions of different systems with a chemotactic term. The theoretical results obtained are slightly modified to be applied to the problems where the systems are coupled in the differentiated terms and / or contain nonlocal terms. We obtain results concerning the global stability of the steady states by comparison with solutions of Ordinary Differential Equations.
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In some countries photovoltaic (PV) technology is at a stage of development at which it can compete with conventional electricity sources. A case in point is Germany where PV market has reached a mature stage. As a manifest of this, the German government has recently reduced the feed-in-tariff, which had been the strongest driver of PV diffusion. This development raises a fundamental question: Why would potential adopters be motivated to adopt PV when feed-in tariff diminishes? The point of departure for the literature on diffusion of PV has been on the effect of subsidies but little attention has paid to adopter motives when the policy support is scaled down. This paper presents an in-depth analysis of the adopter motives for photovoltaic applications. Anchored in an extensive exploratory case study we provide an encompassing explanation of roles of policy, adopters and system suppliers on diffusion of PV.