Supermassive black holes (SMBH) are now thought to exist at the center nearly all massive galaxies. Not only are they thought to be ubiquitous, but it was also discovered nearly two decades ago that the mass of these SMBHs correlate strongly with properties of their host galaxies including bulge stellar velocity dispersion (MBH-sigma) and bulge luminosity (MBH-Lbulge). This correlation was not expected due to the tiny size of the SMBH gravitational sphere of influence compared to the size of the host galaxy, and imply a connection between the two, but this connection is still not well-understood. One step toward understanding this connection is to accurately measure the masses of these black holes. Two of the most common direct SMBH mass measurement techniques are stellar dynamical modeling (SDM), which generally only applies to quiescent galaxies, and reverberation mapping (RM), which can only be applied to active galactic nuclei (AGN) that exhibit broadened emission lines. Due to the unknown geometry of the region that produces these broad lines, the whole RM sample of black hole masses generally needs to be multiplied by a constant called the f-factor to bring it into agreement with the SDM sample on the MBH-sigma relation. It is unknown how well this f-factor, being a population average, applies to individual RM masses. It would therefore be useful to measure an SMBH mass with both methods simultaneously to test whether they produce the same black hole mass. However, because the RM and SDM techniques usually apply to galaxies that are not possible for both, this has only been attempted twice before (NGC 3227 and NGC 4151). The purpose of this dissertation is to apply SDM to the SMBH at the center of NGC 6814 for which there already exists an RM mass. This makes it only the third broad-lined AGN for which an SDM mass has been derived. In order to perform SDM accurately, the distance to the galaxy needs to be well-constrained as the error in the SDM mass scales linearly with distance. Because no adequate distance measurements already exist, the first half of this dissertation is devoted to deriving a Cepheid distance to NGC 6814 from V- and I-band HST WFC3 time series photometry. We measure the distance to NGC 6814 to be 17.54 +1.44/-1.33 Mpc. In the second half, we incorporate that distance measurement into our stellar dynamical modeling on Gemini NIFS+Altair IFU data of NGC 6814's central 1.55''x1.55''. We derive a mass of 1.19 +37.57/-1.17 x108 solar masses, and best fit mass-to-light ratio of 0.948 +0.032/-0.208 in solar units. This mass is nearly an order of magnitude larger than the RM mass but has a 3-sigma range spanning nearly three orders of magnitude. We describe possible reasons for our larger-than-expected mass value, such as the existence of a bar, which would not be well-modeled by an axisymmetric dynamical code. Finally, we describe future steps that may be taken to better constrain the mass, such as creating more models to further explore parameter space.

abstract: With the projected population growth, the need to produce higher agricultural yield to meet projected demand is hindered by water scarcity. Out of many the approaches that could be implemented to meet the water gap, intensification of agriculture through adoption of advanced agricultural irrigation techniques is the focus for this research. Current high water consumption by agricultural sector in Arizona is due to historical dominance in the state economy and established water rights. Efficiency gained in agricultural water use in Arizona has the most potential to reduce the overall water consumption. This research studies the agricultural sector and water management of several counties in Arizona (Maricopa, Pinal, and Yuma). Several research approaches are employed: modeling of agricultural technology adoption using replicator dynamics, interview with water managers and farmers, and Arizona water management law and history review. Using systems thinking, the components of the local farming environment are documented through socio-ecological system/robustness lenses. The replicator dynamics model is employed to evaluate possible conditions in which water efficient agricultural irrigation systems proliferate. The evaluation of conditions that promote the shift towards advanced irrigation technology is conducted through a combination of literature review, interview data, and model analysis. Systematic shift from the currently dominant flood irrigation toward a more water efficient irrigation technologies could be attributed to the followings: the increase in advanced irrigation technology yield efficiency; the reduction of advanced irrigation technology implementation and maintenance cost; the change in growing higher value crop; and the change in growing/harvesting time where there is less competition from other states. Insights learned will further the knowledge useful for this arid state's agricultural policy decision making that will both adhere to the water management goals and meet the projected food production and demand gap. / Dissertation/Thesis / Masters Thesis Sustainability 2014

abstract: Alfalfa is a major feed crop widely cultivated in the United States. It is the fourth largest crop in acreage in the US after corn, soybean, and all types of wheat. As of 2003, about 48% of alfalfa was produced in the western US states where alfalfa ranks first, second, or third in crop acreage. Considering that the western US is historically water-scarce and alfalfa is a water-intensive crop, it creates a concern about exacerbating the current water crisis in the US west. Furthermore, the recent increased export of alfalfa from the western US states to China and the United Arab Emirates has fueled the debate over the virtual water content embedded in the crop. In this study, I analyzed changes of cropland systems under the three basic scenarios, using a stylized model with a combination of dynamical, hydrological, and economic elements. The three scenarios are 1) international demands for alfalfa continue to grow (or at least to stay high), 2) deficit irrigation is widely imposed in the dry region, and 3) long-term droughts persist or intensify reducing precipitation. The results of this study sheds light on how distribution of crop areas responds to climatic, economic, and institutional conditions. First, international markets, albeit small compared to domestic markets, provide economic opportunities to increase alfalfa acreage in the dry region. Second, potential water savings from mid-summer deficit irrigation can be used to expand alfalfa production in the dry region. Third, as water becomes scarce, farmers more quickly switch to crops that make more economic use of the limited water. / Dissertation/Thesis / Masters Thesis Sustainability 2015

MATTOS, C. L. C. Recurrent gaussian processes and robust dynamical modeling. 2017. 189 f. Tese (Doutorado em Engenharia de Teleinformática)–Centro de Tecnologia, Universidade Federal do Ceará, Fortaleza, 2017. / Submitted by Renato Vasconcelos (ppgeti@ufc.br) on 2017-09-09T02:26:38Z No. of bitstreams: 1 2017_tes_clcmattos.pdf: 5961013 bytes, checksum: fc44d8b852e28fa0e1ebe0c87389c0da (MD5) / Rejected by Marlene Sousa (mmarlene@ufc.br), reason: Prezado César; Prezado Pedro: Existe uma orientação para que normalizemos as dissertações e teses da UFC, em suas paginas pré-textuais e lista de referencias, pelas regras da ABNT. Por esse motivo, sugerimos consultar o modelo de template, para ajudá-lo nesta tarefa, disponível em: http://www.biblioteca.ufc.br/educacao-de-usuarios/templates/ Vamos agora as correções sempre de acordo com o template: 1. A partir da folha de aprovação as informações devem ser em língua inglesa. 2. A dedicatória deve ter a distancia até o final da folha observado. 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No. of bitstreams: 1 2017_tes_clcmattos.pdf: 6102699 bytes, checksum: 0a85b8841d77f0685b1153ee8ede0d85 (MD5) Previous issue date: 2017-08-25 / The study of dynamical systems is widespread across several areas of knowledge. Sequential data is generated constantly by different phenomena, most of them we cannot explain by equations derived from known physical laws and structures. In such context, this thesis aims to tackle the task of nonlinear system identification, which builds models directly from sequential measurements. More specifically, we approach challenging scenarios, such as learning temporal relations from noisy data, data containing discrepant values (outliers) and large datasets. In the interface between statistics, computer science, data analysis and engineering lies the machine learning community, which brings powerful tools to find patterns from data and make predictions. In that sense, we follow methods based on Gaussian Processes (GP), a principled, practical, probabilistic approach to learning in kernel machines. We aim to exploit recent advances in general GP modeling to bring new contributions to the dynamical modeling exercise. Thus, we propose the novel family of Recurrent Gaussian Processes (RGPs) models and extend their concept to handle outlier-robust requirements and scalable stochastic learning. The hierarchical latent (non-observed) structure of those models impose intractabilities in the form of non-analytical expressions, which are handled with the derivation of new variational algorithms to perform approximate deterministic inference as an optimization problem. The presented solutions enable uncertainty propagation on both training and testing, with focus on free simulation. We comprehensively evaluate the proposed methods with both artificial and real system identification benchmarks, as well as other related dynamical settings. The obtained results indicate that the proposed approaches are competitive when compared to the state of the art in the aforementioned complicated setups and that GP-based dynamical modeling is a promising area of research. / O estudo dos sistemas dinâmicos encontra-se disseminado em várias áreas do conhecimento. Dados sequenciais são gerados constantemente por diversos fenômenos, a maioria deles não passíveis de serem explicados por equações derivadas de leis físicas e estruturas conhecidas. Nesse contexto, esta tese tem como objetivo abordar a tarefa de identificação de sistemas não lineares, por meio da qual são obtidos modelos diretamente a partir de observações sequenciais. Mais especificamente, nós abordamos cenários desafiadores, tais como o aprendizado de relações temporais a partir de dados ruidosos, dados contendo valores discrepantes (outliers) e grandes conjuntos de dados. Na interface entre estatísticas, ciência da computação, análise de dados e engenharia encontra-se a comunidade de aprendizagem de máquina, que fornece ferramentas poderosas para encontrar padrões a partir de dados e fazer previsões. Nesse sentido, seguimos métodos baseados em Processos Gaussianos (PGs), uma abordagem probabilística prática para a aprendizagem de máquinas de kernel. A partir de avanços recentes em modelagem geral baseada em PGs, introduzimos novas contribuições para o exercício de modelagem dinâmica. Desse modo, propomos a nova família de modelos de Processos Gaussianos Recorrentes (RGPs, da sigla em inglês) e estendemos seu conceito para lidar com requisitos de robustez a outliers e aprendizagem estocástica escalável. A estrutura hierárquica e latente (não-observada) desses modelos impõe expressões não- analíticas, que são resolvidas com a derivação de novos algoritmos variacionais para realizar inferência determinista aproximada como um problema de otimização. As soluções apresentadas permitem a propagação da incerteza tanto no treinamento quanto no teste, com foco em realizar simulação livre. Nós avaliamos em detalhe os métodos propostos com benchmarks artificiais e reais da área de identificação de sistemas, assim como outras tarefas envolvendo dados dinâmicos. Os resultados obtidos indicam que nossas propostas são competitivas quando comparadas ao estado da arte, mesmo nos cenários que apresentam as complicações supracitadas, e que a modelagem dinâmica baseada em PGs é uma área de pesquisa promissora.

Des mesures sur parcelles agroforestières (mélanges d'arbres et de cultures) expérimentales mettent en évidence des productivités exceptionnelles, avec des gains de plus de 30% par rapport à l'assolement de cultures pures. Nous avons mis au point et utilisé un modèle numérique dynamique en 3-D (Hi-sAFe) pour (1) tester si l'intégration spatio-temporelle du partage des ressources (lumière, eau, azote) entre espèces suffit à expliquer cette productivité et (2) évaluer l'impact de différents choix de conception ou de gestion sur cette productivité, à partir d'une compréhension des processus impliqués.Nous avons développé une méthode originale d'analyse des données simulées pour identifier et hiérarchiser les processus déterminant le rendement. Le modèle a été paramétré et calibré sur un système noyer hybride / blé dur situé sur le domaine de Restinclières (Hérault, France). Après une analyse détaillée du fonctionnement de ce système, nous avons exploré par expérimentation virtuelle sa réponse à des choix de conception: (1) traits phénologiques des espèces associées, et (2) densité et disposition des arbres sur la parcelle. Par la richesse de son comportement, Hi-sAFe permet un regard nouveau sur le fonctionnement des systèmes agroforestiers. Il éclaire la complexité du système réel. Nos résultats permettent de proposer une interprétation déterministe du rendement de chaque espèce et de la productivité totale de l'association (Land Equivalent Ratio). L'analyse des expérimentations virtuelles permet de proposer des pistes concrètes pour l'optimisation des systèmes agroforestiers. / Measurements on experimental silvoarable agroforestry plots (mixed tree-crop) showed exceptionally high productivity, with yields more than 30% higher compared to the separate crop and tree systems. We developed a 3-D process-based model (Hi-sAFe) and used it to (1) test if the spatiotemporal integration of resources sharing (light, water, nitrogen) between species could explain this productivity and (2) assess how agroforestry design or management may affect this productivity. We developed a novel method for the analysis of simulated data to identify and sort the processes determining yield by order of importance. The model was parameterized and calibrated on a hybrid walnut / durum wheat located on the Restinclières field (Hérault, France). After a detailed analysis of the functioning of this system, we explored by virtual experiments its response to design choices: (1) phenological traits of associated species, and (2) density and location of trees on the plot. The comprehensiveness of the Hi-sAFe model provides a new insight into the functioning of agroforestry systems. It clarifies the complexity of the real system. Our results put forward a deterministic interpretation of the performance of each species and of the total productivity of the crop-tree association (Land Equivalent Ratio). The analysis of virtual experiments offers practical ways for optimizing agroforestry systems.

In this study the dynamical modeling of the unsteady flow over a flapping wing is considered. The technique is based on collecting instantaneous velocity field data of the flow using Particle Image Velocimetry (PIV), applying image processing to these snapshots to locate the airfoil, filling the airfoil and its surface with proper velocity data, applying Proper Orthogonal Decomposition (POD) to these post-processed images to compute the POD modes and time coefficients, and finally fitting a discrete time state space dynamical model to the trajectories of the time coefficients using subspace system identification (N4SID). The procedure is applied using MATLAB for the data obtained from NACA 0012, SD 7003, elliptic airfoil and flat plate, and the results show that the dynamical model obtained can represent the flow dynamics with acceptable accuracy.

Cette thèse intervient dans le cadre du projet ANR ARMS. L'objectif est de concevoir un système robotisé multi-bras pour la découpe anatomique de muscles. Ce travail vise à développer les modèles mécaniques nécessaires à la mise en place de la stratégie de commande. Il expose le cycle de développement d'un modèle mécanique faisant intervenir la construction de modèles géométriques à partir d'images IRM, l'identification expérimentale des paramètres rhéologiques des matériaux modélisés en passant par les étapes de maillage, de paramétrage, d'implémentation et de validation de tels modèles. Il présente une nouvelle méthode de modélisation dynamique de structures intitulée modèle masse-ressort non-linéaire isotrope transverse, une méthode qui témoigne d'un comportement mécanique alliant réalisme et interactivité. Il intervient aussi dans l'identification dynamique des trajectoires de coupe robotisée en proposant de nouvelles approches de modélisation de la découpe de corps mous et en développant un nouvel algorithme basé sur le calcul de courbures. Cette thèse aborde, aussi, le problème de variabilité des muscles bovins et propose une méthode de recalage dimensionnel du modèle géométrique générique par le biais de transformations géométriques définies par optimisation multicritère d'une fonction objectif. Enfin, en vue de synchroniser le flux d'informations entre les différents modules de commande de la cellule robotisée, une combinaison de la méthode des éléments finis avec la technique de condensation statique de Guyan a permis de développer un modèle mécanique quasi-statique réduit permettant de prédire rapidement l'évolution de la trajectoire de coupe robotisée. / This PhD thesis is done within the framework of the ANR ARMS project. The global objective is to study the robotization of deboning and muscle separation in meat cutting and transformation processes applied to beef rounds. This work aims to develop the necessary mechanical models feeding the process control strategy. It outlines the development cycle of a mechanical model involving the construction of geometrical models using MRI techniques, the experimental identification of rheological parameters of materials while going through the steps of meshing, parameterization, implementation and validation of such models. It presents and tests a new way to fix the parameters of the mass-spring model whilst taking into account material anisotropy. The new approach is entitled « non-linear transversally isotropic mass-sping model » and sets the model in non-linear mechanical behavior mode which therefore increases the realism of the simulations performed. It is also involved in the dynamical estimation of the cutting guideline by proposing new approaches for soft materials cutting and by developing a new algorithm based on vision perception and curvature estimation of 3D surfacic meshes. This work addresses also the problem of muscles variability and provides a readjustement method of the generic geometrical model based on a multicriteria optimization of an objective function. Finally, in order to synchronize the information flow between the control modules of the robotic cell, a combination of the finite element method with Guyan static condensation technique allowed developing a reduced quasi-static mechanical model rapidly predicting the evolution of the cutting trajectory.

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