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Dans le cadre de la protection du bâti face au risque sismique, les techniques d’analyse simplifiées, basées sur des calculs quasi-statiques en poussée progressive, se sont fortement développées au cours des deux dernières décennies. Le travail de thèse a pour objectif d’optimiser une stratégie d’analyse simplifiée proposée par Chopra et al. (2001) et adoptée par les normes américaines FEMA 273. Il s’agit d’une analyse modale non linéaire découplée, dénommée par les auteurs UMRHA qui se caractérisent principalement par : des calculs de type pushover selon les modes de vibration dominants de la structure, la création de modèles à un degré de liberté non linéaire à partir des courbes de pushover, puis le calcul de la réponse temporelle de la structure en recombinant les réponses temporelles associées à chaque mode de vibration. Dans ce travail, la méthode UMRHA a été améliorée en investiguant les points suivants. Tout d’abord, plusieurs modèles à un degré de liberté non linéaire déduits des courbes de pushover modal sont proposés afin d’enrichir la méthode UMRHA originelle qui emploie un simple modèle élasto-plastique : autres modèles élasto-plastiques avec des courbes enveloppes différentes, le modèle de Takeda prenant en compte un comportement hystérétique propre aux structures sous séismes, et enfin, un modèle simplifié basé sur la dégradation de fréquence en fonction d’un indicateur de dommage. Ce dernier modèle à un degré de liberté privilégie la vision de la chute de fréquence au cours du processus d’endommagement de la structure par rapport à une description réaliste des boucles d’hystérésis. La réponse totale de la structure est obtenue en sommant les contributions non linéaires des modes dominants aux contributions linéaires des modes non dominants. Enfin, la dégradation des déformées modales, due à l’endommagement subi par la structure au cours de la sollicitation sismique, est prise en compte dans la méthode M-UMRHA proposée dans ce travail, en généralisant le concept précédent de dégradation des fréquences modales en fonction d’un indicateur de dommage : la déformée modale devient elle-aussi dépendante d’un indicateur de dommage, le déplacement maximum en tête de l’ouvrage ; l’évolution de la déformée modale en fonction de cet indicateur est directement identifiée à partir des calculs de pushover modal. La pertinence de la nouvelle méthode M-UMRHA est investiguée pour plusieurs types de structures, en adoptant des modélisations éprouvées dans le cadre de la simulation des structures sous séismes : portique en béton armé modélisé par des éléments multifibres pour le béton et les armatures, remplissage en maçonnerie avec des éléments barres diagonales résistant uniquement en compression, bâti existant contreventé (Hôtel de Ville de Grenoble) avec des approches coques multicouches. Les résultats obtenus par la méthode simplifiée proposée sont comparés aux résultats de référence issus de l'analyse temporelle non linéaire dynamique. / In the context of building’s protection against seismic risk, simplified analysis techniques, based on quasi-static analysis of pushover, have strongly developed over the past two decades. The thesis aims to optimize a simplified method proposed by Chopra and Goel in 2001 and adopted by American standards FEMA 273. This method is a nonlinear decoupled modal analysis, called by the authors UMRHA (Uncoupled Modal for Response History Analysis) which is mainly characterized by: pushover modal analysis according to the dominant modes of vibration of the structure, setting up nonlinear single degree of freedom systems drawn from modal pushover curves, then determining the history response of the structure by combining of the temporal responses associated with each mode of vibration. The decoupling of nonlinear history responses associated with each mode is the strong assumption of the method UMRHA. In this study, the UMRHA method has been improved by investigating the following points. First of all, several nonlinear single degree of freedom systems drawn from modal pushover curves are proposed to enrich the original UMRHA method, in which a simple elastic-plastic model is used, other elastic-plastic models with different envelope curves, Takeda model taking into account an hysteretic behavior characteristic of structures under earthquakes, and finally, a simplified model based on the frequency degradation as a function of a damage index. The latter nonlinear single degree of freedom model privileges the view of the frequency degradation during the structure damage process relative to a realistic description of hysteresis loops. The total response of the structure is obtained by summing the contributions of the non linear dominant modes to those of linear non dominant modes. Finally, the degradation of the modal shapes due to the structure damage during the seismic loading is taken into account in the new simplified method M-UMRHA (Modified UMRHA) proposed in this study. By generalizing the previous model of frequency degradation as a function of a damage index: the modal shape becomes itself also dependent on a damage index, the maximum displacement at the top of the structure; the evolution of the modal shape as a function of this index is directly obtained from the modal pushover analysis. The pertinence of the new method M-UMRHA is investigated for several types of structures, by adopting tested models of structures simulation under earthquakes: reinforced concrete frame modeled by multifibre elements with uniaxial laws under cyclic loading for concrete and steel, infill masonry wall with diagonal bars elements resistant only in compression, existing building (Grenoble City Hall) with multilayer shell elements and nonlinear biaxial laws based on the concept of smeared and fixed cracks. The obtained results by the proposed simplified method are compared to the reference results derived from the nonlinear response history analysis.

Das Leibniz-Institut für ökologische Raumentwicklung (IÖR) verfolgt u. a. das Ziel, präzise Kenntnisse über das Mengengerüst des deutschen Gebäudebestandes und seiner Eigenschaften zu gewinnen und räumlich hochauflösende Indikatoren als Grundlage einer nachhaltigen Raumentwicklung für Planer und Entscheidungsträger zu erarbeiten. Dieser Beitrag fokussiert auf Ansätze der räumlichen Analyse, die eine Quantifizierung und Charakterisierung des Gesamtbestandes von Wohn- und Nichtwohngebäuden unterstützen. Vorgestellt werden erste Ergebnisse einer deutschlandweiten Auswertung amtlicher Hauskoordinaten und Hausumringe. Der Gebäudebestand wird nach Bundesländern und nach Raumstrukturtypen des Bundesinstituts für Bau-, Stadt- und Raumforschung (BBSR) gegliedert. Es besteht Bedarf, nicht nur Datenmodelle zu entwickeln, sondern daraus auch Erklärungs- und Messmodelle abzuleiten, die einen expliziten Raumbezug aufweisen und sich zur bestandsorientierten Wissensgewinnung sowie zur Strategieentwicklung eignen – auch im europäischen Kontext.

<p>Protein malfunction can cause human diseases, which makes the protein a target in the process of drug discovery. In-depth knowledge of how protein functions can widely contribute to the understanding of the mechanism of these diseases. Protein functions are determined by protein structures and their dynamic properties. Protein dynamics refers to the constant physical movement of atoms in a protein, which may result in the transition between different conformational states of the protein. These conformational transitions are critically important for the proteins to function. Understanding protein dynamics can help to understand and interfere with the conformational states and transitions, and thus with the function of the protein. If we can understand the mechanism of conformational transition of protein, we can design molecules to regulate this process and regulate the protein functions for new drug discovery. Protein Dynamics can be simulated by Molecular Dynamics (MD) Simulations.</p> <p>The MD simulation data generated are spatial-temporal and therefore very high dimensional. To analyze the data, distinguishing various atomic interactions within a protein by interpreting their 3D coordinate values plays a significant role. Since the data is humongous, the essential step is to find ways to interpret the data by generating more efficient algorithms to reduce the dimensionality and developing user-friendly visualization tools to find patterns and trends, which are not usually attainable by traditional methods of data process. The typical allosteric long-range nature of the interactions that lead to large conformational transition, pin-pointing the underlying forces and pathways responsible for the global conformational transition at atomic level is very challenging. To address the problems, Various analytical techniques are performed on the simulation data to better understand the mechanism of protein dynamics at atomic level by developing a new program called Probing Long-distance interactions by Tapping into Paired-Distances (PLITIP), which contains a set of new tools based on analysis of paired distances to remove the interference of the translation and rotation of the protein itself and therefore can capture the absolute changes within the protein.</p> <p>Firstly, we developed a tool called Decomposition of Paired Distances (DPD). This tool generates a distance matrix of all paired residues from our simulation data. This paired distance matrix therefore is not subjected to the interference of the translation or rotation of the protein and can capture the absolute changes within the protein. This matrix is then decomposed by DPD</p> <p>using Principal Component Analysis (PCA) to reduce dimensionality and to capture the largest structural variation. To showcase how DPD works, two protein systems, HIV-1 protease and 14-3-3 σ, that both have tremendous structural changes and conformational transitions as displayed by their MD simulation trajectories. The largest structural variation and conformational transition were captured by the first principal component in both cases. In addition, structural clustering and ranking of representative frames by their PC1 values revealed the long-distance nature of the conformational transition and locked the key candidate regions that might be responsible for the large conformational transitions.</p> <p>Secondly, to facilitate further analysis of identification of the long-distance path, a tool called Pearson Coefficient Spiral (PCP) that generates and visualizes Pearson Coefficient to measure the linear correlation between any two sets of residue pairs is developed. PCP allows users to fix one residue pair and examine the correlation of its change with other residue pairs.</p> <p>Thirdly, a set of visualization tools that generate paired atomic distances for the shortlisted candidate residue and captured significant interactions among them were developed. The first tool is the Residue Interaction Network Graph for Paired Atomic Distances (NG-PAD), which not only generates paired atomic distances for the shortlisted candidate residues, but also display significant interactions by a Network Graph for convenient visualization. Second, the Chord Diagram for Interaction Mapping (CD-IP) was developed to map the interactions to protein secondary structural elements and to further narrow down important interactions. Third, a Distance Plotting for Direct Comparison (DP-DC), which plots any two paired distances at user’s choice, either at residue or atomic level, to facilitate identification of similar or opposite pattern change of distances along the simulation time. All the above tools of PLITIP enabled us to identify critical residues contributing to the large conformational transitions in both HIV-1 protease and 14-3-3σ proteins.</p> <p>Beside the above major project, a side project of developing tools to study protein pseudo-symmetry is also reported. It has been proposed that symmetry provides protein stability, opportunities for allosteric regulation, and even functionality. This tool helps us to answer the questions of why there is a deviation from perfect symmetry in protein and how to quantify it.</p>

In der akademischen Fachliteratur existieren kaum Forschungsergebnisse zu One-to-One-Marketing, die auf Anwendungen im Einzelhandel ausgerichtet sind. Zu den Hauptgründen zählen, dass Ansätze nicht auf die Größe typischer Einzelhandelsanwendungen skalieren und dass die Datenverfügbarkeit auf Händler und Marketing-Systemanbieter beschränkt ist. Die vorliegende Dissertation entwickelt neue deskriptive, prädiktive und präskriptive Modelle für automatisiertes Target Marketing, die auf Representation Learning und Deep Learning basieren, und untersucht deren Wirksamkeit in Praxisanwendungen. Im ersten Schritt zeigt die Arbeit, dass Representation Learning in der Lage ist, skalierbar Marktstrukturen zu analysieren. Der vorgeschlagene Ansatz zur Visualisierung von Marktstrukturen ist vollständig automatisiert und existierenden Methoden überlegen. Die Arbeit entwickelt anschließend ein skalierbares, nichtparametrisches Modell, das Produktwahl auf Konsumentenebene für alle Produkte im Sortiment großer Einzelhändler vorhersagt. Das Deep Neural Network übertrifft die Vorhersagekraft existierender Benchmarks und auf Basis des Modells abgeleitete Coupons erzielen signifikant höhere Umsatzsteigerungen. Die Dissertation untersucht abschließend eine Coupon-Engine, die auf den entwickelten Modellen basiert. Der Vergleich personalisierter Werbeaktionen mit Massenmarketing belegt, dass One-to-One Marketing Einlösungsraten, Umsätze und Gewinne steigern kann. Eine Analyse der Kundenreaktionen auf personalisierte Coupons im Rahmen eines Kundenbindungsprogrammes zeigt, dass personalisiertes Marketing Systemnutzung erhöht. Dies illustriert, wie Target Marketing und Kundenbindungsprogramme effizient kombiniert werden können. Die vorliegende Dissertation ist somit sowohl für Forscher als auch für Praktiker relevant. Neben leistungsfähigeren Modellansätzen bietet diese Arbeit relevante Implikationen für effizientes Promotion-Management und One-to-One-Marketing im Einzelhandel. / Research on one-to-one marketing with a focus on retailing is scarce in academic literature. The two main reasons are that the target marketing approaches proposed by researchers do not scale to the size of typical retail applications and that data regarding one-to-one marketing remain locked within retailers and marketing solution providers. This dissertation develops new descriptive, predictive, and prescriptive marketing models for automated target marketing that are based on representation learning and deep learning and studies the models’ impact in real-life applications. First, this thesis shows that representation learning is capable of analyzing market structures at scale. The proposed approach to visualizing market structures is fully automated and superior to existing mapping methods that are based on the same input data. The thesis then proposes a scalable, nonparametric model that predicts product choice for the entire assortment of a large retailer. The deep neural network outperforms benchmark methods for predicting customer purchases. Coupon policies based on the proposed model lead to substantially higher revenue lifts than policies based on the benchmark models. The remainder of the thesis studies a real-time offer engine that is based on the proposed models. The comparison of personalized promotions to non-targeted promotions shows that one-to-one marketing increases redemption rates, revenues, and profits. A study of customer responses to personalized price promotions within the retailer’s loyalty program reveals that personalized marketing also increases loyalty program usage. This illustrates how targeted price promotions can be integrated smoothly into loyalty programs. In summary, this thesis is highly relevant for both researchers and practitioners. The new deep learning models facilitate more scalable and efficient one-to-one marketing. In addition, this research offers pertinent implications for promotion management and one-to-one marketing.