<p>As the most used dosage form in the world, tablets are widely used for the mass production of drugs. The disintegration and dissolution kinetics of tablets play a vital role in the pharmacokinetics and pharmacodynamics of drugs. It is also critical for evaluating the quality of drug formulations. This thesis reports a modeling and simulation approach of tablet disintegration and dissolution processes in a dissolution test device. By coupling the lattice Boltzmann method with the discrete element method, we simulate the hydrodynamics as well as the particle dynamics in the dissolution test device. Our computational methods could model the tablet structure,</p><p>disintegration of the tablet in the dissolution device, and dissolution of particles under the influence of hydrodynamics. The simulation results show that our computational methods can reproduce experimental results. Our methods pave the path toward an in-silico platform for tablet formulation design and verification.</p>

Current grid computing architectures have been based on cluster management and batch queuing systems, extended to a distributed, federated domain. These have shown shortcomings in terms of scalability, stability, and modularity. To address these problems, this dissertation applies architectural styles from the Internet and Web to the domain of generic computational grids. Using the REST style, a flexible model for grid resource interaction is developed which removes the need for any centralised services or specific protocols, thereby allowing a range of implementations and layering of further functionality. The context for resource interaction is a generalisation and formalisation of the Condor ClassAd match-making mechanism. This set theoretic model is described in depth, including the advantages and features which it realises. This RESTful style is also motivated by operational experience with existing grid infrastructures, and the design, operation, and performance of a proto-RESTful grid middleware package named DIRAC. This package was designed to provide for the LHCb particle physics experiment’s “off-line” computational infrastructure, and was first exercised during a 6 month data challenge which utilised over 670 years of CPU time and produced 98 TB of data through 300,000 tasks executed at computing centres around the world. The design of DIRAC and performance measures from the data challenge are reported. The main contribution of this work is the development of a REST model for grid resource interaction. In particular, it allows resource templating for scheduling queues which provide a novel distributed and scalable approach to resource scheduling on the grid.

In this thesis, the plausibility of developing a portable light-weight artillery computer has been investigated. The main goal of the project has been to replace the traditional methods that the Swedish Armed Forces are using today to find firing solutions for their mortar, the GRK m/84. A computational core has been written in Java that simulates the trajectory of a shell using the model in NATO's STANAG 4355. The developed system finds firing solutions by using shooting methods and the multi-dimensional Newton Raphson's method. A Graphical User Interface (GUI) tailored to mobile computers has been designed in Android. The computational core along with the GUI has been installed on a rugged hand held computer and the whole unit has been tested at Markstridsskolan (MSS). The tests showed that the computational core delivers firing solutions that coincide very well with the actual firing solutions needed to hit the desired target.

Scientific Computation provides a critical role in the scientific process because it allows us ask complex queries and test predictions that would otherwise be unfeasible to perform experimentally. Because of its power, Scientific Computing has helped drive advances in many fields ranging from Engineering and Physics to Biology and Sociology to Economics and Drug Development and even to Machine Learning and Artificial Intelligence. Common among these domains is the desire for timely computational results, thus a considerable amount of human expert effort is spent towards obtaining performance for these scientific codes. However, this is no easy task because each of these domains present their own unique set of challenges to software developers, such as domain specific operations, structurally complex data and ever-growing datasets. Compounding these problems are the myriads of constantly changing, complex and unique hardware platforms that an expert must target. Unfortunately, an expert is typically forced to reproduce their effort across multiple problem domains and hardware platforms. In this thesis, we demonstrate the automatic generation of expert level high-performance scientific codes for Dense Linear Algebra (DLA), Structured Mesh (Stencil), Sparse Linear Algebra and Graph Analytic. In particular, this thesis seeks to address the issue of obtaining performance on many complex platforms for a certain class of matrix-like operations that span across many scientific, engineering and social fields. We do this by automating a method used for obtaining high performance in DLA and extending it to structured, sparse and scale-free domains. We argue that it is through the use of the underlying structure found in the data from these domains that enables this process. Thus, obtaining performance for most operations does not occur in isolation of the data being operated on, but instead depends significantly on the structure of the data.

Übungen zur Vorlesung Theoretische Physik III: Elektrodynamik/Computergestützte Elektrodynamik im Wintersemester 2002/03 für den Studiengang Physik und den Bakkalaureusstudiengang Computational Science

Übungen zur Vorlesung Theoretische Physik III: Elektrodynamik/Computergestützte Elektrodynamik im Wintersemester 2003/04 für den Studiengang Physik und den Bakkalaureusstudiengang Computational Science

Übungen zur Vorlesung Theoretische Physik III: Elektrodynamik/Computergestützte Elektrodynamik im Wintersemester 2004/05 für den Studiengang Physik und den Bakkalaureusstudiengang Computational Science

La science moderne des graphes est apparue ces dernières années comme un domaine d'intérêt et a apporté des progrès significatifs à notre connaissance des réseaux. Jusqu'à récemment, les algorithmes d'exploration de données existants étaient destinés à des données structurées / relationnelles, alors que de nombreux ensembles de données nécessitent une représentation graphique, comme les réseaux sociaux, les réseaux générés par des données textuelles, les structures protéiques 3D ou encore les composés chimiques. Il est donc crucial de pouvoir extraire des informations pertinantes à partir de ce type de données et, pour ce faire, les méthodes d'extraction et d'analyse des graphiques ont été prouvées essentielles.L'objectif de cette thèse est d'étudier les problèmes dans le domaine de la fouille de graphes axés en particulier sur la conception de nouveaux algorithmes et d'outils liés à la diffusion d'informations et plus spécifiquement sur la façon de localiser des entités influentes dans des réseaux réels. Cette tâche est cruciale dans de nombreuses applications telles que la diffusion de l'information, les contrôles épidémiologiques et le marketing viral.Dans la première partie de la thèse, nous avons étudié les processus de diffusion dans les réseaux sociaux ciblant la recherche de caractéristiques topologiques classant les entités du réseau en fonction de leurs capacités influentes. Nous nous sommes spécifiquement concentrés sur la décomposition K-truss qui est une extension de la décomposition k-core. On a montré que les noeuds qui appartiennent au sous-graphe induit par le maximal K-truss présenteront de meilleurs proprietés de propagation par rapport aux critères de référence. De tels épandeurs ont la capacité non seulement d'influencer une plus grande partie du réseau au cours des premières étapes d'un processus d'étalement, mais aussi de contaminer une plus grande partie des noeuds.Dans la deuxième partie de la thèse, nous nous sommes concentrés sur l'identification d'un groupe de noeuds qui, en agissant ensemble, maximisent le nombre attendu de nœuds influencés à la fin du processus de propagation, formellement appelé Influence Maximization (IM). Le problème IM étant NP-hard, il existe des algorithmes efficaces garantissant l’approximation de ses solutions. Comme ces garanties proposent une approximation gloutonne qui est coûteuse en termes de temps de calcul, nous avons proposé l'algorithme MATI qui réussit à localiser le groupe d'utilisateurs qui maximise l'influence, tout en étant évolutif. L'algorithme profite des chemins possibles créés dans le voisinage de chaque nœud et précalcule l'influence potentielle de chaque nœud permettant ainsi de produire des résultats concurrentiels, comparés à ceux des algorithmes classiques.Finallement, nous étudions le point de vue de la confidentialité quant au partage de ces bons indicateurs d’influence dans un réseau social. Nous nous sommes concentrés sur la conception d'un algorithme efficace, correct, sécurisé et de protection de la vie privée, qui résout le problème du calcul de la métrique k-core qui mesure l'influence de chaque noeud du réseau. Nous avons spécifiquement adopté une approche de décentralisation dans laquelle le réseau social est considéré comme un système Peer-to-peer (P2P). L'algorithme est construit de telle sorte qu'il ne devrait pas être possible pour un nœud de reconstituer partiellement ou entièrement le graphe en utilisant les informations obtiennues lors de son exécution. Notre contribution est un algorithme incrémental qui résout efficacement le problème de maintenance de core en P2P tout en limitant le nombre de messages échangés et les calculs. Nous fournissons également une étude de sécurité et de confidentialité de la solution concernant la désanonymisation des réseaux, nous montrons ainsi la rélation avec les strategies d’attaque précédemment definies tout en discutant les contres-mesures adaptés. / Modern science of graphs has emerged the last few years as a field of interest and has been bringing significant advances to our knowledge about networks. Until recently the existing data mining algorithms were destined for structured/relational data while many datasets exist that require graph representation such as social networks, networks generated by textual data, 3D protein structures and chemical compounds. It has become therefore of crucial importance to be able to extract meaningful information from that kind of data and towards this end graph mining and analysis methods have been proven essential. The goal of this thesis is to study problems in the area of graph mining focusing especially on designing new algorithms and tools related to information spreading and specifically on how to locate influential entities in real-world networks. This task is crucial in many applications such as information diffusion, epidemic control and viral marketing. In the first part of the thesis, we have studied spreading processes in social networks focusing on finding topological characteristics that rank entities in the network based on their influential capabilities. We have specifically focused on the K-truss decomposition which is an extension of the core decomposition of the graph. Extensive experimental analysis showed that the nodes that belong to the maximal K-truss subgraph show a better spreading behavior when compared to baseline criteria. Such spreaders can influence a greater part of the network during the first steps of a spreading process but also the total fraction of the influenced nodes at the end of the epidemic is greater. We have also observed that node members of such dense subgraphs are those achieving the optimal spreading in the network.In the second part of the thesis, we focused on identifying a group of nodes that by acting all together maximize the expected number of influenced nodes at the end of the spreading process, formally called Influence Maximization (IM). The IM problem is actually NP-hard though there exist approximation guarantees for efficient algorithms that can solve the problem while obtaining a solution within the 63% of optimal classes of models. As those guarantees propose a greedy approximation which is computationally expensive especially for large graphs, we proposed the MATI algorithm which succeeds in locating the group of users that maximize the influence while also being scalable. The algorithm takes advantage the possible paths created in each node’s neighborhood to precalculate each node’s potential influence and produces competitive results in quality compared to those of baseline algorithms such as the Greedy, LDAG and SimPath. In the last part of the thesis, we study the privacy point of view of sharing such metrics that are good influential indicators in a social network. We have focused on designing an algorithm that addresses the problem of computing through an efficient, correct, secure, and privacy-preserving algorithm the k-core metric which measures the influence of each node of the network. We have specifically adopted a decentralization approach where the social network is considered as a Peer-to-peer (P2P) system. The algorithm is built based on the constraint that it should not be possible for a node to reconstruct partially or entirely the graph using the information they obtain during its execution. While a distributed algorithm that computes the nodes’ coreness is already proposed, dynamic networks are not taken into account. Our main contribution is an incremental algorithm that efficiently solves the core maintenance problem in P2P while limiting the number of messages exchanged and computations. We provide a security and privacy analysis of the solution regarding network de-anonimization and show how it relates to previously defined attacks models and discuss countermeasures.

The problem of efficient parallelization of 2D Ising spin systems requires realistic algorithmic design and implementation based on an understanding of issues from computer science and statistical physics. In this work, we not only consider fundamental parallel computing issues but also ensure that the major constraints and criteria of 2D Ising spin systems are incorporated into our study. This realism in both parallel computation and statistical physics has rarely been reflected in previous research for this problem. In this thesis,we designed and implemented a variety of parallel algorithms for both sweep spin selection and random spin selection. We analyzed our parallel algorithms on a portable and general parallel machine model, namely the LogP model. We were able to obtain rigorous theoretical run-times on LogP for all the parallel algorithms. Moreover, a guiding equation was derived for choosing data layouts (blocked vs. stripped) for sweep spin selection. In regards to random spin selection, we were able to develop parallel algorithms with efficient communication schemes. We analyzed randomness of our schemes using statistical methods and provided comparisons between the different schemes. Furthermore, algorithms were implemented and performance data gathered and analyzed in order to determine further design issues and validate theoretical analysis. / Master of Science

Le givrage a été identifié comme un danger important dès le début de l'aéronautique.L'accrétion de givre sur les ailes d'avion, due à la présence de gouttelettes surfonduesdans les nuages, cause parmi d'autres conséquences néfastes une dégradation des performancesaérodynamiques pouvant conduire au décrochage. C'est pourquoi les avionneursdéveloppent depuis longtemps des systèmes de protection. Comme les essais en vols ou ensoufflerie sont souvent complexes à mettre en oeuvre et onéreux, la simulation numériqueest devenue un outil efficace et complémentaire pour dimensionner ces systèmes.Cette thèse s'inscrit dans le contexte de la modélisation de l'accrétion de givre, duruissellement et des systèmes de protection thermique. Elle s'articule en sept chapitres.Après avoir présenté les enjeux et contexte, on introduit une approche tricouche permettantde modéliser l'accrétion de givre et le ruissellement de manière instationnaire. Les troischapitres suivants traitent des méthodes de discrétisation de ce modèle ainsi que de soncouplage avec un modèle du système de protection thermique. Les deux derniers sontconsacrés à la présentation des résultats de simulations numériques montrant l'intérêt del'approche développée et la faisabilité de simulations complètes de phénomènes d'accrétionde givre sur une paroi chauffée ou non. / Icing has been identified as a serious issue since the start of aeronautics. Ice accretion onwings, due to supercooled droplets inside clouds, leads to severe degradation of aerodynamicperformances, among other undesirable effects. Therefore, aircraft manufacturers have sincea long time developed ice protection systems. As flight tests or wind tunnel experimentsare often complicated to implement and expensive, numerical modeling is an effective andcomplementary tool to design those systems. This thesis concerns the modeling of ice accretion, runback and thermal ice protectionsystems. It consists of seven chapters. The first one is dedicated to the presentation of theconcerns and the context. Then a three layer approach allowing to model in an unsteadyway ice accretion and runback is presented. Following three chapters deal with this modeldiscretization as well as a method to couple it with a thermal ice protection system model.Two last chapters are dedicated to numerical simulations showing the sake of the approachand the feasibility of a whole simulation of ice accretion on a heated or unheated surface.