BayGO: Bayesian analysis of ontology term enrichment in microarray data

Abstract Background The search for enriched (aka over-represented or enhanced) ontology terms in a list of genes obtained from microarray experiments is becoming a standard procedure for a system-level analysis. This procedure tries to summarize the information focussing on classification designs such as Gene Ontology, KEGG pathways, and so on, instead of focussing on individual genes. Although it is well known in statistics that association and significance are distinct concepts, only the former approach has been used to deal with the ontology term enrichment problem. Results BayGO implements a Bayesian approach to search for enriched terms from microarray data. The R source-code is freely available at http://blasto.iq.usp.br/~tkoide/BayGO in three versions: Linux, which can be easily incorporated into pre-existent pipelines; Windows, to be controlled interactively; and as a web-tool. The software was validated using a bacterial heat shock response dataset, since this stress triggers known system-level responses. Conclusion The Bayesian model accounts for the fact that, eventually, not all the genes from a given category are observable in microarray data due to low intensity signal, quality filters, genes that were not spotted and so on. Moreover, BayGO allows one to measure the statistical association between generic ontology terms and differential expression, instead of working only with the common significance analysis.

Semantic integration of gene expression analysis tools and data sources using software connectors

Abstract Background The study and analysis of gene expression measurements is the primary focus of functional genomics. Once expression data is available, biologists are faced with the task of extracting (new) knowledge associated to the underlying biological phenomenon. Most often, in order to perform this task, biologists execute a number of analysis activities on the available gene expression dataset rather than a single analysis activity. The integration of heteregeneous tools and data sources to create an integrated analysis environment represents a challenging and error-prone task. Semantic integration enables the assignment of unambiguous meanings to data shared among different applications in an integrated environment, allowing the exchange of data in a semantically consistent and meaningful way. This work aims at developing an ontology-based methodology for the semantic integration of gene expression analysis tools and data sources. The proposed methodology relies on software connectors to support not only the access to heterogeneous data sources but also the definition of transformation rules on exchanged data. Results We have studied the different challenges involved in the integration of computer systems and the role software connectors play in this task. We have also studied a number of gene expression technologies, analysis tools and related ontologies in order to devise basic integration scenarios and propose a reference ontology for the gene expression domain. Then, we have defined a number of activities and associated guidelines to prescribe how the development of connectors should be carried out. Finally, we have applied the proposed methodology in the construction of three different integration scenarios involving the use of different tools for the analysis of different types of gene expression data. Conclusions The proposed methodology facilitates the development of connectors capable of semantically integrating different gene expression analysis tools and data sources. The methodology can be used in the development of connectors supporting both simple and nontrivial processing requirements, thus assuring accurate data exchange and information interpretation from exchanged data.

Variance Reduction in Analytical Chemistry : New Numerical Methods in Chemometrics and Molecular Simulation

This thesis is based on five papers addressing variance reduction in different ways. The papers have in common that they all present new numerical methods. Paper I investigates quantitative structure-retention relationships from an image processing perspective, using an artificial neural network to preprocess three-dimensional structural descriptions of the studied steroid molecules. Paper II presents a new method for computing free energies. Free energy is the quantity that determines chemical equilibria and partition coefficients. The proposed method may be used for estimating, e.g., chromatographic retention without performing experiments. Two papers (III and IV) deal with correcting deviations from bilinearity by so-called peak alignment. Bilinearity is a theoretical assumption about the distribution of instrumental data that is often violated by measured data. Deviations from bilinearity lead to increased variance, both in the data and in inferences from the data, unless invariance to the deviations is built into the model, e.g., by the use of the method proposed in paper III and extended in paper IV. Paper V addresses a generic problem in classification; namely, how to measure the goodness of different data representations, so that the best classifier may be constructed. Variance reduction is one of the pillars on which analytical chemistry rests. This thesis considers two aspects on variance reduction: before and after experiments are performed. Before experimenting, theoretical predictions of experimental outcomes may be used to direct which experiments to perform, and how to perform them (papers I and II). After experiments are performed, the variance of inferences from the measured data are affected by the method of data analysis (papers III-V).

Dense disparity map estimation respecting image discontinuities

[EN] We present an energy based approach to estimate a dense disparity map from a set of two weakly calibrated stereoscopic images while preserving its discontinuities resulting from image boundaries. We first derive a simplified expression for the disparity that allows us to estimate it from a stereo pair of images using an energy minimization approach. We assume that the epipolar geometry is known, and we include this information in the energy model. Discontinuities are preserved by means of a regularization term based on the Nagel-Enkelmann operator. We investigate the associated Euler-Lagrange equation of the energy functional, and we approach the solution of the underlying partial differential equation (PDE) using a gradient descent method The resulting parabolic problem has a unique solution. In order to reduce the risk to be trapped within some irrelevant local minima during the iterations, we use a focusing strategy based on a linear scalespace. Experimental results on both synthetic and real images arere presented to illustrate the capabilities of this PDE and scale-space based method.

A variational approach to 3D geometry reconstruction from two or multiple views

[EN] In the last years we have developed some methods for 3D reconstruction. First we began with the problem of reconstructing a 3D scene from a stereoscopic pair of images. We developed some methods based on energy functionals which produce dense disparity maps by preserving discontinuities from image boundaries. Then we passed to the problem of reconstructing a 3D scene from multiple views (more than 2). The method for multiple view reconstruction relies on the method for stereoscopic reconstruction. For every pair of consecutive images we estimate a disparity map and then we apply a robust method that searches for good correspondences through the sequence of images. Recently we have proposed several methods for 3D surface regularization. This is a postprocessing step necessary for smoothing the final surface, which could be afected by noise or mismatch correspondences. These regularization methods are interesting because they use the information from the reconstructing process and not only from the 3D surface. We have tackled all these problems from an energy minimization approach. We investigate the associated Euler-Lagrange equation of the energy functional, and we approach the solution of the underlying partial differential equation (PDE) using a gradient descent method.

TCAD approaches to multidimensional simulation of advanced semiconductor devices

Technology scaling increasingly emphasizes complexity and non-ideality of the electrical behavior of semiconductor devices and boosts interest on alternatives to the conventional planar MOSFET architecture. TCAD simulation tools are fundamental to the analysis and development of new technology generations. However, the increasing device complexity is reflected in an augmented dimensionality of the problems to be solved. The trade-off between accuracy and computational cost of the simulation is especially influenced by domain discretization: mesh generation is therefore one of the most critical steps and automatic approaches are sought. Moreover, the problem size is further increased by process variations, calling for a statistical representation of the single device through an ensemble of microscopically different instances. The aim of this thesis is to present multi-disciplinary approaches to handle this increasing problem dimensionality in a numerical simulation perspective. The topic of mesh generation is tackled by presenting a new Wavelet-based Adaptive Method (WAM) for the automatic refinement of 2D and 3D domain discretizations. Multiresolution techniques and efficient signal processing algorithms are exploited to increase grid resolution in the domain regions where relevant physical phenomena take place. Moreover, the grid is dynamically adapted to follow solution changes produced by bias variations and quality criteria are imposed on the produced meshes. The further dimensionality increase due to variability in extremely scaled devices is considered with reference to two increasingly critical phenomena, namely line-edge roughness (LER) and random dopant fluctuations (RD). The impact of such phenomena on FinFET devices, which represent a promising alternative to planar CMOS technology, is estimated through 2D and 3D TCAD simulations and statistical tools, taking into account matching performance of single devices as well as basic circuit blocks such as SRAMs. Several process options are compared, including resist- and spacer-defined fin patterning as well as different doping profile definitions. Combining statistical simulations with experimental data, potentialities and shortcomings of the FinFET architecture are analyzed and useful design guidelines are provided, which boost feasibility of this technology for mainstream applications in sub-45 nm generation integrated circuits.

The meccano method for isogeometric analysis of planar domains

[EN]The authors have recently introduced the meccano method for tetrahedral mesh generation and volume parameterization of solids. In this paper, we present advances of the method for T-spline modelling and analysis of complex geometries. We consider a planar domain composed by several irregular sub-domains. These sub-regions are defined by their boundaries and can represent different materials. The bivariate T-spline representation of the whole physical domain is constructed from a square. In this procedure, a T-mesh optimization method is crucial. We show results of an elliptic problem by using a quadtree local T-mesh refinement technique…

Aspetti innovativi per la valutazione e la misura della porosità nei materiali dell'edilizia antica e moderna. La geometria frattale della porosità

New concepts on porosity appraisal in ancient and modern construction materials. The role of Fractal Geometry on porosity characterization and transport phenomena. This work studied the potential of Fractal Geometry to the characterization of porous materials. Besides the descriptive aspects of the pore size distribution, the fractal dimensions have led to the development of rational relations for the prediction of permeability coefficients to fluid and heat transfer. The research considered natural materials used in historical buildings (rock and earth) as well as currently employed materials as hydraulic cement and technologically advanced materials such as silicon carbide or YSZ ceramics. The experimental results of porosity derived from the techniques of mercury intrusion and from the image analysis. Data elaboration was carried out according to established procedures of Fractal Geometry. It was found that certain classes of materials are clearly fractal and respond to simple patterns such as Sierpinski and Menger models. In several cases, however, the fractal character is not recognised because the microstructure of the material is based on different phases at different dimensional scales, and in consequence the “fractal dimensions” calculated from porosimetric data do not come within the standard range (less than 3). Using different type and numbers of fractal units is possible, however, to obtain “virtual” microstructures that have the fraction of voids and pore size distribution equivalent with the experimental ones for almost any material. Thus it was possible to take the expressions for the permeability and the thermal conduction which does not require empirical “constants”, these expressions have also provided values that are generally in agreement with the experimental available data. More problematic has been the fractal discussion of the geometry of the rupture of the material subjected to mechanical stress both external and internal applied. The results achieved on these issues are qualitative and prone to future studies. Keywords: Materials, Microstructure, Porosity, Fractal Geometry, Permeability, Thermal conduction, Mechanical strength.

Troposphere Calibration Techniques for Deep Space Probe Tracking

Ground-based Earth troposphere calibration systems play an important role in planetary exploration, especially to carry out radio science experiments aimed at the estimation of planetary gravity fields. In these experiments, the main observable is the spacecraft (S/C) range rate, measured from the Doppler shift of an electromagnetic wave transmitted from ground, received by the spacecraft and coherently retransmitted back to ground. If the solar corona and interplanetary plasma noise is already removed from Doppler data, the Earth troposphere remains one of the main error sources in tracking observables. Current Earth media calibration systems at NASA’s Deep Space Network (DSN) stations are based upon a combination of weather data and multidirectional, dual frequency GPS measurements acquired at each station complex. In order to support Cassini’s cruise radio science experiments, a new generation of media calibration systems were developed, driven by the need to achieve the goal of an end-to-end Allan deviation of the radio link in the order of 3×〖10〗^(-15) at 1000 s integration time. The future ESA’s Bepi Colombo mission to Mercury carries scientific instrumentation for radio science experiments (a Ka-band transponder and a three-axis accelerometer) which, in combination with the S/C telecommunication system (a X/X/Ka transponder) will provide the most advanced tracking system ever flown on an interplanetary probe. Current error budget for MORE (Mercury Orbiter Radioscience Experiment) allows the residual uncalibrated troposphere to contribute with a value of 8×〖10〗^(-15) to the two-way Allan deviation at 1000 s integration time. The current standard ESA/ESTRACK calibration system is based on a combination of surface meteorological measurements and mathematical algorithms, capable to reconstruct the Earth troposphere path delay, leaving an uncalibrated component of about 1-2% of the total delay. In order to satisfy the stringent MORE requirements, the short time-scale variations of the Earth troposphere water vapor content must be calibrated at ESA deep space antennas (DSA) with more precise and stable instruments (microwave radiometers). In parallel to this high performance instruments, ESA ground stations should be upgraded to media calibration systems at least capable to calibrate both troposphere path delay components (dry and wet) at sub-centimetre level, in order to reduce S/C navigation uncertainties. The natural choice is to provide a continuous troposphere calibration by processing GNSS data acquired at each complex by dual frequency receivers already installed for station location purposes. The work presented here outlines the troposphere calibration technique to support both Deep Space probe navigation and radio science experiments. After an introduction to deep space tracking techniques, observables and error sources, in Chapter 2 the troposphere path delay is widely investigated, reporting the estimation techniques and the state of the art of the ESA and NASA troposphere calibrations. Chapter 3 deals with an analysis of the status and the performances of the NASA Advanced Media Calibration (AMC) system referred to the Cassini data analysis. Chapter 4 describes the current release of a developed GNSS software (S/W) to estimate the troposphere calibration to be used for ESA S/C navigation purposes. During the development phase of the S/W a test campaign has been undertaken in order to evaluate the S/W performances. A description of the campaign and the main results are reported in Chapter 5. Chapter 6 presents a preliminary analysis of microwave radiometers to be used to support radio science experiments. The analysis has been carried out considering radiometric measurements of the ESA/ESTEC instruments installed in Cabauw (NL) and compared with the requirements of MORE. Finally, Chapter 7 summarizes the results obtained and defines some key technical aspects to be evaluated and taken into account for the development phase of future instrumentation.

Methodologische Untersuchungen zur Veränderung sahelischer Überschwemmungsgebiete mittels Fernerkundung - Fallbeispiel Nigerbinnendelta (Mali) -

ZUSAMMENFASSUNG Langzeitbeobachtungsstudien zur Landschaftsdynamik inSahelländern stehen generell einem defizitären Angebot anquantitativen Rauminformationen gegenüber. Der in Malivorgefundene lokal- bis regionalräumliche Datenmangelführte zu einer methodologischen Studie, die die Entwicklungvon Verfahren zur multi-temporalen Erfassung und Analyse vonLandschaftsveränderungsdaten beinhaltet. Für den RaumWestafrika existiert in großer Flächenüberdeckunghistorisches Fernerkundungsmaterial in Form hochauflösenderLuftbilder ab den 50er Jahren und erste erdbeobachtendeSatellitendaten von Landsat-MSS ab den 70er Jahren.Multitemporale Langzeitanalysen verlangen zur digitalenReproduzierbarkeit, zur Datenvergleich- undObjekterfaßbarkeit die a priori-Betrachtung derDatenbeschaffenheit und -qualität. Zwei, ohne verfügbare, noch rekonstruierbareBodenkontrolldaten entwickelte Methodenansätze zeigen nichtnur die Möglichkeiten, sondern auch die Grenzen eindeutigerradiometrischer und morphometrischerBildinformationsgewinnung. Innerhalb desÜberschwemmungsgunstraums des Nigerbinnendeltas im ZentrumMalis stellen sich zwei Teilstudien zur Extraktion vonquantitativen Sahelvegetationsdaten den radiometrischen undatmosphärischen Problemen:1. Präprozessierende Homogenisierung von multitemporalenMSS-Archivdaten mit Simulationen zur Wirksamkeitatmosphärischer und sensorbedingter Effekte2. Entwicklung einer Methode zur semi-automatischenErfassung und Quantifizierung der Dynamik derGehölzbedeckungsdichte auf panchromatischenArchiv-Luftbildern Die erste Teilstudie stellt historischeLandsat-MSS-Satellitenbilddaten für multi-temporale Analysender Landschaftsdynamik als unbrauchbar heraus. In derzweiten Teilstudie wird der eigens, mittelsmorphomathematischer Filteroperationen für die automatischeMusterkennung und Quantifizierung von Sahelgehölzobjektenentwickelte Methodenansatz präsentiert. Abschließend wird die Forderung nach kosten- undzeiteffizienten Methodenstandards hinsichtlich ihrerRepräsentativität für die Langzeitbeobachtung desRessourceninventars semi-arider Räume sowie deroperationellen Transferierbarkeit auf Datenmaterial modernerFernerkundungssensoren diskutiert.

Verifikation der Niederschlagsvorhersage für Deutschland von 2001 - 2004

Die Verifikation numerischer Modelle ist für die Verbesserung der Quantitativen Niederschlagsvorhersage (QNV) unverzichtbar. Ziel der vorliegenden Arbeit ist die Entwicklung von neuen Methoden zur Verifikation der Niederschlagsvorhersagen aus dem regionalen Modell der MeteoSchweiz (COSMO-aLMo) und des Globalmodells des Europäischen Zentrums für Mittelfristvorhersage (engl.: ECMWF). Zu diesem Zweck wurde ein neuartiger Beobachtungsdatensatz für Deutschland mit stündlicher Auflösung erzeugt und angewandt. Für die Bewertung der Modellvorhersagen wurde das neue Qualitätsmaß „SAL“ entwickelt. Der neuartige, zeitlich und räumlich hoch-aufgelöste Beobachtungsdatensatz für Deutschland wird mit der während MAP (engl.: Mesoscale Alpine Program) entwickelten Disaggregierungsmethode erstellt. Die Idee dabei ist, die zeitlich hohe Auflösung der Radardaten (stündlich) mit der Genauigkeit der Niederschlagsmenge aus Stationsmessungen (im Rahmen der Messfehler) zu kombinieren. Dieser disaggregierte Datensatz bietet neue Möglichkeiten für die quantitative Verifikation der Niederschlagsvorhersage. Erstmalig wurde eine flächendeckende Analyse des Tagesgangs des Niederschlags durchgeführt. Dabei zeigte sich, dass im Winter kein Tagesgang existiert und dies vom COSMO-aLMo gut wiedergegeben wird. Im Sommer dagegen findet sich sowohl im disaggregierten Datensatz als auch im COSMO-aLMo ein deutlicher Tagesgang, wobei der maximale Niederschlag im COSMO-aLMo zu früh zwischen 11-14 UTC im Vergleich zu 15-20 UTC in den Beobachtungen einsetzt und deutlich um das 1.5-fache überschätzt wird. Ein neues Qualitätsmaß wurde entwickelt, da herkömmliche, gitterpunkt-basierte Fehlermaße nicht mehr der Modellentwicklung Rechnung tragen. SAL besteht aus drei unabhängigen Komponenten und basiert auf der Identifikation von Niederschlagsobjekten (schwellwertabhängig) innerhalb eines Gebietes (z.B. eines Flusseinzugsgebietes). Berechnet werden Unterschiede der Niederschlagsfelder zwischen Modell und Beobachtungen hinsichtlich Struktur (S), Amplitude (A) und Ort (L) im Gebiet. SAL wurde anhand idealisierter und realer Beispiele ausführlich getestet. SAL erkennt und bestätigt bekannte Modelldefizite wie das Tagesgang-Problem oder die Simulation zu vieler relativ schwacher Niederschlagsereignisse. Es bietet zusätzlichen Einblick in die Charakteristiken der Fehler, z.B. ob es sich mehr um Fehler in der Amplitude, der Verschiebung eines Niederschlagsfeldes oder der Struktur (z.B. stratiform oder kleinskalig konvektiv) handelt. Mit SAL wurden Tages- und Stundensummen des COSMO-aLMo und des ECMWF-Modells verifiziert. SAL zeigt im statistischen Sinne speziell für stärkere (und damit für die Gesellschaft relevante Niederschlagsereignisse) eine im Vergleich zu schwachen Niederschlägen gute Qualität der Vorhersagen des COSMO-aLMo. Im Vergleich der beiden Modelle konnte gezeigt werden, dass im Globalmodell flächigere Niederschläge und damit größere Objekte vorhergesagt werden. Das COSMO-aLMo zeigt deutlich realistischere Niederschlagsstrukturen. Diese Tatsache ist aufgrund der Auflösung der Modelle nicht überraschend, konnte allerdings nicht mit herkömmlichen Fehlermaßen gezeigt werden. Die im Rahmen dieser Arbeit entwickelten Methoden sind sehr nützlich für die Verifikation der QNV zeitlich und räumlich hoch-aufgelöster Modelle. Die Verwendung des disaggregierten Datensatzes aus Beobachtungen sowie SAL als Qualitätsmaß liefern neue Einblicke in die QNV und lassen angemessenere Aussagen über die Qualität von Niederschlagsvorhersagen zu. Zukünftige Anwendungsmöglichkeiten für SAL gibt es hinsichtlich der Verifikation der neuen Generation von numerischen Wettervorhersagemodellen, die den Lebenszyklus hochreichender konvektiver Zellen explizit simulieren.

A Semantic-based Framework for Digital Survey of Architectural Heritage

The research aims at developing a framework for semantic-based digital survey of architectural heritage. Rooted in knowledge-based modeling which extracts mathematical constraints of geometry from architectural treatises, as-built information of architecture obtained from image-based modeling is integrated with the ideal model in BIM platform. The knowledge-based modeling transforms the geometry and parametric relation of architectural components from 2D printings to 3D digital models, and create large amount variations based on shape grammar in real time thanks to parametric modeling. It also provides prior knowledge for semantically segmenting unorganized survey data. The emergence of SfM (Structure from Motion) provides access to reconstruct large complex architectural scenes with high flexibility, low cost and full automation, but low reliability of metric accuracy. We solve this problem by combing photogrammetric approaches which consists of camera configuration, image enhancement, and bundle adjustment, etc. Experiments show the accuracy of image-based modeling following our workflow is comparable to that from range-based modeling. We also demonstrate positive results of our optimized approach in digital reconstruction of portico where low-texture-vault and dramatical transition of illumination bring huge difficulties in the workflow without optimization. Once the as-built model is obtained, it is integrated with the ideal model in BIM platform which allows multiple data enrichment. In spite of its promising prospect in AEC industry, BIM is developed with limited consideration of reverse-engineering from survey data. Besides representing the architectural heritage in parallel ways (ideal model and as-built model) and comparing their difference, we concern how to create as-built model in BIM software which is still an open area to be addressed. The research is supposed to be fundamental for research of architectural history, documentation and conservation of architectural heritage, and renovation of existing buildings.

Role of Reactive Oxygen Species in signalling and oxidative stress

Results reported in this Thesis contribute to the comprehension of the complicated world of “redox biology”. ROS regulate signalling pathways both in physiological responses and in pathogenesis and progression of diseases. In cancer cells, the increase in ROS generation from metabolic abnormalities and oncogenic signalling may trigger a redox adaptation response, leading to an up-regulation of antioxidant capacity in order to maintain the ROS level below the toxic threshold. Thus, cancer cells would be more dependent on the antioxidant system and more vulnerable to further oxidative stress induced by exogenous ROS-generating agents or compounds that inhibit the antioxidant system. Results here reported indicate that the development of new drugs targeting specific Nox isoforms, responsible for intracellular ROS generation, or AQP isoforms, involved in the transport of extracellular H2O2 toward intracellular targets, might be an interesting novel anti-leukaemia strategy. Furthermore, also the use of CSD peptide, which simulate the VEGFR-2 segregation into caveolae in the inactive form, might be a strategy to stop the cellular response to VEGF signalling. As above stated, in the understanding of the redox biology, it is also important to identify and distinguish the molecular effectors that maintain normal biological and physiological responses, such as agents that stimulate our adaptation systems and elevate our endogenous antioxidant defences or other protective systems. Data here reported indicate that the nutraceutical compound sulforaphane and the Klotho protein are able to stimulate the HO-1 and Prx-1 expression, as well as the GSH levels, confirming their antioxidant and protective role. Finally, results here reported demonstrated that Stevia extracts are involved in insulin regulated glucose metabolism, suggesting that the use of these compounds goes beyond their sweetening power and may also offer therapeutic benefits hence improving the quality of life.

Advanced data deduplication techniques and their application

Data deduplication describes a class of approaches that reduce the storage capacity needed to store data or the amount of data that has to be transferred over a network. These approaches detect coarse-grained redundancies within a data set, e.g. a file system, and remove them.rnrnOne of the most important applications of data deduplication are backup storage systems where these approaches are able to reduce the storage requirements to a small fraction of the logical backup data size.rnThis thesis introduces multiple new extensions of so-called fingerprinting-based data deduplication. It starts with the presentation of a novel system design, which allows using a cluster of servers to perform exact data deduplication with small chunks in a scalable way.rnrnAfterwards, a combination of compression approaches for an important, but often over- looked, data structure in data deduplication systems, so called block and file recipes, is introduced. Using these compression approaches that exploit unique properties of data deduplication systems, the size of these recipes can be reduced by more than 92% in all investigated data sets. As file recipes can occupy a significant fraction of the overall storage capacity of data deduplication systems, the compression enables significant savings.rnrnA technique to increase the write throughput of data deduplication systems, based on the aforementioned block and file recipes, is introduced next. The novel Block Locality Caching (BLC) uses properties of block and file recipes to overcome the chunk lookup disk bottleneck of data deduplication systems. This chunk lookup disk bottleneck either limits the scalability or the throughput of data deduplication systems. The presented BLC overcomes the disk bottleneck more efficiently than existing approaches. Furthermore, it is shown that it is less prone to aging effects.rnrnFinally, it is investigated if large HPC storage systems inhibit redundancies that can be found by fingerprinting-based data deduplication. Over 3 PB of HPC storage data from different data sets have been analyzed. In most data sets, between 20 and 30% of the data can be classified as redundant. According to these results, future work in HPC storage systems should further investigate how data deduplication can be integrated into future HPC storage systems.rnrnThis thesis presents important novel work in different area of data deduplication re- search.

Imaging and modeling of pore scale processes in porous media using X-ray computed tomography and lattice Boltzmann solver

In der Erdöl– und Gasindustrie sind bildgebende Verfahren und Simulationen auf der Porenskala im Begriff Routineanwendungen zu werden. Ihr weiteres Potential lässt sich im Umweltbereich anwenden, wie z.B. für den Transport und Verbleib von Schadstoffen im Untergrund, die Speicherung von Kohlendioxid und dem natürlichen Abbau von Schadstoffen in Böden. Mit der Röntgen-Computertomografie (XCT) steht ein zerstörungsfreies 3D bildgebendes Verfahren zur Verfügung, das auch häufig für die Untersuchung der internen Struktur geologischer Proben herangezogen wird. Das erste Ziel dieser Dissertation war die Implementierung einer Bildverarbeitungstechnik, die die Strahlenaufhärtung der Röntgen-Computertomografie beseitigt und den Segmentierungsprozess dessen Daten vereinfacht. Das zweite Ziel dieser Arbeit untersuchte die kombinierten Effekte von Porenraumcharakteristika, Porentortuosität, sowie die Strömungssimulation und Transportmodellierung in Porenräumen mit der Gitter-Boltzmann-Methode. In einer zylindrischen geologischen Probe war die Position jeder Phase auf Grundlage der Beobachtung durch das Vorhandensein der Strahlenaufhärtung in den rekonstruierten Bildern, das eine radiale Funktion vom Probenrand zum Zentrum darstellt, extrahierbar und die unterschiedlichen Phasen ließen sich automatisch segmentieren. Weiterhin wurden Strahlungsaufhärtungeffekte von beliebig geformten Objekten durch einen Oberflächenanpassungsalgorithmus korrigiert. Die Methode der „least square support vector machine” (LSSVM) ist durch einen modularen Aufbau charakterisiert und ist sehr gut für die Erkennung und Klassifizierung von Mustern geeignet. Aus diesem Grund wurde die Methode der LSSVM als pixelbasierte Klassifikationsmethode implementiert. Dieser Algorithmus ist in der Lage komplexe geologische Proben korrekt zu klassifizieren, benötigt für den Fall aber längere Rechenzeiten, so dass mehrdimensionale Trainingsdatensätze verwendet werden müssen. Die Dynamik von den unmischbaren Phasen Luft und Wasser wird durch eine Kombination von Porenmorphologie und Gitter Boltzmann Methode für Drainage und Imbibition Prozessen in 3D Datensätzen von Böden, die durch synchrotron-basierte XCT gewonnen wurden, untersucht. Obwohl die Porenmorphologie eine einfache Methode ist Kugeln in den verfügbaren Porenraum einzupassen, kann sie dennoch die komplexe kapillare Hysterese als eine Funktion der Wassersättigung erklären. Eine Hysterese ist für den Kapillardruck und die hydraulische Leitfähigkeit beobachtet worden, welche durch die hauptsächlich verbundenen Porennetzwerke und der verfügbaren Porenraumgrößenverteilung verursacht sind. Die hydraulische Konduktivität ist eine Funktion des Wassersättigungslevels und wird mit einer makroskopischen Berechnung empirischer Modelle verglichen. Die Daten stimmen vor allem für hohe Wassersättigungen gut überein. Um die Gegenwart von Krankheitserregern im Grundwasser und Abwässern vorhersagen zu können, wurde in einem Bodenaggregat der Einfluss von Korngröße, Porengeometrie und Fluidflussgeschwindigkeit z.B. mit dem Mikroorganismus Escherichia coli studiert. Die asymmetrischen und langschweifigen Durchbruchskurven, besonders bei höheren Wassersättigungen, wurden durch dispersiven Transport aufgrund des verbundenen Porennetzwerks und durch die Heterogenität des Strömungsfeldes verursacht. Es wurde beobachtet, dass die biokolloidale Verweilzeit eine Funktion des Druckgradienten als auch der Kolloidgröße ist. Unsere Modellierungsergebnisse stimmen sehr gut mit den bereits veröffentlichten Daten überein.