abstract: There is increasing interest in the medical and behavioral health communities towards developing effective strategies for the treatment of chronic diseases. Among these lie adaptive interventions, which consider adjusting treatment dosages over time based on participant response. Control engineering offers a broad-based solution framework for optimizing the effectiveness of such interventions. In this thesis, an approach is proposed to develop dynamical models and subsequently, hybrid model predictive control schemes for assigning optimal dosages of naltrexone, an opioid antagonist, as treatment for a chronic pain condition known as fibromyalgia. System identification techniques are employed to model the dynamics from the daily diary reports completed by participants of a blind naltrexone intervention trial. These self-reports include assessments of outcomes of interest (e.g., general pain symptoms, sleep quality) and additional external variables (disturbances) that affect these outcomes (e.g., stress, anxiety, and mood). Using prediction-error methods, a multi-input model describing the effect of drug, placebo and other disturbances on outcomes of interest is developed. This discrete time model is approximated by a continuous second order model with zero, which was found to be adequate to capture the dynamics of this intervention. Data from 40 participants in two clinical trials were analyzed and participants were classified as responders and non-responders based on the models obtained from system identification. The dynamical models can be used by a model predictive controller for automated dosage selection of naltrexone using feedback/feedforward control actions in the presence of external disturbances. The clinical requirement for categorical (i.e., discrete-valued) drug dosage levels creates a need for hybrid model predictive control (HMPC). The controller features a multiple degree-of-freedom formulation that enables the user to adjust the speed of setpoint tracking, measured disturbance rejection and unmeasured disturbance rejection independently in the closed loop system. The nominal and robust performance of the proposed control scheme is examined via simulation using system identification models from a representative participant in the naltrexone intervention trial. The controller evaluation described in this thesis gives credibility to the promise and applicability of control engineering principles for optimizing adaptive interventions. / Dissertation/Thesis / M.S. Electrical Engineering 2011

Title: Computer Modeling of Tissue Development Author: Vojtěch Bednář Department: Department of applied mathematics Supervisor: Doc. RNDr. Zdeněk Hedrlín, CSc. Abstract: This thesis describes hybrid individual cell-based approach to modeling of systems of biological cells. In the first part reaction-diffusion model of environment is introduced together with vax equilibrium and model of a cell based on zygotic graph and cummulative states. Further, simulations modeling three biologically motivated situations are introduced: Lumen formation, tumor growth, and cellular migration in chronic inflammation. The first model shows a scenario of hollow structure formation based on directional division and cellular migration. The second model is concerned with the growth of a progeny of a slightly damaged cell. The resulting tumor exhibits three stages of malign transformation. Further, emergence of an aggressive tumor without detectable precursor is observed on one hand and a continual transformation of a benign neoplasm into a malign one is seen on the other hand. Each of these cases is a consequence of different parametrization of the model situation. The last model analyses the role of membrane enzymatic activity in migrating cells of the immune system in chronic inflammation. In this model it is observed that...

The transition of emphasis in business competition from a technology-led age to a market-oriented era has led to a rapid shift from the conventional "economy of scale" towards the "economy of scope" in contemporary manufacturing. Hence, it is necessary and essential to be able to respond to the dynamic market and customer requirements systematically and consistently. The central theme of this research is to rationalise and improve the conventional means of analysing and interpreting the linguistic and often imprecise customer requirements in order to identify the essential product features and determine their appropriate design targets dynamically and quantitatively through a series of well proven methodologies and techniques. The major objectives of this research are: a) To put forward a hybrid approach for decoding and processing the Voice of Customer (VoC) in order to interpret the specific customer requirements and market demands into definitive product design features, and b) To quantify the essential product design features with the appropriate technical target values for facilitating the downstream planning and control activities in delivering the products or services. These objectives would be accomplished through activities as follows: • Investigating and understanding the fundamental nature and variability of customer attributes (requirements); • Surveying and evaluating the contemporary approaches in handling customer attributes; • Proposing an original and generic hybrid model for categorising, prioritising and interpreting specific customer attributes into the relevant product attributes with tangible target values; • Developing a software system to facilitate the implementation of the proposed model; • Demonstrating the functions of the hybrid model through a practical case study. This research programme begins with a thorough overview of the roles, the changing emphasis and the dynamic characteristics of the contemporary customer demand with a view to gaining a better understanding on the fundamental nature and variability of customer attributes. It is followed by a review of a number of well proven tools and techniques including QFD, HoQ, Affinity Diagram and AHP etc. on their applicability and effectiveness in organising, analysing and responding to dynamic customer requirements. Finally, an intelligent hybrid model amalgamating a variety of these techniques and a fuzzy inference sub-system is proposed to handle the diverse, ever-changing and often imprecise VoC. The proposed hybrid model is subsequently demonstrated in a practical case study.

Objetivo. Desenvolvimento de uma estrutura para aplicação de simulação numérica estocástica no estudo de espalhamento de doenças em metapopulações de maneira que esta incorpore a topologia dinâmica de contatos entre as subpopulações, verificando as peculiaridades do modelo e aplicando este modelo às redes de movimentação de animais de Pernambuco para estudar o papel das feiras de animais. Método. Foi utilizado o paradigma de modelos híbridos para tratar do espalhamento de doenças nas metapopulações que, das nossas aplicações, resultou na união de duas estratégias de modelagem: Modelos Baseados no Indivíduo e o Algorítimo de Simulação Estocástica. Aplicamos os modelos híbridos em redes de movimentação de animais reais e fictícias para destacar as diferenças dos modelos híbridos com diferentes abordagens de migração (pendular e definitiva) e comparamos estes modelos com modelos clássicos de equações diferenciais. Ainda, através do pacote hybridModels, estudamos o papel das feiras de animais em cenários de epidemia de febre aftosa na rede de movimentação de animais de Pernambuco, introduzindo a doença numa feira de animais contida numa amostra da base de Guia de Trânsito Animal e calculamos a cadeia de infecção dos estabelecimentos. Resultados. Constatamos que no estudo de epidemias com o uso de modelo híbrido, a migração pendular, na média, subestima o número de animais infectados no cenário de comercialização de animais (migração defi nitiva), além de traduzir uma dinâmica de espalhamento enganosa, ignorando cenários mais complexo oferecido pela migração definitiva. Criamos o pacote hybridModels que generaliza os modelos híbridos com migração definitiva e com ele aplicamos um modelo híbrido SIR na rede de Pernambuco e verificamos que as feiras de animais de Pernambuco são potentes disseminadores de doenças transmissíveis. Conclusão. Apesar de custo computacional maior no estudo de espalhamento de doenças, a migração definitiva é o mais adequado tipo de conexão entre as subpopulações de animais de produção. Ainda, de acordo com as nossas analises, as feiras de animais estão entre os mais importantes nós na rede de movimentação de Pernambuco e devem ter lugar de destaque nas estratégias de controle e vigilância epidemiológica / Objective. Development of framework applied to stochastic numerical simulation for the study of disease spreading in metapopulations, in a way that it incorporates the dynamic topology of contacts between subpopulations, checking the framework peculiarities and applying it to the animal movement network of Pernambuco to study the role of animal markets. Method. We used hybrid models paradigm to treat disease spread in metapopulations. From our applications it has resulted in the union of two modeling strategies: Individual-based model and the Algorithm for Stochastic Simulation. We applied hybrid models in real and fictitious networks to highlight the differences between different animal movement approaches (commuting and migration) and we compared these models with classic models of differential equations. Furthermore, through the hybridModels package, we studied the role of animal markets in epidemic scenarios of Foot and Mouth Disease (FMD) in animal movement networks of Pernambuco, introducing the disease in an animal market of a sample from the Animal Transit Record of Pernambuco’s database and calculating the contact infection chain of premises. Results. We noted that in the study of epidemics using a hybrid model, commuting can underestimates the number of infected animals in the animal trade scenario (migration), and resulting in a misleading spreading dynamic by ignoring a more complex scenario that occurs with migration. We created the hybridModels package that generalizes the hybrid models with migration, applied a SIR hybrid model to the animal movement network of Pernambuco and verified that animal markets are important disease spreaders. Conclusion. Despite its higher computational cost in the study of epidemics in animal movement networks, migration is the most suitable type of connection between subpopulations. Furthermore, animal markets of Pernambuco are among the most important nodes for disease transmission and should be considered in strategies of surveillance and disease control

Dans le contexte énergétique actuel, les pompes à chaleur (PAC) géothermiques sont parmi les technologies les plus performantes pour augmenter l’efficacité énergétique des bâtiments. Par contre le coût initial et l’encombrement des capteurs enterrés traditionnels peuvent être un obstacle à sa diffusion sur le marché des énergies renouvelables. Pour réduire ces coût et encombrement, une réflexion sur l’adjonction d’un système d’appoint et/ou de recharge thermique du sol aux capteurs enterrés est actuellement en cours de tests. Les outils actuels de modélisation des capteurs enterrés obtiennent en effet de bons résultats mais seulement pour un dimensionnement classique en régime permanent. Les modèles existants ne permettent donc pas de représenter correctement les dynamiques rapides des échanges entre le sol et les tubes et cela est d’autant plus vrai si l’on adjoint le système de recharge solaire. Par conséquence, cette thèse a pour objectif de développer les modèles fins et dynamiques nécessaires à l’analyse des phénomènes transitoires dans les capteurs enterrés eux-mêmes. Un maillage fin, sur les bases de la triangulation de Delaunay, est choisi pour le forage ainsi que pour le sol avoisinant. Une approche numérique en 3D (FVM + FEM) peut être obtenue sur les bases de la discrétisation spatiale du domaine. Cette approche appliquée brutalement induirait des temps de calcul très élevés et de toute façon incompatible avec les moyens informatiques ordinaires. Afin de répondre à l’ensemble de ces problèmes, différentes techniques ont été utilisées afin d’accélérer le temps de calcul: décomposition de domaine, emboîtement des pas de temps de calcul pour chaque sous-domaine, réduction des modèles d’états de chaque sous-domaine et finalement couplages temporels et spatiaux des équations de transferts de l’ensemble du problème. Ce dernier est développé particulièrement sur les bases de la méthode des éléments finis. Par ailleurs, un modèle hybride est développé en combinaison de différentes approches. Une approche numérique est adoptée pour la modélisation du puits et la modélisation des transferts de chaleur dans le sol environnant est faite par l’utilisation de solutions analytiques. Ainsi, ce modèle est implanté dans TRNSYS. Une plate-forme expérimentale comprenant trois puits verticaux couplés à une pompe à chaleur géothermique est également présentée. Les résultats expérimentaux sont comparés avec les résultats de la simulation aussi bien au niveau de la température du fluide qu’à la température à différentes profondeurs dans les puits. Le modèle développé donne des résultats très similaires avec ceux qui sont obtenus grâce à l’expérimentation même lors que les pas de temps sont très petits. Il y a des choses à améliorer dans ce modèle développé, mais cela concerne essentiellement l’accélération du temps de calcul. Nous avons constaté que les modèles que nous avons dévéloppés donnent des résultats meilleurs à pas de temps courts que les modèles classiques. Il faut donc bien préciser le domaine d’utilisation de chacun des modèles: consommation sur le long terme, stratégie de contrôle de la PAC, les transferts de chaleur à l’intérieur du puits et etc. De plus, une application du modèle dans le dimensionnement d’échangeurs ainsi que l’investigation de son impact sur le sol avoisinant est également envisagée. Finalement, la méthodologie de modélisation présentée dans ce travail pourrait être aussi utilisé pour différents types d’échangeurs, ouvrant aussi la porte à une analyse fine dans le domaine géothermique. / Ground-source heat pump systems with vertical ground heat exchangers (GHE) are gaining popularity worldwide for their higher coefficients of performance and lower CO2 emissions. However, the higher initial cost of installing the borehole GHEs is a main obstacle to spread the systems. To reduce the required total GHE length and efficiently operate the systems, various systems such as hybrid ones (e.g. solar heat injection) have recently been introduced. Accurate prediction of heat transfer in and around boreholes of such systems is crucial to avoid costly overdesigns or catastrophic failures of undersized systems as it is for typical GCHP systems. However, unlike the traditional sizing methods, it is increasingly required to take into account detailed borehole configuration and transient effects (e.g. short circuit effects between U-tubes). Many of the existing GHE models have been reviewed. Some of these models have serious limitations when it comes to transient heat transfer, particularly in the borehole itself. Accordingly, the objective of this thesis is to develop a model that is capable to accurately predict thermal behaviors of the GHEs. A precise response to input variations even in a short time-step is also expected in the model. The model also has to account for a correct temperature and flux distribution between the U-tubes and inside the borehole that seems to be important in the solar heat injection case. Considering these effects in 3D with a detailed mesh used for describing the borehole configurations is normally time-consuming. This thesis attempts to alleviate the calculation time using state model reduction techniques that use fewer modes for a fast calculation but predict similar results. Domain decomposition is also envisaged to sub-structure the domain and vary the time-step sizes. Since the decomposed domains should be coupled one another spatially as well as temporally, new coupling methods are proposed and validated particularly in the FEM. For the simulation purpose, a hybrid model (HM) is developed that combines a numerical solution, the same one as the 3D-RM but only for the borehole, and well-known analytical ones for a fast calculation. An experimental facility used for validation of the model has been built and is described. A comparison with the experimental results shows that the relatively fast transients occurring in the borehole are well predicted not only for the outlet fluid temperature but also for the grout temperatures at different depths even in very short time-steps. Even though the current version of 3D-RM is experimentally validated, it is still worth optimizing the model in terms of the computational time. Further simulations with the 3D-RM are expected to be carried out to estimate the performance of new hybrid systems and propose its appropriate sizing with correspondent thermal impacts on the ground. Finally, the development of the model 3D-RM can be an initiation to accurately model various types of GHE within an acceptable calculation time.

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第23311号 / 情博第747号 / 新制||情||128(附属図書館) / 京都大学大学院情報学研究科知能情報学専攻 / (主査)准教授 吉井 和佳, 教授 河原 達也, 教授 西野 恒, 教授 鹿島 久嗣 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

Ferroelektroelastische Keramiken besitzen aufgrund ihrer elektromechanischen Koppeleigenschaften Bedeutung in der Sensorik und Aktuatorik. Zur Vorhersage der Bauteileigenschaften und Beurteilung der Bauteilfestigkeit werden Materialmodelle benötigt. In der vorliegenden Arbeit wird ein mehrachsiges, ratenunabhängiges Materialmodell für ferroelektroelastische Keramiken einschließlich der zur effizienten Lösung notwendigen numerischen Methoden ausgearbeitet. Dabei erfolgt die Einbeziehung von Ansätzen aus der makroskopischen phänomenologischen und mikroelektromechanischen phänomenologischen Modellierung. Das resultierende Materialmodell stellt einen Versuch dar, die Vorteile beider Betrachtungsweisen zu vereinen und trägt deshalb die Bezeichnung "hybrid". In einem ersten Beispiel wird gezeigt, dass das hybride Materialmodell die für Barium-Titanat-Keramiken experimentell beobachtete Materialantwort reproduzieren kann. In einem zweiten Beispiel erfolgt die Anwendung auf morphotrope PZT-Keramiken. Dabei wird die in jüngerer Vergangenheit entdeckte monokline Phase zusammen mit der elektronenmikroskopisch beobachteten hierarchischen Struktur von Mikro- und Nanodomänen in vereinfachter Weise berücksichtigt. Auf Grundlage der getroffenen Modellannahmen gelingt es, die experimentell gemessene makroskopische Materialantwort der morphotropen PZT-Keramik PIC151 (PI Ceramic GmbH, Lederhose, Deutschland) für ausgewählte Lastfälle mit guter Genauigkeit vorherzusagen. / Ferroelectroelastic ceramics are used in sensor and actuator applications due to their electromechanical coupling properties. In order to predict the behavior of components or to assess their strength, material models are required. In the present work, a multi-axial, rate-independent material model for ferroelectroelastic ceramics is elaborated. This includes the development of efficient numerical solution methods. By incorporating ideas from known macroscopic phenomenological and micro-electromechanical phenomenological models into the novel model, it is attempted to combine the advantages of both approaches. In a first example, it is shown that the hybrid model can reproduce the experimentally observed material response of barium titanate ceramics. In a second example, the model is applied to morphotropic PZT ceramics. In this context, the recently discovered monoclinic phase as well as the hierarchical structure of micro-domains and nano-domains observed by means of electron microscopy are taken into account in a simplified way. Based on the assumptions made, the experimentally measured material response of the morphotropic PZT ceramic PIC151 (PI Ceramic GmbH, Lederhose, Germany) is predicted with reasonable accuracy for selected load cases.

No description available.

Forests in the southern United States with diverse forest ownership entities are facing threats associated with climate change and natural disturbances. This study represented the relationship between climate and species dominance, predicted future species distribution probability under a changing climate, and projected forest dynamics under ownership-based management regimes. Correlative statistics and mechanistic modeling approaches are implemented. Temporal scale includes the recent past 40 years and the future 60 years; spatial scale downscaled from southern United States to the coastal region of the northern Gulf of Mexico. In the southern United States, dominance of four major pine species experienced shifts from 1970 to 2000; quantile regression models built on the relationships among pine dominance and climatic variables can be used to predict future southern pine dominance. Furthermore, multiple climate envelope models (CEMs) were constructed for nineteen native and one invasive tree species (Chinese tallow, Triadica sebifera) to predict species establishment probabilities (SEPs) on the various land types from 2010 to 2070. CEMs achieved both predictive consistency and ecological conformity in estimating SEPs. Chinese tallow was predicted to have the highest invasionability in longleaf/slash pine and oak/gum/cypress forests during the next 60 years. Forest dynamics, in the coastal region, was projected by linking CEMs and forest landscape model (LANDIS) to evaluate ownership-based management regimes under climate change and natural disturbances. The dominance of forest species will diminish due to climate change and natural disturbances at both spatial scales—in the coastal region and non-industrial private forest (NIPF). No management on NIPF land was predicted to substantially increase the ratio of occupancy area between pines and oaks, but moderate and intensive management regimes were not significantly different. Pines are expected to be more resistant than oaks by maintaining stable age structures, which matched the forest inventory records. Overall, this study projected a future of southern forests on climate-species relationship, invasion risks, and forest community dynamics under multiple scenarios in the United States. Such knowledge could assist forest managers and landowners in foreseeing the future and making effective management prescriptions to mitigate potential threats.

Industrial automation systems normally consist of four different hierarchy levels: planning, scheduling, real-time optimization, and control. At the planning level, the goal is to compute an optimal production plan that minimizes the production cost while meeting process constraints. The planning model is typically formulated as a mixed integer nonlinear programming (MINLP), which is hard to solve to global optimality due to nonconvexity and large dimensionality attributes. Uncertainty in component qualities in gasoline blending due to measurement errors and variation in upstream processes may lead to off-specification products which require re-blending. Uncertainty in product demands may lead to a suboptimal solution and fail in capturing some potential profit due to shortage in products supply. While incorporating process uncertainties is essential to reducing the production cost and increasing profitability, it comes with the disadvantage of increasing the complexity of the MINLP planning model. The key contribution in the planning level is to employ the inventory pinch decomposition method to consider uncertainty in components qualities and products demands to reduce the production cost and increase profitability of the gasoline blend application. At the control level, the goal is to ensure desired operation conditions by meeting process setpoints, ensure process safety, and avoid process failures. Model predictive control (MPC) is an advanced control strategy that utilizes a dynamic model of the process to predict future process dynamic behavior over a time horizon. The effectiveness of the MPC relies heavily on the availability of a reasonably accurate process model. The key contributions in the control level are: (1) investigate the use of different system identification methods for the purpose of developing a dynamic model for high-purity distillation column, which is a highly nonlinear process. (2) Develop a novel hybrid based MPC to improve the control of the column and achieve flooding-free control. / Dissertation / Doctor of Philosophy (PhD) / The operation of a chemical process involves many decisions which are normally distributed into levels referred to as process automation hierarchy. The process automation hierarchy levels are planning, scheduling, real-time optimization, and control. This thesis addresses two of the levels in the process automation hierarchy, which are planning and control. At the planning level, the objective is to ensure optimal utilization of raw materials and equipment to reduce production cost. At the control level, the objective is to meet and follow process setpoints determined by the real-time optimization level. The main goals of the thesis are: (1) develop an efficient algorithm to solve a large-scale planning problem that incorporates uncertainties in components qualities and products demands to reduce the production cost and maximize profit for gasoline blending application. (2) Develop a novel hybrid-based model predictive control to improve the control strategy of an industrial distillation column that faces flooding issues.