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la remarque de la présidente commençait par redonner le nom complet de la théorie de la conceptualisation dans l'action, ce qui lui permettait d'interroger la méthodologie développée par William mais après elle lui indiquait trois pistes majeures pour revoir sa méthodologie ; en réponse William s'est un peu tiré une balle dans le pied en disant qu'il avait effectivement pas mis en oeuvre une méthodologie pertinente, je lui ai suggéré de ré-écouter l'enregistrement de ce qu'elle lui avait dit pour mieux le comprendre (voyez-vous le texte que je produis en cours d'écriture ?)
essai
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Tout ceci demande réflexion
Mesozoic Dipole Low in Relation to the Cretaceous Normal Superchron
Abstract
The behavior of a Mesozoic dipole low and a long interval of normal polarity in the Cretaceous can be described in relation to the geodynamo. Paleomagnetic data has been critical in understanding the behavior of the Earth’s geomagnetic polarity reversals during the Mesozoic. Looking into the Cretaceous Normal Superchron in relation to the Mesozoic dipole low is may potentially help understand the behaviors of the geomagnetic polarity reversals in the future and better understand the geodynamo. Numerous studies and hypotheses work to understand the extrinsic mechanisms that could affect the behavior in a way that is predictable. Studies include researching electromagnetism, geomagnetic polarity reversal evidence, theories for polarity reversal behaviors, and models to understand the complexities through studying the Cretaceous Normal Superchron. Understanding polarity reversals is important to understanding the geodynamo.
Introduction and Background
Diffusion weighted magnetic resonance imaging (MRI) is an imaging technique that has allowed unique insights into both the microstructural properties and the organization of cerebral white matter tissues. This technique measures the relative displacement of water molecules in the biological tissues and is highly sensitive to any microstructure which restricts this diffusion. Diffusion MRI is particularly useful in tissues with a high degree of organization, such as the white matter tissues of the brain. This organization means that the restricted diffusion is coherent and measuring the diffusion signal allows us to not only infer the presence or absence of diffusion restricting elements, but also how they’re organized and other tissue properties. For example, in the white matter of the brain, the diffusion characteristics of the tissue can give us insight into the organization of axons, the level of myelination of those axons and possible pathology in these tissues. The level of information contained in diffusion imaging makes this imaging technique both powerful and challenging to work with.
Diffusion images cannot generally be read by human specialists, but instead must be modeled using computational techniques. These modeling techniques can produce either composite images or 3d renderings, which can then be used in clinic or for research, or quantitative measurements which can then be used as biomarkers of brain heath or disease progression. However, in order to maximize the utility of diffusion imaging one needs to pick the right imaging protocol and modeling approach for a given problem. The modeling of the diffusion signal can be approached both as a local problem, modeling the characteristics of a given brain region or voxel using the diffusion signal specific to that structure. The model can also be thought of as a whole brain model, trying not only the estimate local tissue properties, but also the organization of connections and pathways that contribute to the architecture of the whole brain. Fiber tracking, or tractography, describes the process of using local directional information from brain tissues to build reconstruct these tracts and pathways. These techniques are fairly new and actively being developed and have had some success in describing the organizational properties of the human brain, or the human connectome.
In this work, I present some common diffusion modeling techniques that have been applied to diffusion MRI, focused specifically on modeling techniques that estimate directional information which can be used for fiber tacking. I present and compare several methods for estimating diffusion MRI noise and in the process discuss how noise estimates for diffusion MRI acquisitions can help identify model failures inform the choice of model for an acquisition type. I further present a framework for thinking about and implement fiber tract reconstruction from diffusion MRI data. Finally I present an application of these methods to a large, public data set for the purpose of understanding the impact of high body mass index on brain health.
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Sucesiones y Series
Sucesiones y Series
Sucesiones, series e integrales
Sucesiones
Sucesiones ARITMÉTICAS
Existen 3 formas de representar una sucesión:
La forma Explícita y la forma Recursiva.
n (Nº del término) | an (n-simo término de la sucesión) |
1 | 3 |
2 | 5 |
3 | 7 |
Comparação entre algoritmo genético sequencial e paralelo
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Superluminous Supernovae
Superluminous supernovae (SLSNe), as the name attests, are supernovae that are brighter than usual. As a result of the overly broad name, the category is a catch-all describing several classes of supernovae – some with hydrogen, some without, some interacting, some probably not. A few authors have defined SLSNe as those brighter than M = −21 at peak, though this arbitrary cut could leave out related physical phenomena. Instead, I define SLSNe as luminous SNe which cannot be explained by the power sources fueling traditional (Types I and II) supernovae: radioactive decay from a moderate amount of elements synthesized in the explosion, the energy deposited by a shock unbinding the star, or interaction with moderate but obvious amounts of circumstellar material (CSM) previously lost by the supernova progenitor or a companion.
This last point creates a gray area. Should Type IIn supernovae count as SLSNe? Type IIn supernovae are those with a strong blue continuum at early times, and narrow and intermediate width hydrogen emission lines at some points in their spectroscopic evolution. They are thought to be the collapse of massive stars whose ejecta shock CSM. On one hand, they have been recognized as a class since the 1980s, a large and diverse one, and the source of their luminosity is not a mystery. On the other, some SNe IIn are so bright that they have been considered SLSNe [e.g. SN 2006gy \citep{Smith_2007,Ofek_2007}, which reached a peak absolute magnitude of −22]. A complicating factor is that interaction should be considered as a possible power source for SLSNe, whether or not the spectra show narrow lines. Here I compromise – I will generally not include clear SNe IIn, as their power source is not a mystery. However, I will mention a few extraordinary cases where appropriate, and discuss interaction as a possible power source.
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8 Historic Elections in Science
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Generos periodísticos
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