Multidimensional modelling of X-ray spectra for AGN accretion disc outflows - III. Application to a hydrodynamical simulation

We perform multidimensional radiative transfer simulations to compute spectra for a hydrodynamical simulation of a line-driven accretion disc wind from an active galactic nucleus. The synthetic spectra confirm expectations from parametrized models that a disc wind can imprint a wide variety of spectroscopic signatures including narrow absorption lines, broad emission lines and a Compton hump. The formation of these features is complex with contributions originating from many of the different structures present in the hydrodynamical simulation. In particular, spectral features are shaped both by gas in a successfully launched outflow and in complex flows where material is lifted out of the disc plane but ultimately falls back. We also confirm that the strong Fe Ka line can develop a weak, red-skewed line wing as a result of Compton scattering in the outflow. In addition, we demonstrate that X-ray radiation scattered and reprocessed in the flow has a pivotal part in both the spectrum formation and determining the ionization conditions in the wind. We find that scattered radiation is rather effective in ionizing gas which is shielded from direct irradiation from the central source. This effect likely makes the successful launching of a massive disc wind somewhat more challenging and should be considered in future wind simulations. © 2010 The Authors. Journal compilation © 2010 RAS.

Multidimensional modelling of X-ray spectra for AGN accretion disc outflows

We use a multidimensional Monte Carlo code to compute X-ray spectra for a variety of active galactic nucleus (AGN) disc-wind outflow geometries. We focus on the formation of blueshifted absorption features in the Fe K band and show that line features similar to those which have been reported in observations are often produced for lines of sight through disc-wind geometries. We also discuss the formation of other spectral features in highly ionized outflows. In particular, we show that, for sufficiently high wind densities, moderately strong Fe K emission lines can form and that electron scattering in the flow may cause these lines to develop extended red wings. We illustrate the potential relevance of such models to the interpretation of real X-ray data by comparison with observations of a well-known AGN, Mrk 766. Journal compilation © 2008 RAS.

Multidimensional modelling of X-ray spectra for AGN accretion disc outflows - II

Highly ionized fast accretion disc winds have been suggested as an explanation for a variety of observed absorption and emission features in the X-ray spectra of active galactic nuclei. Simple estimates have suggested that these flows may be massive enough to carry away a significant fraction of the accretion energy and could be involved in creating the link between supermassive black holes and their host galaxies. However, testing these hypotheses, and quantifying the outflow signatures, requires high-quality theoretical spectra for comparison with observations. Here, we describe extensions of our Monte Carlo radiative transfer code that allow us to generate realistic theoretical spectra for a much wider variety of disc wind models than that was possible in our previous work. In particular, we have expanded the range of atomic physics simulated by the code so that L- and M-shell ions can now be included. We have also substantially improved our treatment of both ionization and radiative heating such that we are now able to compute spectra for outflows containing far more diverse plasma conditions. We present example calculations that illustrate the variety of spectral features predicted by parametrized outflow models and demonstrate their applicability to the interpretation of data by comparison with observations of the bright quasar PG1211+143. We find that the major features in the observed 2-10 keV spectrum of this object can be well reproduced by our spectra, confirming that it likely hosts a massive outflow. © 2010 The Authors. Journal compilation © 2010 RAS.

Assessing agriculture-water relationships: a Pan-European multidimensional modelling approach.

This paper aims at developing a simulation framework to jointly assess agricultural and water issues. While the strong linkages between water, food, and the environment call for an integrated and multidisciplinary modelling approach, a complete and consistent modelling system to evaluate food-water relationships in Europe was missing so far. The spatial economic simulation model for agriculture CAPRI, which comprises a set of environmental indicators to assess food-environment interrelations within European regions, has been extended to account for food-water links. This modelling framework enables simulating the potential impact of climate change and water availability on agricultural production at the EU regional level, as well as looking at the sustainable use of water, the implementation of water policies or the integration of water issues in the Common Agricultural Policy

Joint assessment of water and agricultural policies: a Pan-European multidimensional modelling approach.

Sustainability and the food-water-environment nexus. Food-water linkages in global agro-economic models. The CAPRI water module. Potential to jointly assess food and water policies. Pilot case study. Further development.

Multidimensional modelling to investigate interspecies hydrogen transfer in anaerobic biofilms

Anaerobic digestion is a multistep process, mediated by a functionally and phylogenetically diverse microbial population. One of the crucial steps is oxidation of organic acids, with electron transfer via hydrogen or formate from acetogenic bacteria to methanogens. This syntrophic microbiological process is strongly restricted by a thermodynamic limitation on the allowable hydrogen or formate concentration. In order to study this process in more detail, we developed an individual-based biofilm model which enables to describe the processes at a microbial resolution. The biochemical model is the ADM1, implemented in a multidimensional domain. With this model, we evaluated three important issues for the syntrophic relationship: (i) is there a fundamental difference in using hydrogen or formate as electron carrier? (ii) Does a thermodynamic-based inhibition function produced substantially different results from an empirical function? and; (iii) Does the physical colocation of acetogens and methanogens follow directly from a general model. Hydrogen or formate as electron carrier had no substantial impact on model results. Standard inhibition functions or thermodynamic inhibition function gave similar results at larger substrate field grid sizes (> 10 mu m), but at smaller grid sizes, the thermodynamic-based function reduced the number of cells with long interspecies distances (> 2.5 mu m). Therefore, a very fine grid resolution is needed to reflect differences between the thermodynamic function, and a more generic inhibition form. The co-location of syntrophic bacteria was well predicted without a need to assume a microbiological based mechanism (e.g., through chemotaxis) of biofilm formation.

Towards a model-driven engineering approach of data mining

Nowadays, data mining is based on low-level specications of the employed techniques typically bounded to a specic analysis platform. Therefore, data mining lacks a modelling architecture that allows analysts to consider it as a truly software-engineering process. Here, we propose a model-driven approach based on (i) a conceptual modelling framework for data mining, and (ii) a set of model transformations to automatically generate both the data under analysis (via data-warehousing technology) and the analysis models for data mining (tailored to a specic platform). Thus, analysts can concentrate on the analysis problem via conceptual data-mining models instead of low-level programming tasks related to the underlying-platform technical details. These tasks are now entrusted to the model-transformations scaffolding.

Integrating the development of data mining and data warehouses via model-driven engineering

Data mining is one of the most important analysis techniques to automatically extract knowledge from large amount of data. Nowadays, data mining is based on low-level specifications of the employed techniques typically bounded to a specific analysis platform. Therefore, data mining lacks a modelling architecture that allows analysts to consider it as a truly software-engineering process. Bearing in mind this situation, we propose a model-driven approach which is based on (i) a conceptual modelling framework for data mining, and (ii) a set of model transformations to automatically generate both the data under analysis (that is deployed via data-warehousing technology) and the analysis models for data mining (tailored to a specific platform). Thus, analysts can concentrate on understanding the analysis problem via conceptual data-mining models instead of wasting efforts on low-level programming tasks related to the underlying-platform technical details. These time consuming tasks are now entrusted to the model-transformations scaffolding. The feasibility of our approach is shown by means of a hypothetical data-mining scenario where a time series analysis is required.

Modelling software quality : a multidimensional approach

Les sociétés modernes dépendent de plus en plus sur les systèmes informatiques et ainsi, il y a de plus en plus de pression sur les équipes de développement pour produire des logiciels de bonne qualité. Plusieurs compagnies utilisent des modèles de qualité, des suites de programmes qui analysent et évaluent la qualité d'autres programmes, mais la construction de modèles de qualité est difficile parce qu'il existe plusieurs questions qui n'ont pas été répondues dans la littérature. Nous avons étudié les pratiques de modélisation de la qualité auprès d'une grande entreprise et avons identifié les trois dimensions où une recherche additionnelle est désirable : Le support de la subjectivité de la qualité, les techniques pour faire le suivi de la qualité lors de l'évolution des logiciels, et la composition de la qualité entre différents niveaux d'abstraction. Concernant la subjectivité, nous avons proposé l'utilisation de modèles bayésiens parce qu'ils sont capables de traiter des données ambiguës. Nous avons appliqué nos modèles au problème de la détection des défauts de conception. Dans une étude de deux logiciels libres, nous avons trouvé que notre approche est supérieure aux techniques décrites dans l'état de l'art, qui sont basées sur des règles. Pour supporter l'évolution des logiciels, nous avons considéré que les scores produits par un modèle de qualité sont des signaux qui peuvent être analysés en utilisant des techniques d'exploration de données pour identifier des patrons d'évolution de la qualité. Nous avons étudié comment les défauts de conception apparaissent et disparaissent des logiciels. Un logiciel est typiquement conçu comme une hiérarchie de composants, mais les modèles de qualité ne tiennent pas compte de cette organisation. Dans la dernière partie de la dissertation, nous présentons un modèle de qualité à deux niveaux. Ces modèles ont trois parties: un modèle au niveau du composant, un modèle qui évalue l'importance de chacun des composants, et un autre qui évalue la qualité d'un composé en combinant la qualité de ses composants. L'approche a été testée sur la prédiction de classes à fort changement à partir de la qualité des méthodes. Nous avons trouvé que nos modèles à deux niveaux permettent une meilleure identification des classes à fort changement. Pour terminer, nous avons appliqué nos modèles à deux niveaux pour l'évaluation de la navigabilité des sites web à partir de la qualité des pages. Nos modèles étaient capables de distinguer entre des sites de très bonne qualité et des sites choisis aléatoirement. Au cours de la dissertation, nous présentons non seulement des problèmes théoriques et leurs solutions, mais nous avons également mené des expériences pour démontrer les avantages et les limitations de nos solutions. Nos résultats indiquent qu'on peut espérer améliorer l'état de l'art dans les trois dimensions présentées. En particulier, notre travail sur la composition de la qualité et la modélisation de l'importance est le premier à cibler ce problème. Nous croyons que nos modèles à deux niveaux sont un point de départ intéressant pour des travaux de recherche plus approfondis.

A taxonomy for wavelet neural networks applied to nonlinear modelling

This article presents a novel classification of wavelet neural networks based on the orthogonality/non-orthogonality of neurons and the type of nonlinearity employed. On the basis of this classification different network types are studied and their characteristics illustrated by means of simple one-dimensional nonlinear examples. For multidimensional problems, which are affected by the curse of dimensionality, the idea of spherical wavelet functions is considered. The behaviour of these networks is also studied for modelling of a low-dimension map.

Multidimensional simulations of radiative transfer in Type Ia supernovae

A three-dimensional Monte Carlo code for modelling radiation transport in Type Ia supernovae is described. In addition to tracking Monte Carlo quanta to follow the emission, scattering and deposition of radiative energy, a scheme involving volume-based Monte Carlo estimators is used to allow properties of the emergent radiation field to be extracted for specific viewing angles in a multidimensional structure. This eliminates the need to compute spectra or light curves by angular binning of emergent quanta. The code is applied to two test problems to illustrate consequences of multidimensional structure on the modelling of light curves. First, elliptical models are used to quantify how large-scale asphericity can introduce angular dependence to light curves. Secondly, a model which incorporates complex structural inhomogeneity, as predicted by modern explosion models, is used to investigate how such structure may affect light-curve properties. © 2006 RAS.

Self-consistent modelling of line-driven hot-star winds with Monte Carlo radiation hydrodynamics

Radiative pressure exerted by line interactions is a prominent driver of outflows in astrophysical systems, being at work in the outflows emerging from hot stars or from the accretion discs of cataclysmic variables, massive young stars and active galactic nuclei. In this work, a new radiation hydrodynamical approach to model line-driven hot-star winds is presented. By coupling a Monte Carlo radiative transfer scheme with a finite volume fluid dynamical method, line-driven mass outflows may be modelled self-consistently, benefiting from the advantages of Monte Carlo techniques in treating multiline effects, such as multiple scatterings, and in dealing with arbitrary multidimensional configurations. In this work, we introduce our approach in detail by highlighting the key numerical techniques and verifying their operation in a number of simplified applications, specifically in a series of self-consistent, one-dimensional, Sobolev-type, hot-star wind calculations. The utility and accuracy of our approach are demonstrated by comparing the obtained results with the predictions of various formulations of the so-called CAK theory and by confronting the calculations with modern sophisticated techniques of predicting the wind structure. Using these calculations, we also point out some useful diagnostic capabilities our approach provides. Finally, we discuss some of the current limitations of our method, some possible extensions and potential future applications.

Multiple linear and principal component regressions for modelling ecotoxicity bioassay response

The ecotoxicological response of the living organisms in an aquatic system depends on the physical, chemical and bacteriological variables, as well as the interactions between them. An important challenge to scientists is to understand the interaction and behaviour of factors involved in a multidimensional process such as the ecotoxicological response.With this aim, multiple linear regression (MLR) and principal component regression were applied to the ecotoxicity bioassay response of Chlorella vulgaris and Vibrio fischeri in water collected at seven sites of Leça river during five monitoring campaigns (February, May, June, August and September of 2006). The river water characterization included the analysis of 22 physicochemical and 3 microbiological parameters. The model that best fitted the data was MLR, which shows: (i) a negative correlation with dissolved organic carbon, zinc and manganese, and a positive one with turbidity and arsenic, regarding C. vulgaris toxic response; (ii) a negative correlation with conductivity and turbidity and a positive one with phosphorus, hardness, iron, mercury, arsenic and faecal coliforms, concerning V. fischeri toxic response. This integrated assessment may allow the evaluation of the effect of future pollution abatement measures over the water quality of Leça River.

Multidimensional Adaptations for Open Learning Management Systems

Our work is focused on alleviating the workload for designers of adaptive courses on the complexity task of authoring adaptive learning designs adjusted to specific user characteristics and the user context. We propose an adaptation platform that consists in a set of intelligent agents where each agent carries out an independent adaptation task. The agents apply machine learning techniques to support the user modelling for the adaptation process

A Web Map Service implementation for the visualization of multidimensional gridded environmental data

We describe ncWMS, an implementation of the Open Geospatial Consortium’s Web Map Service (WMS) specification for multidimensional gridded environmental data. ncWMS can read data in a large number of common scientific data formats – notably the NetCDF format with the Climate and Forecast conventions – then efficiently generate map imagery in thousands of different coordinate reference systems. It is designed to require minimal configuration from the system administrator and, when used in conjunction with a suitable client tool, provides end users with an interactive means for visualizing data without the need to download large files or interpret complex metadata. It is also used as a “bridging” tool providing interoperability between the environmental science community and users of geographic information systems. ncWMS implements a number of extensions to the WMS standard in order to fulfil some common scientific requirements, including the ability to generate plots representing timeseries and vertical sections. We discuss these extensions and their impact upon present and future interoperability. We discuss the conceptual mapping between the WMS data model and the data models used by gridded data formats, highlighting areas in which the mapping is incomplete or ambiguous. We discuss the architecture of the system and particular technical innovations of note, including the algorithms used for fast data reading and image generation. ncWMS has been widely adopted within the environmental data community and we discuss some of the ways in which the software is integrated within data infrastructures and portals.