Sensitivity to lunar cycles prior to the 2007 eruption of Ruapehu volcano

A long-standing question in Earth Science is the extent to which seismic and volcanic activity can be regulated by tidal stresses, a repeatable and predictable external excitation induced by the Moon-Sun gravitational force. Fortnightly tides, a similar to 14-day amplitude modulation of the daily tidal stresses that is associated to lunar cycles, have been suggested to affect volcano dynamics. However, previous studies found contradictory results and remain mostly inconclusive. Here we study how fortnightly tides have affected Ruapehu volcano (New Zealand) from 2004 to 2016 by analysing the rolling correlation between lunar cycles and seismic amplitude recorded close to the crater. The long-term (similar to 1-year) correlation is found to increase significantly (up to confidence level of 5-sigma) during the similar to 3 months preceding the 2007 phreatic eruption of Ruapehu, thus revealing that the volcano is sensitive to fortnightly tides when it is prone to explode. We show through a mechanistic model that the real-time monitoring of seismic sensitivity to lunar cycles may help to detect the clogging of active volcanic vents, and thus to better forecast phreatic volcanic eruptions

Computational convergence of the path integral for real dendritic morphologies

Neurons are characterised by a morphological structure unique amongst biological cells, the core of which is the dendritic tree. The vast number of dendritic geometries, combined with heterogeneous properties of the cell membrane, continue to challenge scientists in predicting neuronal input-output relationships, even in the case of sub-threshold dendritic currents. The Green’s function obtained for a given dendritic geometry provides this functional relationship for passive or quasi-active dendrites and can be constructed by a sum-over-trips approach based on a path integral formalism. In this paper, we introduce a number of efficient algorithms for realisation of the sum-over-trips framework and investigate the convergence of these algorithms on different dendritic geometries. We demonstrate that the convergence of the trip sampling methods strongly depends on dendritic morphology as well as the biophysical properties of the cell membrane. For real morphologies, the number of trips to guarantee a small convergence error might become very large and strongly affect computational efficiency. As an alternative, we introduce a highly-efficient matrix method which can be applied to arbitrary branching structures

Galileo: Le Partenariat Public-Prive a l'Epreuve du "Juste Retour". = The public-private partnership (PPP) put to the test of "fair return". Bruges Political Research Papers No. 11, February 2010

No abstract

Officers and Directors of ASBMT

catalogue d'expositionNational audienceLa pratique des bains de mer est avérée à La Rochelle à partir des années 1770, dans le secteur de l'actuelle plage de la Concurrence. Mais les noyades ne sont pas rares et le corps de ville met en place, à terre et sur l'eau, un dispositif de secours aux noyés

The use of mixture density networks in the emulation of complex epidemiological individual-based models

Complex, highly-computational, individual-based models are abundant in epidemiology. For epidemics such as macro-parasitic diseases, detailed modelling of human behaviour and pathogen life-cycle are required in order to produce accurate results. This can often lead to models that are computationally-expensive to analyse and perform model fitting, and often require many simulation runs in order to build up sufficient statistics. Emulation can provide a more computationally-efficient output of the individual-based model, by approximating it using a statistical model. Previous work has used Gaussian processes (GPs) in order to achieve this, but these can not deal with multi-modal, heavy-tailed, or discrete distributions. Here, we introduce the concept of a mixture density network (MDN) in its application in the emulation of epidemiological models. MDNs incorporate both a mixture model and a neural network to provide a flexible tool for emulating a variety of models and outputs. We develop an MDN emulation methodology and demonstrate its use on a number of simple models incorporating both normal, gamma and beta distribution outputs. We then explore its use on the stochastic SIR model to predict the final size distribution and infection dynamics. MDNs have the potential to faithfully reproduce multiple outputs of an individual-based model and allow for rapid analysis from a range of users. As such, an open-access library of the method has been released alongside this manuscript

Expectations for observation of top-quark pair production in the dileptonic final state in early CMS data at √s =10 TeV

The dileptonic decay channel of the top-quark pair production at LHC is the best candidate for an early observation of a process involving top-quark. Due to its clean signature (two isolated and high-pT leptons with opposite charge, two b-jets and missing transverse energy) and its large cross-section, the expected background events can be sufficiently reduced to allow a rediscovery after few inverse picobarns of integrated luminosity. This presentation describes the result of a Monte Carlo study done at √s = 10TeV under the assumption 10 pb−1 of integrated luminosity in the CMS experiment. It presents the expected precision on the cross-section measurement and covers the description of the simulation and the backgrounds, the event selection, the data-driven background estimation methods, the systematic uncertainties and two alternative analyses (tracker-based and b-quark content)

The design and construction of a spectrograph and the investigation of the spectral sensitivity of various photographic emulsions

Thesis (M.A.)--Boston UniversityThe spectral sensitivity of five film types was measured over a region of 3380 to 6000 Angstroms. Curves of sensitivity vs wave length were produced for each film. The method of producing these curves was to expose the film to the continuous spectrum in the spectrograph, develope and make a microdensitometer trace in order to secure a density vs wave length curve. From this curve densities at various specified wave lengths were obtained. Using the Eastman IB sensitometer H and D curves were obtained throughout the wave length interval of interest. From these curves a difference in exposure, Δlog Hλ, is obtained: Δlog Hλ= log HD - log Hλ where log HD is the irradiance required to give a density of 1.00 and Hλ the irradiance of the same wave length in the spectrograph. The spectral radiancy ratio Rλ/Rλmax obtained from the black body radiation curve for the source is corrected by the Δlog Hλ value log H = Δlog Hλ + log Rλ/Rλmax The spectral sensitivity S, is the reciprocal of H. A special spectrograph was designed and built for this work. It was designed to use a concave grating so that glass elements would not interfere with wave lengths below the 4000 Angstrom point. The film was scanned in a recording densitometer to give a trace of density vs wave length. The H and D curves for the various wave lengths of interest were obtained on an Eastman IB sensitometer. The Wratten 78AA conversion filter was replaced by narrow band interference filters. The sensitometric strips made on the sensitometer were read on an Ansco-Macbeth densitometer. The five films under consideration were Eastman Tri X and Panatomic X, Ansco 569 and D 643A, and Illford BY 2670. All films were processed in Eastman D-19 developer at 68° Fahrenheit using standard processing procedures

Neuronal computation on complex dendritic morphologies

When we think about neural cells, we immediately recall the wealth of electrical behaviour which, eventually, brings about consciousness. Hidden deep in the frequencies and timings of action potentials, in subthreshold oscillations, and in the cooperation of tens of billions of neurons, are synchronicities and emergent behaviours that result in high-level, system-wide properties such as thought and cognition. However, neurons are even more remarkable for their elaborate morphologies, unique among biological cells. The principal, and most striking, component of neuronal morphologies is the dendritic tree. Despite comprising the vast majority of the surface area and volume of a neuron, dendrites are often neglected in many neuron models, due to their sheer complexity. The vast array of dendritic geometries, combined with heterogeneous properties of the cell membrane, continue to challenge scientists in predicting neuronal input-output relationships, even in the case of subthreshold dendritic currents. In this thesis, we will explore the properties of neuronal dendritic trees, and how they alter and integrate the electrical signals that diffuse along them. After an introduction to neural cell biology and membrane biophysics, we will review Abbott's dendritic path integral in detail, and derive the theoretical convergence of its infinite sum solution. On certain symmetric structures, closed-form solutions will be found; for arbitrary geometries, we will propose algorithms using various heuristics for constructing the solution, and assess their computational convergences on real neuronal morphologies. We will demonstrate how generating terms for the path integral solution in an order that optimises convergence is non-trivial, and how a computationally-significant number of terms is required for reasonable accuracy. We will, however, derive a highly-efficient and accurate algorithm for application to discretised dendritic trees. Finally, a modular method for constructing a solution in the Laplace domain will be developed