Ensemble wind forecasting based on the HARMONIE model and adaptive finite elements in complex orography

[EN]Ensemble forecasting is a methodology to deal with uncertainties in the numerical wind prediction. In this work we propose to apply ensemble methods to the adaptive wind forecasting model presented in. The wind field forecasting is based on a mass-consistent model and a log-linear wind profile using as input data the resulting forecast wind from Harmonie, a Non-Hydrostatic Dynamic model used experimentally at AEMET with promising results. The mass-consistent model parameters are estimated by using genetic algorithms. The mesh is generated using the meccano method and adapted to the geometry…

Intermittent spike-wave dynamics in a heterogeneous, spatially extended neural mass model

Generalised epileptic seizures are frequently accompanied by sudden, reversible transitions from low amplitude, irregular background activity to high amplitude, regular spike-wave discharges (SWD) in the EEG. The underlying mechanisms responsible for SWD generation and for the apparently spontaneous transitions to SWD and back again are still not fully understood. Specifically, the role of spatial cortico-cortical interactions in ictogenesis is not well studied. We present a macroscopic, neural mass model of a cortical column which includes two distinct time scales of inhibition. This model can produce both an oscillatory background and a pathological SWD rhythm. We demonstrate that coupling two of these cortical columns can lead to a bistability between out-of-phase, low amplitude background dynamics and in-phase, high amplitude SWD activity. Stimuli can cause state-dependent transitions from background into SWD. In an extended local area of cortex, spatial heterogeneities in a model parameter can lead to spontaneous reversible transitions from a desynchronised background to synchronous SWD due to intermittency. The deterministic model is therefore capable of producing absence seizure-like events without any time dependent adjustment of model parameters. The emergence of such mechanisms due to spatial coupling demonstrates the importance of spatial interactions in modelling ictal dynamics, and in the study of ictogenesis.

MODELING DYNAMICS OF OZONE AND NITROGEN OXIDES AT SUMMIT, GREENLAND WITH A 1-D PROCESS-SCALE MODEL

This work presents a 1-D process scale model used to investigate the chemical dynamics and temporal variability of nitrogen oxides (NOx) and ozone (O3) within and above snowpack at Summit, Greenland for March-May 2009 and estimates surface exchange of NOx between the snowpack and surface layer in April-May 2009. The model assumes the surface of snowflakes have a Liquid Like Layer (LLL) where aqueous chemistry occurs and interacts with the interstitial air of the snowpack. Model parameters and initialization are physically and chemically representative of snowpack at Summit, Greenland and model results are compared to measurements of NOx and O3 collected by our group at Summit, Greenland from 2008-2010. The model paired with measurements confirmed the main hypothesis in literature that photolysis of nitrate on the surface of snowflakes is responsible for nitrogen dioxide (NO2) production in the top ~50 cm of the snowpack at solar noon for March – May time periods in 2009. Nighttime peaks of NO2 in the snowpack for April and May were reproduced with aqueous formation of peroxynitric acid (HNO4) in the top ~50 cm of the snowpack with subsequent mass transfer to the gas phase, decomposition to form NO2 at nighttime, and transportation of the NO2 to depths of 2 meters. Modeled production of HNO4 was hindered in March 2009 due to the low production of its precursor, hydroperoxy radical, resulting in underestimation of nighttime NO2 in the snowpack for March 2009. The aqueous reaction of O3 with formic acid was the major sync of O3 in the snowpack for March-May, 2009. Nitrogen monoxide (NO) production in the top ~50 cm of the snowpack is related to the photolysis of NO2, which underrepresents NO in May of 2009. Modeled surface exchange of NOx in April and May are on the order of 1011 molecules m-2 s-1. Removal of measured downward fluxes of NO and NO2 in measured fluxes resulted in agreement between measured NOx fluxes and modeled surface exchange in April and an order of magnitude deviation in May. Modeled transport of NOx above the snowpack in May shows an order of magnitude increase of NOx fluxes in the first 50 cm of the snowpack and is attributed to the production of NO2 during the day from the thermal decomposition and photolysis of peroxynitric acid with minor contributions of NO from HONO photolysis in the early morning.

Prediction of PA12 melt viscosity in Laser Sintering by a Time and Temperature dependent rheological model

Ein auf Basis von Prozessdaten kalibriertes Viskositätsmodell wird vorgeschlagen und zur Vorhersage der Viskosität einer Polyamid 12 (PA12) Kunststoffschmelze als Funktion von Zeit, Temperatur und Schergeschwindigkeit angewandt. Im ersten Schritt wurde das Viskositätsmodell aus experimentellen Daten abgeleitet. Es beruht hauptsächlich auf dem drei-parametrigen Ansatz von Carreau, wobei zwei zusätzliche Verschiebungsfaktoren eingesetzt werden. Die Temperaturabhängigkeit der Viskosität wird mithilfe des Verschiebungsfaktors aT von Arrhenius berücksichtigt. Ein weiterer Verschiebungsfaktor aSC (Structural Change) wird eingeführt, der die Strukturänderung von PA12 als Folge der Prozessbedingungen beim Lasersintern beschreibt. Beobachtet wurde die Strukturänderung in Form einer signifikanten Viskositätserhöhung. Es wurde geschlussfolgert, dass diese Viskositätserhöhung auf einen Molmassenaufbau zurückzuführen ist und als Nachkondensation verstanden werden kann. Abhängig von den Zeit- und Temperaturbedingungen wurde festgestellt, dass die Viskosität als Folge des Molmassenaufbaus exponentiell gegen eine irreversible Grenze strebt. Die Geschwindigkeit dieser Nachkondensation ist zeit- und temperaturabhängig. Es wird angenommen, dass die Pulverbetttemperatur einen Molmassenaufbau verursacht und es damit zur Kettenverlängerung kommt. Dieser fortschreitende Prozess der zunehmenden Kettenlängen setzt molekulare Beweglichkeit herab und unterbindet die weitere Nachkondensation. Der Verschiebungsfaktor aSC drückt diese physikalisch-chemische Modellvorstellung aus und beinhaltet zwei zusätzliche Parameter. Der Parameter aSC,UL entspricht der oberen Viskositätsgrenze, wohingegen k0 die Strukturänderungsrate angibt. Es wurde weiterhin festgestellt, dass es folglich nützlich ist zwischen einer Fließaktivierungsenergie und einer Strukturänderungsaktivierungsenergie für die Berechnung von aT und aSC zu unterscheiden. Die Optimierung der Modellparameter erfolgte mithilfe eines genetischen Algorithmus. Zwischen berechneten und gemessenen Viskositäten wurde eine gute Übereinstimmung gefunden, so dass das Viskositätsmodell in der Lage ist die Viskosität einer PA12 Kunststoffschmelze als Folge eines kombinierten Lasersinter Zeit- und Temperatureinflusses vorherzusagen. Das Modell wurde im zweiten Schritt angewandt, um die Viskosität während des Lasersinter-Prozesses in Abhängigkeit von der Energiedichte zu berechnen. Hierzu wurden Prozessdaten, wie Schmelzetemperatur und Belichtungszeit benutzt, die mithilfe einer High-Speed Thermografiekamera on-line gemessen wurden. Abschließend wurde der Einfluss der Strukturänderung auf das Viskositätsniveau im Prozess aufgezeigt.

Simulating Carbon Stocks and Fluxes of an African Tropical Montane Forest with an Individual-Based Forest Model

Tropical forests are carbon-dense and highly productive ecosystems. Consequently, they play an important role in the global carbon cycle. In the present study we used an individual-based forest model (FORMIND) to analyze the carbon balances of a tropical forest. The main processes of this model are tree growth, mortality, regeneration, and competition. Model parameters were calibrated using forest inventory data from a tropical forest at Mt. Kilimanjaro. The simulation results showed that the model successfully reproduces important characteristics of tropical forests (aboveground biomass, stem size distribution and leaf area index). The estimated aboveground biomass (385 t/ha) is comparable to biomass values in the Amazon and other tropical forests in Africa. The simulated forest reveals a gross primary production of 24 tcha-1yr-1. Modeling above- and belowground carbon stocks, we analyzed the carbon balance of the investigated tropical forest. The simulated carbon balance of this old-growth forest is zero on average. This study provides an example of how forest models can be used in combination with forest inventory data to investigate forest structure and local carbon balances.

Development of a Novel Rabies Simulation Model for Application in a Non-endemic Environment.

Domestic dog rabies is an endemic disease in large parts of the developing world and also epidemic in previously free regions. For example, it continues to spread in eastern Indonesia and currently threatens adjacent rabies-free regions with high densities of free-roaming dogs, including remote northern Australia. Mathematical and simulation disease models are useful tools to provide insights on the most effective control strategies and to inform policy decisions. Existing rabies models typically focus on long-term control programs in endemic countries. However, simulation models describing the dog rabies incursion scenario in regions where rabies is still exotic are lacking. We here describe such a stochastic, spatially explicit rabies simulation model that is based on individual dog information collected in two remote regions in northern Australia. Illustrative simulations produced plausible results with epidemic characteristics expected for rabies outbreaks in disease free regions (mean R0 1.7, epidemic peak 97 days post-incursion, vaccination as the most effective response strategy). Systematic sensitivity analysis identified that model outcomes were most sensitive to seven of the 30 model parameters tested. This model is suitable for exploring rabies spread and control before an incursion in populations of largely free-roaming dogs that live close together with their owners. It can be used for ad-hoc contingency or response planning prior to and shortly after incursion of dog rabies in previously free regions. One challenge that remains is model parameterisation, particularly how dogs' roaming and contacts and biting behaviours change following a rabies incursion in a previously rabies free population.

An ordinal logistic regression model with misclassification of the outcome variable and categorical covariate

Ordinal outcomes are frequently employed in diagnosis and clinical trials. Clinical trials of Alzheimer's disease (AD) treatments are a case in point using the status of mild, moderate or severe disease as outcome measures. As in many other outcome oriented studies, the disease status may be misclassified. This study estimates the extent of misclassification in an ordinal outcome such as disease status. Also, this study estimates the extent of misclassification of a predictor variable such as genotype status. An ordinal logistic regression model is commonly used to model the relationship between disease status, the effect of treatment, and other predictive factors. A simulation study was done. First, data based on a set of hypothetical parameters and hypothetical rates of misclassification was created. Next, the maximum likelihood method was employed to generate likelihood equations accounting for misclassification. The Nelder-Mead Simplex method was used to solve for the misclassification and model parameters. Finally, this method was applied to an AD dataset to detect the amount of misclassification present. The estimates of the ordinal regression model parameters were close to the hypothetical parameters. β1 was hypothesized at 0.50 and the mean estimate was 0.488, β2 was hypothesized at 0.04 and the mean of the estimates was 0.04. Although the estimates for the rates of misclassification of X1 were not as close as β1 and β2, they validate this method. X 1 0-1 misclassification was hypothesized as 2.98% and the mean of the simulated estimates was 1.54% and, in the best case, the misclassification of k from high to medium was hypothesized at 4.87% and had a sample mean of 3.62%. In the AD dataset, the estimate for the odds ratio of X 1 of having both copies of the APOE 4 allele changed from an estimate of 1.377 to an estimate 1.418, demonstrating that the estimates of the odds ratio changed when the analysis includes adjustment for misclassification. ^

A multivariate frailty model for disease recurrences and survival

A multivariate frailty hazard model is developed for joint-modeling of three correlated time-to-event outcomes: (1) local recurrence, (2) distant recurrence, and (3) overall survival. The term frailty is introduced to model population heterogeneity. The dependence is modeled by conditioning on a shared frailty that is included in the three hazard functions. Independent variables can be included in the model as covariates. The Markov chain Monte Carlo methods are used to estimate the posterior distributions of model parameters. The algorithm used in present application is the hybrid Metropolis-Hastings algorithm, which simultaneously updates all parameters with evaluations of gradient of log posterior density. The performance of this approach is examined based on simulation studies using Exponential and Weibull distributions. We apply the proposed methods to a study of patients with soft tissue sarcoma, which motivated this research. Our results indicate that patients with chemotherapy had better overall survival with hazard ratio of 0.242 (95% CI: 0.094 - 0.564) and lower risk of distant recurrence with hazard ratio of 0.636 (95% CI: 0.487 - 0.860), but not significantly better in local recurrence with hazard ratio of 0.799 (95% CI: 0.575 - 1.054). The advantages and limitations of the proposed models, and future research directions are discussed. ^

LONGITUDINAL GROWTH PARAMETERS AND THEIR ECOLOGICAL DETERMINANTS (SEASONALITY)

The growth patterns of weight from birth through the first twelve months of life among rural Taiwanese infants were investigated with the following objectives: (i) compare each of the parameters of the Count model estimated for infants who were nutritionally at risk with those for a reference population from the United States; and (ii) within the Taiwanese infants, account for the variance in the growth patterns in the first and second six months of life on the basis of selected ecological factors.^ The significance between group differences were observed in the patterns of the weight growth in both linear growth and in the timing and the direction of velocity changes. A significant decline in growth velocity was observed among Taiwanese infants at about the fourth month of life. The decline is in keeping with a recent proposal made by J. C. Waterlow regarding the timing of change in growth velocity among nutritionally at risk populations in developing countries. The growth course of a nutritionally at risk infant during the first three months is apparently protected by the nurturance of the mother and innate biological properties of the infant.^ A highly significant portion of the growth variance in the second six months of life was accounted for by exogenous factors and biological factors related to the infant. Conversely, none of the growth variance in the first six months of life was accounted for by predictor variables. The most potent determinant of growth in the second six months of life was seasonality which represents a multiple environmental event.^ The model parameters estimated from the Count model represent different aspect of physical growth; yet the correlation coefficients between parameters b and c are high (r > .80). Clearly, the biological interpretation of the model parameters requires analysis of the whole function in the specific context of a given age period. ^

Modal analysis of buildings : quantification of uncertainties and model updating

Una estructura vibra con la suma de sus infinitos modos de vibración, definidos por sus parámetros modales (frecuencias naturales, formas modales y coeficientes de amortiguamiento). Estos parámetros se pueden identificar a través del Análisis Modal Operacional (OMA). Así, un equipo de investigación de la Universidad Politécnica de Madrid ha identificado las propiedades modales de un edificio de hormigón armado en Madrid con el método Identificación de los sub-espacios estocásticos (SSI). Para completar el estudio dinámico de este edificio, se ha desarrollado un modelo de elementos finitos (FE) de este edificio de 19 plantas. Este modelo se ha calibrado a partir de su comportamiento dinámico obtenido experimentalmente a través del OMA. Los objetivos de esta tesis son; (i) identificar la estructura con varios métodos de SSI y el uso de diferentes ventanas de tiempo de tal manera que se cuantifican incertidumbres de los parámetros modales debidos al proceso de estimación, (ii) desarrollar FEM de este edificio y calibrar este modelo a partir de su comportamiento dinámico, y (iii) valorar la bondad del modelo. Los parámetros modales utilizados en esta calibración han sido; espesor de las losas, densidades de los materiales, módulos de elasticidad, dimensiones de las columnas y las condiciones de contorno de la cimentación. Se ha visto que el modelo actualizado representa el comportamiento dinámico de la estructura con una buena precisión. Por lo tanto, este modelo puede utilizarse dentro de un sistema de monitorización estructural (SHM) y para la detección de daños. En el futuro, podrá estudiar la influencia de los agentes medioambientales, tales como la temperatura o el viento, en los parámetros modales. A structure vibrates according to the sum of its vibration modes, defined by their modal parameters (natural frequencies, damping ratios and modal shapes). These parameters can be identified through Operational Modal Analysis (OMA). Thus, a research team of the Technical University of Madrid has identified the modal properties of a reinforced-concrete-frame building in Madrid using the Stochastic Subspace Identification (SSI) method and a time domain technique for the OMA. To complete the dynamic study of this building, a finite element model (FE) of this 19-floor building has been developed throughout this thesis. This model has been updated from its dynamic behavior identified by the OMA. The objectives of this thesis are to; (i) identify the structure with several SSI methods and using different time blocks in such a way that uncertainties due to the modal parameter estimation are quantified, (ii) develop a FEM of this building and tune this model from its dynamic behavior, and (iii) Assess the quality of the model, the modal parameters used in this updating process have been; thickness of slabs, material densities, modulus of elasticity, column dimensions and foundation boundary conditions. It has been shown that the final updated model represents the structure with a very good accuracy. Thus, this model might be used within a structural health monitoring framework (SHM). The study of the influence of changing environmental factors (such as temperature or wind) on the model parameters might be considered as a future work.

Stability and sensitivity analysis of Be-CoDiS, an epidemiological model to predict the spread of human diseases between countries. Validation with data from the 2014-16 West African Ebola Virus Disease epidemic.

Ebola virus disease is a lethal human and primate disease that requires a particular attention from the international health authorities due to important recent outbreaks in some Western African countries and isolated cases in European and North-America continents. Regarding the emergency of this situation, various decision tools, such as mathematical models, were developed to assist the authorities to focus their efforts in important factors to eradicate Ebola. In a previous work, we have proposed an original deterministic spatial-temporal model, called Be-CoDiS (Between-Countries Disease Spread), to study the evolution of human diseases within and between countries by taking into consideration the movement of people between geographical areas. This model was validated by considering numerical experiments regarding the 2014-16 West African Ebola Virus Disease epidemic. In this article, we propose to perform a stability analysis of Be-CoDiS. Our first objective is to study the equilibrium states of simplified versions of this model, limited to the cases of one an two countries, and to determine their basic reproduction ratios. Then, in order to give some recommendations for the allocation of resources used to control the disease, we perform a sensitivity analysis of those basic reproduction ratios regarding the model parameters. Finally, we validate the obtained results by considering numerical experiments based on data from the 2014-16 West African Ebola Virus Disease epidemic.

Can model Hamiltonians describe the electron–electron interaction in π-conjugated systems?: PAH and graphene

Model Hamiltonians have been, and still are, a valuable tool for investigating the electronic structure of systems for which mean field theories work poorly. This review will concentrate on the application of Pariser–Parr–Pople (PPP) and Hubbard Hamiltonians to investigate some relevant properties of polycyclic aromatic hydrocarbons (PAH) and graphene. When presenting these two Hamiltonians we will resort to second quantisation which, although not the way chosen in its original proposal of the former, is much clearer. We will not attempt to be comprehensive, but rather our objective will be to try to provide the reader with information on what kinds of problems they will encounter and what tools they will need to solve them. One of the key issues concerning model Hamiltonians that will be treated in detail is the choice of model parameters. Although model Hamiltonians reduce the complexity of the original Hamiltonian, they cannot be solved in most cases exactly. So, we shall first consider the Hartree–Fock approximation, still the only tool for handling large systems, besides density functional theory (DFT) approaches. We proceed by discussing to what extent one may exactly solve model Hamiltonians and the Lanczos approach. We shall describe the configuration interaction (CI) method, a common technology in quantum chemistry but one rarely used to solve model Hamiltonians. In particular, we propose a variant of the Lanczos method, inspired by CI, that has the novelty of using as the seed of the Lanczos process a mean field (Hartree–Fock) determinant (the method will be named LCI). Two questions of interest related to model Hamiltonians will be discussed: (i) when including long-range interactions, how crucial is including in the Hamiltonian the electronic charge that compensates ion charges? (ii) Is it possible to reduce a Hamiltonian incorporating Coulomb interactions (PPP) to an 'effective' Hamiltonian including only on-site interactions (Hubbard)? The performance of CI will be checked on small molecules. The electronic structure of azulene and fused azulene will be used to illustrate several aspects of the method. As regards graphene, several questions will be considered: (i) paramagnetic versus antiferromagnetic solutions, (ii) forbidden gap versus dot size, (iii) graphene nano-ribbons, and (iv) optical properties.

Application of the Hidden Markov Model for Innovative Projects "Viability" Analysis

This master thesis deals with determining of innovative projects "viability". "Viability" is the probability of innovative project being implemented. Hidden Markov Models are used for evaluation of this factor. The problem of determining parameters of model, which produce given data sequence with the highest probability, are solving in this research. Data about innovative projects contained in reports of Russian programs "UMNIK", "START" and additional data obtained during study are used as input data for determining of model parameters. The Baum-Welch algorithm which is one implementation of expectation-maximization algorithm is used at this research for calculating model parameters. At the end part of the master thesis mathematical basics for practical implementation are given (in particular mathematical description of the algorithm and implementation methods for Markov models).