Modelling variation of needle density of Scots pine at high latitudes

[Abstract]

Spatiotemporal adaptive multiscale multiphysics simulations of two-phase flow

We present a spatiotemporal adaptive multiscale algorithm, which is based on the Multiscale Finite Volume method. The algorithm offers a very efficient framework to deal with multiphysics problems and to couple regions with different spatial resolution. We employ the method to simulate two-phase flow through porous media. At the fine scale, we consider a pore-scale description of the flow based on the Volume Of Fluid method. In order to construct a global problem that describes the coarse-scale behavior, the equations are averaged numerically with respect to auxiliary control volumes, and a Darcy-like coarse-scale model is obtained. The space adaptivity is based on the idea that a fine-scale description is only required in the front region, whereas the resolution can be coarsened elsewhere. Temporal adaptivity relies on the fact that the fine-scale and the coarse-scale problems can be solved with different temporal resolution (longer time steps can be used at the coarse scale). By simulating drainage under unstable flow conditions, we show that the method is able to capture the coarse-scale behavior outside the front region and to reproduce complex fluid patterns in the front region.

Modelling the effect of temperature on height increment of Scots pine at high latitudes

Abstract

Modelling of crowded polymers elucidate effects of double-strand breaks in topological domains of bacterial chromosomes.

Using numerical simulations of pairs of long polymeric chains confined in microscopic cylinders, we investigate consequences of double-strand DNA breaks occurring in independent topological domains, such as these constituting bacterial chromosomes. Our simulations show a transition between segregated and mixed state upon linearization of one of the modelled topological domains. Our results explain how chromosomal organization into topological domains can fulfil two opposite conditions: (i) effectively repulse various loops from each other thus promoting chromosome separation and (ii) permit local DNA intermingling when one or more loops are broken and need to be repaired in a process that requires homology search between broken ends and their homologous sequences in closely positioned sister chromatid.

Environmental data mining and modeling based on machine learning algorithms and geostatistics

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model-machine learning (ML) residuals sequential simulations-MLRSS. The models are based on multilayer perceptron and support vector regression ML algorithms used for modeling long-range spatial trends and sequential simulations of the residuals. NIL algorithms deliver non-linear solution for the spatial non-stationary problems, which are difficult for geostatistical approach. Geostatistical tools (variography) are used to characterize performance of ML algorithms, by analyzing quality and quantity of the spatially structured information extracted from data with ML algorithms. Sequential simulations provide efficient assessment of uncertainty and spatial variability. Case study from the Chernobyl fallouts illustrates the performance of the proposed model. It is shown that probability mapping, provided by the combination of ML data driven and geostatistical model based approaches, can be efficiently used in decision-making process. (C) 2003 Elsevier Ltd. All rights reserved.

Linear prediction application for modelling the relationships between a large number of stand characteristics of Norway spruce stands

Abstract

Individual-based modelling and simulation: towards a better understanding of growth dynamics from small inocula

Postprint (published version)

New directions in Stochastic Multicriteria Acceptability Analysis

Decisions taken in modern organizations are often multi-dimensional, involving multiple decision makers and several criteria measured on different scales. Multiple Criteria Decision Making (MCDM) methods are designed to analyze and to give recommendations in this kind of situations. Among the numerous MCDM methods, two large families of methods are the multi-attribute utility theory based methods and the outranking methods. Traditionally both method families require exact values for technical parameters and criteria measurements, as well as for preferences expressed as weights. Often it is hard, if not impossible, to obtain exact values. Stochastic Multicriteria Acceptability Analysis (SMAA) is a family of methods designed to help in this type of situations where exact values are not available. Different variants of SMAA allow handling all types of MCDM problems. They support defining the model through uncertain, imprecise, or completely missing values. The methods are based on simulation that is applied to obtain descriptive indices characterizing the problem. In this thesis we present new advances in the SMAA methodology. We present and analyze algorithms for the SMAA-2 method and its extension to handle ordinal preferences. We then present an application of SMAA-2 to an area where MCDM models have not been applied before: planning elevator groups for high-rise buildings. Following this, we introduce two new methods to the family: SMAA-TRI that extends ELECTRE TRI for sorting problems with uncertain parameter values, and SMAA-III that extends ELECTRE III in a similar way. An efficient software implementing these two methods has been developed in conjunction with this work, and is briefly presented in this thesis. The thesis is closed with a comprehensive survey of SMAA methodology including a definition of a unified framework.

Energy-based modelling and simulation of the interconnection of a back-to-back converter and a doubly-fed induction machine

This paper describes the port interconnection of two subsystems: a power electronics subsystem (a back-to-back AC/CA converter (B2B), coupled to a phase of the power grid), and an electromechanical subsystem (a doubly-fed induction machine (DFIM). The B2B is a variable structure system (VSS), due to presence of control-actuated switches: however, from a modelling simulation, as well as a control-design, point of view, it is sensible to consider modulated transformers (MTF in the bond graph language) instead of the pairs of complementary switches. The port-Hamiltonian models of both subsystems are presented and, using a power-preserving interconnection, the Hamiltonian description of the whole system is obtained; detailed bond graphs of all subsystems and the complete system are also provided. Using passivity-based controllers computed in the Hamiltonian formalism for both subsystems, the whole model is simulated; simulations are run to rest the correctness and efficiency of the Hamiltonian network modelling approach used in this work.

Quantitative modelling of transcription factor binding sites

Abstract One of the most important issues in molecular biology is to understand regulatory mechanisms that control gene expression. Gene expression is often regulated by proteins, called transcription factors which bind to short (5 to 20 base pairs),degenerate segments of DNA. Experimental efforts towards understanding the sequence specificity of transcription factors is laborious and expensive, but can be substantially accelerated with the use of computational predictions. This thesis describes the use of algorithms and resources for transcriptionfactor binding site analysis in addressing quantitative modelling, where probabilitic models are built to represent binding properties of a transcription factor and can be used to find new functional binding sites in genomes. Initially, an open-access database(HTPSELEX) was created, holding high quality binding sequences for two eukaryotic families of transcription factors namely CTF/NF1 and LEFT/TCF. The binding sequences were elucidated using a recently described experimental procedure called HTP-SELEX, that allows generation of large number (> 1000) of binding sites using mass sequencing technology. For each HTP-SELEX experiments we also provide accurate primary experimental information about the protein material used, details of the wet lab protocol, an archive of sequencing trace files, and assembled clone sequences of binding sequences. The database also offers reasonably large SELEX libraries obtained with conventional low-throughput protocols.The database is available at http://wwwisrec.isb-sib.ch/htpselex/ and and ftp://ftp.isrec.isb-sib.ch/pub/databases/htpselex. The Expectation-Maximisation(EM) algorithm is one the frequently used methods to estimate probabilistic models to represent the sequence specificity of transcription factors. We present computer simulations in order to estimate the precision of EM estimated models as a function of data set parameters(like length of initial sequences, number of initial sequences, percentage of nonbinding sequences). We observed a remarkable robustness of the EM algorithm with regard to length of training sequences and the degree of contamination. The HTPSELEX database and the benchmarked results of the EM algorithm formed part of the foundation for the subsequent project, where a statistical framework called hidden Markov model has been developed to represent sequence specificity of the transcription factors CTF/NF1 and LEF1/TCF using the HTP-SELEX experiment data. The hidden Markov model framework is capable of both predicting and classifying CTF/NF1 and LEF1/TCF binding sites. A covariance analysis of the binding sites revealed non-independent base preferences at different nucleotide positions, providing insight into the binding mechanism. We next tested the LEF1/TCF model by computing binding scores for a set of LEF1/TCF binding sequences for which relative affinities were determined experimentally using non-linear regression. The predicted and experimentally determined binding affinities were in good correlation.

Modélisation par automate cellulaire des phénomènes diagénétiques des plateformes carbonatées : calibration et paramétrage à partir de deux cas d'études : l'Urgonien du Vercors (Crétacé inférieur, SE France) et les Calcaires Gris du Mont Compomolon (Lias, NE Italie)

Une fois déposé, un sédiment est affecté au cours de son enfouissement par un ensemble de processus, regroupé sous le terme diagenèse, le transformant parfois légèrement ou bien suffisamment pour le rendre méconnaissable. Ces modifications ont des conséquences sur les propriétés pétrophysiques qui peuvent être positives ou négatives, c'est-à-dire les améliorer ou bien les détériorer. Une voie alternative de représentation numérique des processus, affranchie de l'utilisation des réactions physico-chimiques, a été adoptée et développée en mimant le déplacement du ou des fluides diagénétiques. Cette méthode s'appuie sur le principe d'un automate cellulaire et permet de simplifier les phénomènes sans sacrifier le résultat et permet de représenter les phénomènes diagénétiques à une échelle fine. Les paramètres sont essentiellement numériques ou mathématiques et nécessitent d'être mieux compris et renseignés à partir de données réelles issues d'études d'affleurements et du travail analytique effectué. La représentation des phénomènes de dolomitisation de faible profondeur suivie d'une phase de dédolomitisation a été dans un premier temps effectuée. Le secteur concerne une portion de la série carbonatée de l'Urgonien (Barrémien-Aptien), localisée dans le massif du Vercors en France. Ce travail a été réalisé à l'échelle de la section afin de reproduire les géométries complexes associées aux phénomènes diagénétiques et de respecter les proportions mesurées en dolomite. De plus, la dolomitisation a été simulée selon trois modèles d'écoulement. En effet, la dédolomitisation étant omniprésente, plusieurs hypothèses sur le mécanisme de dolomitisation ont été énoncées et testées. Plusieurs phases de dolomitisation per ascensum ont été également simulées sur des séries du Lias appartenant aux formations du groupe des Calcaire Gris, localisées au nord-est de l'Italie. Ces fluides diagénétiques empruntent le réseau de fracturation comme vecteur et affectent préférentiellement les lithologies les plus micritisées. Cette étude a permis de mettre en évidence la propagation des phénomènes à l'échelle de l'affleurement. - Once deposited, sediment is affected by diagenetic processes during their burial history. These diagenetic processes are able to affect the petrophysical properties of the sedimentary rocks and also improve as such their reservoir capacity. The modelling of diagenetic processes in carbonate reservoirs is still a challenge as far as neither stochastic nor physicochemical simulations can correctly reproduce the complexity of features and the reservoir heterogeneity generated by these processes. An alternative way to reach this objective deals with process-like methods, which simplify the algorithms while preserving all geological concepts in the modelling process. The aim of the methodology is to conceive a consistent and realistic 3D model of diagenetic overprints on initial facies resulting in petrophysical properties at a reservoir scale. The principle of the method used here is related to a lattice gas automata used to mimic diagenetic fluid flows and to reproduce the diagenetic effects through the evolution of mineralogical composition and petrophysical properties. This method developed in a research group is well adapted to handle dolomite reservoirs through the propagation of dolomitising fluids and has been applied on two case studies. The first study concerns a mid-Cretaceous rudist and granular platform of carbonate succession (Urgonian Fm., Les Gorges du Nan, Vercors, SE France), in which several main diagenetic stages have been identified. The modelling in 2D is focused on dolomitisation followed by a dédolomitisation stage. For the second study, data collected from outcrops on the Venetian platform (Lias, Mont Compomolon NE Italy), in which several diagenetic stages have been identified. The main one is related to per ascensum dolomitisation along fractures. In both examples, the evolution of the effects of the mimetic diagenetic fluid on mineralogical composition can be followed through space and numerical time and help to understand the heterogeneity in reservoir properties. Carbonates, dolomitisation, dédolomitisation, process-like modelling, lattice gas automata, random walk, memory effect.

Interacting populations in heterogeneous environments

To optimally manage a metapopulation, managers and conservation biologists can favor a type of habitat spatial distribution (e.g. aggregated or random). However, the spatial distribution that provides the highest habitat occupancy remains ambiguous and numerous contradictory results exist. Habitat occupancy depends on the balance between local extinction and colonization. Thus, the issue becomes even more puzzling when various forms of relationships - positive or negative co-variation - between local extinction and colonization rate within habitat types exist. Using an analytical model we demonstrate first that the habitat occupancy of a metapopulation is significantly affected by the presence of habitat types that display different extinction-colonization dynamics, considering: (i) variation in extinction or colonization rate and (ii) positive and negative co-variation between the two processes within habitat types. We consequently examine, with a spatially explicit stochastic simulation model, how different degrees of habitat aggregation affect occupancy predictions under similar scenarios. An aggregated distribution of habitat types provides the highest habitat occupancy when local extinction risk is spatially heterogeneous and high in some places, while a random distribution of habitat provides the highest habitat occupancy when colonization rates are high. Because spatial variability in local extinction rates always favors aggregation of habitats, we only need to know about spatial variability in colonization rates to determine whether aggregating habitat types increases, or not, metapopulation occupancy. From a comparison of the results obtained with the analytical and with the spatial-explicit stochastic simulation model we determine the conditions under which a simple metapopulation model closely matches the results of a more complex spatial simulation model with explicit heterogeneity.

Use of anisotropic modelling in electrical impedance tomography: description of method and preliminary assessment of utility in imaging brain function in the adult human head.

Electrical Impedance Tomography (EIT) is an imaging method which enables a volume conductivity map of a subject to be produced from multiple impedance measurements. It has the potential to become a portable non-invasive imaging technique of particular use in imaging brain function. Accurate numerical forward models may be used to improve image reconstruction but, until now, have employed an assumption of isotropic tissue conductivity. This may be expected to introduce inaccuracy, as body tissues, especially those such as white matter and the skull in head imaging, are highly anisotropic. The purpose of this study was, for the first time, to develop a method for incorporating anisotropy in a forward numerical model for EIT of the head and assess the resulting improvement in image quality in the case of linear reconstruction of one example of the human head. A realistic Finite Element Model (FEM) of an adult human head with segments for the scalp, skull, CSF, and brain was produced from a structural MRI. Anisotropy of the brain was estimated from a diffusion tensor-MRI of the same subject and anisotropy of the skull was approximated from the structural information. A method for incorporation of anisotropy in the forward model and its use in image reconstruction was produced. The improvement in reconstructed image quality was assessed in computer simulation by producing forward data, and then linear reconstruction using a sensitivity matrix approach. The mean boundary data difference between anisotropic and isotropic forward models for a reference conductivity was 50%. Use of the correct anisotropic FEM in image reconstruction, as opposed to an isotropic one, corrected an error of 24 mm in imaging a 10% conductivity decrease located in the hippocampus, improved localisation for conductivity changes deep in the brain and due to epilepsy by 4-17 mm, and, overall, led to a substantial improvement on image quality. This suggests that incorporation of anisotropy in numerical models used for image reconstruction is likely to improve EIT image quality.

Modelling tourist itineraries in heritage cities: Routes around Old The District of Girona

Tourists do not follow random behavior in heritage cities, but they are consciously or unconsciously guided by socially constructed itineraries. This article studies the shaping of these itineraries in a heritage city (Girona), using the direct observation methodology during the visit (following the tourists from a prudent distance and gathering all the information about their visits) and the conventional questionnaire at the end of the visit. It also establishes which the sociodemographic, environmental and informative factors are that explain this behavior. The simultaneous use of the observation method and a questionnaire was found to be a useful technique for analyzing tourists' behavior and the factors that explain this behavior