Karst aquifers can be conceptualized as dual flow systems comprised of a low-conductive matrix with embedded high-conductive conduits / preferential flow zones. Discharge in conduits ranges from low-velocity laminar flow to high-velocity transitional and turbulent flow. Commonly employed continuum models do not account for the specific behavior of transitional and turbulent flow. In response to this limitation, enhancements have been made to MODFLOW, a commonly used groundwater flow model, by adding a discrete conduit network to the matrix continuum (hybrid model). The Conduit Flow Process (CFP) package is the latest realization of this model approach. CFP Mode 1 (CFPM1) computes laminar and turbulent flow in discrete conduits that are coupled to the laminar continuum model. CFP Mode 2 (CFPM2) accounts for turbulent flow in preferential flow layers by adapting the continuum model. Therefore, laminar hydraulic conduc-tivities are converted into turbulent hydraulic conductivities. CFPM2 was further modified to consider steady turbulent pipe flow. Karst models based on CFPM2 require potentially less input data and computational efforts than karst models based on CFPM1. Furthermore, CFPM2 integrates more easily into MODFLOW versions including e.g. transport models. Parameter studies for a synthetic catchment demonstrates that continuum models with turbulent flow representation and an additional flow barrier between conduits and matrix can represent karst systems similar to hybrid models. For simulation of highly transient flow processes in karst conduit systems, i.e. during flood events, it is crucial to consider dynamics such as free-surface flow, wave propagation, and changes between pressurized and non-pressurized conduit flow. The coupled overland- and groundwater flow model MODBRANCH was therefore enhanced to consider unsteady and non-uniform flow processes in karst conduits. Flow in discrete conduits is simulated using the Saint-Venant-equations for free-surface flow. Contrary to overland flow, the cross sectional area of karst conduits is finite. Accordingly, both pressurized and non-pressurized flow may occur within conduits. To simulate pressurized flow, a hypothetical, narrow, open-top slot (Preissmann slot) is added to the conduit crown, which allows the use of the free-surface flow equations for fully filled conduits. Beyond this, the model features a variable time step to consider wave speed variations, for example due to the transition from free-surface to pressurized flow. Parameter studies for a synthetic catchment demonstrate the significance of free-surface flow representation for variably filled conduits. / Karstgrundwasserleiter können als duale Fließsysteme konzeptionalisiert werden, bestehend aus einer geringdurchlässigen Matrix mit eingebundenen hochdurchlässigen Bereichen, z. B. Karströhren. Der Abfluss in den hochdurchlässigen Bereichen reicht von langsamer laminarer Strömung bis zu schneller turbulenter Strömung. Herkömmliche numerische Grundwasser-strömungsmodelle berücksichtigen nicht die spezifischen Eigenschaften von nicht-laminarer Strömung (Übergangsbereich laminar-turbulent bzw. turbulente Verhältnisse). Ein Ansatz um diese Einschränkung zu umgehen, ist die Erweiterung des laminaren Kontinuums um ein dis-kretes Röhrenmodell, das zustandsabhängig laminare und turbulente Strömung berücksichtigt (Hybridmodell). Eine aktuelle Umsetzung dieses Ansatzes ist Conduit Flow Process (CFP), ein Modul für das weitverbreitete Grundwasserströmungsmodell MODFLOW. CFP Mode 1 (CFPM1) berechnet laminare und turbulente Strömung in diskreten, mit dem Kontinuummodell gekoppelten Röhren. CFP Mode 2 (CFPM2) berücksichtigt nicht-laminare Strömung in hochdurchlässigen Schichten mit einer angepassten hydraulischen Leitfähigkeit des Kontinuummodells. CFPM2 wurde weiter modifiziert, so dass auch turbulente Strömung in Karströhren berechnet werden kann. Dadurch kann möglicherweise der Parameterbedarf sowie der Rechenaufwand gegenüber Hybrid¬modellen reduziert werden. CFPM2 lässt sich einfach in vorhandene MODFLOW Modelle einbinden, z. B. zur Berechnung von Transportprozessen. Parameterstudien für ein idealisiertes Karsteinzugsgebiet zeigen, dass Kontinuummodelle bei Berücksichtigung der turbulenten Strömung sowie des zusätzlichen hydraulischen Widerstand zwischen Röhren und Matrix, Karstsysteme ähnlich wie Hybridmodelle darstellen. Zur Simulation von instationären Prozessen in Karströhren, z. B. ausgeprägte Abflusssignale infolge pulsförmiger Grundwasserneubildung, ist es notwendig, dynamische Prozesse infolge Freispiegelabfluss, Wellenausbreitung sowie Wechsel zwischen Abfluss in teil- und vollgefüllten Röhren zu berücksichtigen. Aus diesem Grund wurde das numerische Modell MODBRANCH, welches ein diskretes Oberflächenwassermodell mit einem Kontinuummodell koppelt, so angepasst, dass instationäre und nichtgleichförmige Abflussprozesse in Karströhren berücksichtigt werden können. Der Abfluss in diskreten Röhren wird dabei mit den Saint-Venant-Gleichungen für Freispiegelabfluss berechnet. Im Gegensatz zu Oberflächengewässern ist der für den Abfluss zur Verfügung stehende Querschnitt in Karströhren limitiert, so dass sowohl Freispiegel- als auch Druckabfluss innerhalb der Röhren auftreten kann. Druckabfluss wird mit Hilfe eines schmalen virtuellen Schlitzes an der Röhrenoberkante simuliert (Preissmann Schlitz), der auch im Fall vollgefüllter Röhren die Anwendung der Gleichungen für Freispiegelabfluss erlaubt. Durch die Verwendung eines variablen Zeitschrittes kann die geänderte Dynamik beim Übergang von Freispiegel- zu Druckabfluss berücksichtigt werden. Parameterstudien für idealisierte, synthetische Karsteinzugsgebiete demonstrieren die Bedeutung der Berücksichtigung von Freispiegelabfluss in teilgefüllter Röhren.

[pt] Existe uma crescente demanda por modelos dinâmicos precisos, parte impulsionada pelo paradigma da indústria 4.0 que introduz, dentre outros, o conceito de gêmeo digital no qual modelos dinâmicos possuem um papel importante. Idealmente, um modelo dinâmico apresenta um compromisso entre complexidade e precisão, enquanto proporciona informações sobre a física do sistema. Para melhorar a precisão de um modelo mantendo a interpretabilidade, a abordagem usual é modelar matematicamente todas não-linearidades, o que leva a um modelo muito complexo. Outra abordagem envolve identificação caixa-preta, uma abordagem onde um modelo matemático é ajustado para descrever a relação de entrada e saída do sistema, a qual pode fornecer um modelo preciso, porém não interpretável. Os avanços na capacidade de processamento computacional permitiram o florescimento da area de aprendizado de máquinas, a qual tem mostrado resultados interessantes em diferentes campos do conhecimento. Uma dessas aplicações é em identificação caixa-preta, onde o aprendizado de máquinas tem sido empregado com sucesso na modelagem de sistemas não-lineares, o que tem inspirado pesquisas sobre o tema. Apesar dos modelos baseados em aprendizado de máquina apresentarem elevada precisão, o que é suficiente para diversas aplicações, eles não são interpretáveis. Dessa forma, visando obter modelos que possuem ambas as características de precisão e interpretabilidade, enquanto mantém um compromisso com a complexidade, esta tese propõe uma metodologia de identificação híbrida que combina um modelo fenomenológico caixa cinza com um modelo caixa preta baseado em redes neurais artificiais. A metodologia proposta é aplicada em três casos de estudo de sistemas não lineares com dados experimentais, a saber, a dinâmica vertical de um veículo, um atuador com junta flexível baseado em elastômero e um sistema de posicionamento eletromecânico. Os resultados mostram que o modelo híbrido proposto é até 60 por cento mais preciso enquanto proporciona a interpretabilidade física do sistema, sem aumentar significativamente a complexidade do modelo. / [en] There is a growing demand for accurate dynamic models, driven by the Industry 4.0 paradigm that introduces, among others, the concept of the digital twin in which dynamic models play an important role. Ideally, a dynamic model presents a compromise between complexity and accuracy, while providing physical insight into the system. To improve a model accuracy while keeping interpretability, the usual approach is to mathematically model all the nonlinearities, which ultimately leads to an overcomplex model. Another approach involves a black-box identification, a data-driven approach where a mathematical model is adjusted to describe the system s input-output relation, which may provide an accurate model, but it does not provide interpretability. The developments in computational processing capacity have allowed the flourishing of the field of machine learning, which has shown interesting results in different fields of knowledge. One of these applications is black-box identification, where machine learning has successfully been employed in the modeling of nonlinear systems, which has inspired research on the topic. Even though the machine-learning-based models present enhanced accuracy, which for several applications is sufficient, they do not provide interpretability. Aiming at providing both accuracy and interpretability while keeping a compromise with model complexity, this work proposes a hybrid identification methodology that combines a gray-box phenomenological model with a black-box model based on artificial neural networks. The proposed methodology is applied in three case studies of nonlinear systems with experimental data, namely, the vertical dynamics of a vehicle, an elastomer-based series elastic actuator, and an electromechanical positioning system. The results show that the proposed hybrid model is up to 60 percent more accurate while providing the physical interpretability of the system, without significantly increasing the complexity of the model.

Time series forecasting and volatility forecasting is a particularly active research field within financial mathematics. More recent studies extend well-established forecasting methods with machine learning. This thesis will evaluate and compare the standard Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and some of its extensions to a proposed Long Short-Term Memory (LSTM) model on historic data from five Swedish stocks. It will also explore hybrid models that combine the two techniques to increase prediction accuracy over longer horizons. The results show that the predictability increases when switching from univariate GARCH and LSTM models to hybrid models combining them both. Combining GARCH, Glosten, Jagannathan, and Runkle GARCH (GJR-GARCH), and Fractionally Integrated GARCH (FIGARCH) yields the most accurate result with regards to mean absolute error and mean square error. The forecasting errors decreased with 10 to 50 percent using the hybrid models. Comparing standard GARCH to the hybrid models, the biggest gains were seen at the longest horizon, while comparing the LSTM to the hybrid models, the biggest gains were seen for the shorter horizons. In conclusion, the prediction ability increases using the hybrid models compared to the regular models. / Tidsserieprognostisering, och volatilitetsprognostiering i synnerhet, är ett växande fält inom finansiell matamatik som kontinereligt står inför implementation av nya tekniker. Det som en gång startade med klassiksa tidsseriemodeller som ARCH har nu utvecklats till att dra fördel av maskininlärning och neurala nätverk. Detta examensarbetet uvärderar och jämför Generalized Autoregressive Conditional Heteroskedasticity (GARCH) modeller och några av dess vidare tillämpningar med Long Short-Term Memory (LSTM) modeller på fem svenska aktier. ARbetet kommer även gå närmare inpå hybridmodeller som kombinerar dessa två tekniker för att öka tillförlitlig prognostisering under längre tidshorisonter. Resultaten visar att förutsägbarheten ökar genom att byta envariata GARCH och LSTM modeller till hybridmodeller som kombinerar båda delarna. De mest korrekta resultaten kom från att kombinera GARCH, Glosten, Jagannathan, och Runkle GARCH (GJR-GARCH) och Fractionally Integrated GARCH (FIGARCH) modeller med ett LSTM nätverk. Prognostiseringsfelen minskade med 10 till 50 procent med hybridmodellerna. Specifikt, vid jämförelse av GARCH modellerna till hybridmodellerna sågs de största förbättringarna för de längre tidshorisonterna, medans jämförelse mellan LSTM och hybridmodellerna sågs den mesta förbättringen hos de kortare tidshorisonterna. Sammanfattningsvis öker prognostiseringsförmågan genom användning av hybridmodeller i jämförelse med standardmodellerna.

Ambient air pollution is among the top 10 health risk factors in the US. With increasing concerns about adverse health effects of ambient air pollution among stakeholders including environmental scientists, health professionals, urban planners and community residents, improving air quality is a crucial goal for developing healthy communities. The US Environmental Protection Agency (EPA) aims to reduce air pollution by regulating emissions and continuously monitoring air pollution levels. Local communities also benefit from crowd-sourced monitoring to measure air pollution, particularly with the help of rapidly developed low-cost sampling technologies. The shift from relying only on government-based regulatory monitoring to crowd-sourced effort has provided new opportunities for air quality data. In addition, the fast-growing data sciences (e.g., data mining) allow for leveraging open data from different sources to improve air pollution exposure assessment. My dissertation investigates how new data sources of air quality (e.g., community-based monitoring, low-cost sensor platform) and model predictor variables (e.g., non-government open data) based on emerging modeling approaches (e.g., machine learning [ML]) could be used to improve air quality models (i.e., land use regression [LUR]) at local, regional, and national levels for refined exposure assessment. LUR models are commonly used for predicting air pollution concentrations at locations without monitoring data based on neighboring land use and geographic variables. I explore the use of crowd-sourced low-cost monitoring data, new/open dataset from government and non-government sponsored platforms, and emerging modeling techniques to develop LUR models in the US. I focus on testing whether: (1) air quality data from community-based monitoring is feasible for developing LUR models, (2) air quality data from non-government crowd-sourced low-cost sensor platforms could supplement regulatory monitors for LUR development, and (3) new/open data extracted from non-government sponsored platforms could serve as alternative datasets to traditional predictor variable sources (e.g., land use and geographic features) in LUR models. In Chapter 3, I developed LUR models using community-based sampling (n = 50) for 60 volatile organic compounds (VOC) in the city of Minneapolis, US. I assessed whether adding area source-related features improves LUR model performance and compared model performance using variables featuring area sources from government vs. non-government sponsored platforms. I developed three sets of models: (1) base-case models with land use and transportation variables, (2) base-case models adding area source variables from local business permit data (government sponsored platform), and (3) base-case models adding Google point of interest (POI) data for area sources. Models with Google POI data performed the best; for example, the total VOC (TVOC) model had better goodness-of-fit (adj-R2: 0.56; Root Mean Square Error [RMSE]: 0.32 µg/m3) as compared to the permit data model (0.42; 0.37) and the base-case model (0.26; 0.41). This work suggests that VOC LUR models can be developed using community-based samples and adding Google POI could improve model performance as compared to using local business permit data. In Chapter 4, I evaluated a national LUR model using annual average PM2.5 concentrations from low-cost sensors (i.e., PurpleAir platform) in 6 US urban areas (n = 149) and tested the feasibility of using low-cost sensor data for developing LUR models. I compared LUR models using only the PurpleAir sensors vs. hybrid LUR models (combining both the EPA regulatory monitors and the PurpleAir sensors). I found that the low-cost sensor network could serve as a promising alternative to fill the gaps of existing regulatory networks. For example, the national regulatory monitor-based LUR (i.e., CACES LUR developed as part of the Center for Air, Climate, and Energy Solutions) may fail to capture locations with high PM2.5 concentrations and the within-city spatial variability. Developing LUR models using the PurpleAir sensors was reasonable (PurpleAir sensors only: 10-fold CV R2 = 0.66, MAE = 2.01 µg/m3; PurpleAir and regulatory monitors: R2 = 0.85, MAE = 1.02 µg/m3). I also observed that incorporating PurpleAir sensor data into LUR models could help capture within-city variability and merit further investigation on areas of disagreement with the regulatory monitors. This work suggests that the use of crowd-sourced low-cost sensor networks for LUR models could potentially help exposure assessment and inform environmental and health policies, particularly for places (e.g., developing countries) where regulatory monitoring network is limited. In Chapter 5, I developed national LUR models to predict annual average concentrations of 6 criteria pollutants (NO2, PM2.5, O3, CO, SO2 and PM10) in the US to compare models using new data (Google POI, Google Street View [GSV] and Local Climate Zone [LCZ]) vs. traditional geographic variables (e.g., road lengths, area of built land) based on different modeling approaches (partial least square [PLS], stepwise regression and machine learning [ML] with and without Kriging effect). Model performance was similar for both variable scenarios (e.g., random 10-fold CV R2 of ML-kriging models for NO2, new vs. traditional: 0.89 vs. 0.91); whereas adding the new variables to the traditional LUR models didn't necessarily improve model performance. Models with kriging effect outperformed those without (e.g., CV R2 for PM2.5 using the new variables, ML-kriging vs. ML: 0.83 vs. 0.67). The importance of the new variables to LUR models highlights the potential of substituting traditional variables, thus enabling LUR models for areas with limited or no data (e.g., developing countries) and across cities. The dissertation presents the integration of new/open data from non-government sponsored platform and crowd-sourced low-cost sensor networks in LUR models based on different modeling approaches for predicting ambient air pollution. The analyses provide evidence that using new data sources of both air quality and predictor variables could serve as promising strategies to improve LUR models for tracking exposures more accurately. The results could inform environment scientists, health policy makers, as well as urban planners interested in promoting healthy communities. / Doctor of Philosophy / According to the US Centers for Disease Control and Prevention (CDC), a healthy community aims at preventing disease, reducing health gaps, and creating more accessible options for a wider population. Outdoor air pollution has been evidenced to cause a wide range of diseases (e.g., cardiovascular diseases, respiratory diseases, diabetes and adverse birth outcome), ranking as the top 10 health risks in the US. Thus, improving understanding of ambient air quality is one of the common goals among environmental scientists, urban planners, health professionals, and local residents to achieving healthy communities. To understand air pollution exposures in different areas, US Environmental Protection Agency (EPA) has regulatory monitors for outdoor air pollution measurements across the country. For locations without these regulatory monitors, land use regression (LUR) models (one type of air quality models) are commonly employed to make a prediction. Usually, information including number of people, location of bus stops, and type of roads are shared online from government websites. These datasets are often used as significant predictor variables for developing LUR models. Questions remain on whether new air quality data and alternative land use data from non-government sources could improve air quality modeling. In recent years, local communities have been actively involving in air pollution monitoring using rapidly developed low-cost sensors and sampling campaigns with the help of local residents. In the meantime, advances in data sciences make open data much easier to acquire and use, particularly from non-government sponsored platforms. My dissertation aims to explore the use of new data sources including community-based low-cost monitoring data and open dataset from non-government websites in LUR modes based on emerging modeling techniques (e.g. machine learning) to predict air pollution levels in the US. I first built LUR models for volatile organic compounds (VOC: organic chemicals with a high vapor pressure at room temperature [e.g., Benzene]) based on community-based sampling data in the City of Minneapolis, US. I added information on number of neighboring gas stations, dry cleaners, paint booths, and auto shops from both the local government and Google website into the model and compared the model performance for both data sources (Chapter 3). Then, I used PM2.5 data from a non-government website (PurpleAir low-cost sensors) for 6 US cities evaluating an existing air quality model that used air quality data from government websites. I further developed LUR models using the PurpleAir PM2.5 data to see whether this non-government source of low-cost sensor data could be as reasonable as the government data for LUR model development. I finally extracted new/open data from non-government sponsored platforms (e.g., Google products and local climate zone [LCZ: a map that describes the development patterns of land, such as high-rise vs. low-rise or trees vs. sands]) in the US to investigate if these data sources can be used to alternate the land use and geographic data often used in national LUR model development. I found that: (1) adding information (e.g., number of neighboring gas stations) from non-government sponsored sources (e.g., Google) could improve the air quality model performance for VOCs, (2) integrating non-government low-cost PM2.5 sensor data into government regulatory monitoring data to develop LUR models could improve model performance and offer more insights on the air pollution exposure, (3) new/open data from non-government sponsored platforms could be used to replace the land use and geographic data previous obtained from government websites for air quality models. These findings mean that air quality data and street-level land use characteristics could serve as alternative data sources and are capable of developing better air quality models for promoting healthy communities.

股價的漲跌變化是由於證券市場中眾多不同投資人及其投資決策後所產生的結果。然而，影響股價變動的因素眾多且複雜，新聞也屬於其中一種，新聞事件不但是投資人用來得知該股票上市公司的相關營運資訊的主要媒介，同時也是影響投資人決定或變更其股票投資策略的主要因素之一。本研究提出以新聞文件做為股價漲跌預測系統的基礎架構，透過文字探勘技術及分類技術來建置出能預測當日個股收盤股價漲跌趨勢之系統。 本研究共提出三種分類模型，分別是簡易貝氏模型、k最近鄰居模型以及混合模型，並設計了三組實驗，分別是分類器效能的比較、新聞樣本資料深度的比較、以及新聞樣本資料廣度的比較來檢驗系統的預測效能。實驗結果顯示，本研究所提出的分類模型可以有效改善相關研究中整體正確率高但各個類別的預測效能卻差異甚大的情況。而對於影響投資人獲利與否的關鍵類別"漲"及類別"跌"的平均預測效能上，本研究所提出的這三種分類模型亦同時具有良好的成效，可以做為投資人進行投資決策時的有效參考依據。 / Stocks' closing price levels can provide hints about investors' aggregate demands and aggregate supplies in the stock trading markets. If the level of a stock's closing price is higher than its previous closing price, it indicates that the aggregate demand is stronger than the aggregate supply in this trading day. Otherwise, the aggregate demand is weaker than the aggregate supply. It would be profitable if we can predict the individual stock's closing price level. For example, in case that one stock's current price is lower than its previous closing price. We can do the proper strategies(buy or sell) to gain profit if we can predict the stock's closing price level correctly in advance. In this thesis, we propose and evaluate three models for predicting individual stock's closing price in the Taiwan stock market. These models include a naïve Bayes model, a k-nearest neighbors model, and a hybrid model. Experimental results show the proposed methods perform better than the NewsCATS system for the "UP" and "DOWN" categories.

Avec l’électrification des moyens de transport, nous constatons ces dernières années une augmentation de l’utilisation de l’électronique de puissance et de la puissance mise en jeu dans les véhicules électriques ou hybrides (VEH). A cela s’ajoute une intégration de cette électronique dans des milieux de plus en plus compacts, a conduit à l’apparition de problèmes de la compatibilité électromagnétique (CEM) et d’exposition aux champs électromagnétiques. Pour réduire les effets indésirables des champs électromagnétiques, le blindage électromagnétique est l’une des solutions envisageables. Ces travaux de thèse portent essentiellement sur le blindage magnétique basse fréquence en champ proche des boîtiers contenant des équipements d’électroniques de puissance. Généralement, les boîtiers sont équipés d’ouvertures et de fentes ce qui conduit à une dégradation des performances du blindage magnétique. Nous nous intéressons dans ces travaux de thèse au développement de modèles permettant la prédiction de l’efficacité de blindage magnétique en tenant compte des effets de la diffusion, des ouvertures et des fentes. Ces différents modèles permettront aux concepteurs de maitriser les contraintes liées au blindage magnétique des dispositifs d’électroniques embarquées à bord des véhicules. L’aptitude ou la capacité des méthodes numériques à résoudre les problèmes de diffusion des champs magnétiques dans les tôles minces en présence et en absence d'ouvertures est présentée. Dans le cas de boîtiers munis de fentes de faibles dimensions, des approches basées sur la méthode des moments magnétiques sont développées pour la prédiction de l’efficacité de blindage magnétique. Des bancs de test ont été développés pour valider ces modèles. Dans le cas de fentes de dimensions quelconques, des approches dites "hybrides" associant des méthodes numériques et analytiques ont été développées et validées expérimentalement. Les approches développées ont été appliquées dans le cadre de l’étude d’une application industrielle. / The electrification of transport means, in recent years leads to an increase of the use of the power electronics and the power involved in electric or hybrid vehicles (HEV). With the integration of electronic devices in more compact environments, appear problems of the electromagnetic compatibility (EMC) and the electromagnetic field exposure. To reduce the effects of electromagnetic fields, electromagnetic shielding is one of the possible solutions.This thesis focus mainly on the magnetic shielding at low frequency in near field of enclosures containing power electronics. Usually, the enclosures are equipped with openings and slots which results in degraded performance of the magnetic shield. We develop models for predicting the magnetic shielding taking into account the effects of diffusion, openings and slots.These models will allow designers to control the magnetic shielding constraints of embedded electronic devices in vehicles. The ability or capability of numerical methods to solve the problems of the magnetic fields diffusion in thin sheets in the presence and absence of openings is presented. In the case of enclosures with slots of small dimensions's, magnetic moments approaches are developed for the prediction of magnetic shielding effectiveness. Test benches are developed to validate these models. In the general case, so-called “hybrid” approaches combining the analytical and numerical methods are developed and experimentally validated. The developed approaches are also applied in the framework of an industrial application.

As organizações desenvolveram recentemente novas práticas de gerenciamento de projetos, ditas ágeis e que convivem com as práticas anteriores, ditas direcionadas ao plano (Plan-driven). Ao invés de optar entre uma ou outra, vem ganhando força a ideia de combiná-las. Entretanto, a singularidade dos projetos torna difícil identificar a combinação de práticas mais apropriadas a cada caso. A presente pesquisa propõe uma ferramenta de configuração que relaciona as características e contexto de um projeto com práticas gerenciais, fundamentada em uma matriz morfológica e cujo propósito é apoiar a escolha de práticas e configuração de um modelo de gestão específico para o projeto. O estudo restringiu-se ao planejamento e controle do escopo e tempo em gerenciamento de projetos. A pesquisa iniciou com uma revisão bibliográfica sistemática para identificar ferramentas similares e modelos híbridos disponíveis na área de gerenciamento de projetos. Em seguida, compreendeu uma fase de proposição teórica da ferramenta e seu teste por meio de um estudo de caso em uma empresa de desenvolvimento de softwares, envolvendo desenvolvedores e gestores de projetos com experiência em gerenciamento ágil. A aplicação da ferramenta resultou em propostas diferentes para situações de projetos distintas. Uma análise das justificativas dos profissionais para as suas escolhas, mostrou que as experiências dos profissionais da organização, bem como suas preferências pessoais, influenciam as escolhas e não apenas o contexto do projeto. Há, portanto, indícios de que é viável optar por uma personalização para cada projeto, mas é importante investigar a experiência prévia e preferências dos profissionais de forma a aprimorar a ferramenta. A ferramenta proposta pode servir como referência para as empresas que desejam adequar modelos de gestão para seus diferentes tipos de projetos, combinando as práticas das diferentes abordagens de gerenciamento. O estudo também agrega ao desenvolvimento do tema de gestão hibrida na área de gerenciamento de projetos, trazendo uma inovação em relação as propostas encontradas na literatura. / Organizations have recently developed new project management practices, called agile and coexisting with previous practices, called Plan-driven. Instead of choosing between one and the other, the idea of combining them is gaining strength. However, the uniqueness of the projects makes it difficult to identify the most appropriate combination of practices to each case. The present research proposes a configuration tool that relates the characteristics and context of a project with management practices, based on a morphological matrix, and whose purpose is to support the choice of practices and configuration of a specific management model for the project. The study was limited to the planning and control of the scope and time in project management. The research began with a systematic literature review to identify similar tools and hybrid models available in the project management area. Then, it comprised a theoretical proposition phase of the tool and its test through the case study method in a software development company, involving developers and project managers with experience in agile management. The application of the tool resulted in different proposals for different project situations. An analysis of the professionals\' justifications for their choices showed that the experiences of the organization\'s professionals, as well as their personal preferences, influence the choices and not just the context of the project. There are, therefore, indications that it is feasible to choose a customization for each project, but it is important to investigate the previous experience and preferences of the professionals in order to improve the tool. The proposed tool can serve as a reference for companies that want to adapt management models to their different types of projects, combining the practices of the different management approaches. The study also adds to the development of hybrid management topic in the project management area, bringing an innovation in relation to the proposals found in the literature.

La thèse est consacrée à la modélisation discrète et continue des écoulements sanguins et des phénomènes connexes tels que la coagulation du sang et l'athérosclérose. Ce travail comprend l'élaboration des modèles mathématiques et numériques de la coagulation du sang, des simulations numériques et l'analyse mathématique d'un modèle d'inflammation chronique au cours d'athérosclérose. Une partie importante de la thèse est liée à la programmation, la mise en œuvre et l'optimisation des codes numériques. La partie principale de la thèse concerne la modélisation de la coagulation du sang in vivo tenant compte des écoulements sanguins, les réactions biochimiques dans le plasma et l'agrégation de plaquettes. La nouveauté principale de ce travail est l'élaboration d'un modèle hybride (discret-continu) de la coagulation du sang et de la formation de caillot sanguin dans le flux. La partie théorique de la thèse est consacrée à l'analyse mathématique d'un modèle d'inflammation chronique liée à l'athérosclérose. Les simulations numériques réalisées dans le cadre de cette thèse impliquent l'élaboration des algorithmes numériques pour les modèles mathématiques et le d´développement des logiciels. Vu le fait que les simulations numériques ont été coûteuse en temps de calcul, des efforts considérables ont été consacrés à la parallélisation des logiciels et à leur optimisation / The thesis is devoted to discrete and continuous modelling of blood flows and related phenomena such as blood coagulation and atherosclerosis. It includes the development of mathematical and numerical models of blood coagulation, numerical simulations and the mathematical analysis of a model problem of chronic inflammation during atherosclerosis. The main part of the thesis concerns modelling of blood coagulation in vivo which takes into account blood flows, biochemical reactions in plasma and platelet aggregation. The main novelty of this work is the development of a hybrid (discrete-continuous) model of blood coagulation and clot formation in flow. The model is used to study several aspects of blood coagulation in flow : platelet aggregation and its interaction with coagulation pathways, influence of the flow speed on the clot development, a possible mechanism by which clot stops growing. The theoretical part of the thesis is devoted to the mathematical analysis of a model of chronic inflammation related to atherosclerosis. In this thesis we study a model problem which describes the propagation of a reaction-diffusion wave in the 2D case with non-linear boundary conditions. For that we use the Leray-Schauder method and a priori estimates of solutions in order to prove the existence of waves in the bistable case. Numerical simulations carried out in the framework of this thesis were based on the numerical implementation of the corresponding models and on the software development. Since the numerical simulations were computationally expensive, a substantial effort was directed to software parallelization and optimization

As organizações desenvolveram recentemente novas práticas de gerenciamento de projetos, ditas ágeis e que convivem com as práticas anteriores, ditas direcionadas ao plano (Plan-driven). Ao invés de optar entre uma ou outra, vem ganhando força a ideia de combiná-las. Entretanto, a singularidade dos projetos torna difícil identificar a combinação de práticas mais apropriadas a cada caso. A presente pesquisa propõe uma ferramenta de configuração que relaciona as características e contexto de um projeto com práticas gerenciais, fundamentada em uma matriz morfológica e cujo propósito é apoiar a escolha de práticas e configuração de um modelo de gestão específico para o projeto. O estudo restringiu-se ao planejamento e controle do escopo e tempo em gerenciamento de projetos. A pesquisa iniciou com uma revisão bibliográfica sistemática para identificar ferramentas similares e modelos híbridos disponíveis na área de gerenciamento de projetos. Em seguida, compreendeu uma fase de proposição teórica da ferramenta e seu teste por meio de um estudo de caso em uma empresa de desenvolvimento de softwares, envolvendo desenvolvedores e gestores de projetos com experiência em gerenciamento ágil. A aplicação da ferramenta resultou em propostas diferentes para situações de projetos distintas. Uma análise das justificativas dos profissionais para as suas escolhas, mostrou que as experiências dos profissionais da organização, bem como suas preferências pessoais, influenciam as escolhas e não apenas o contexto do projeto. Há, portanto, indícios de que é viável optar por uma personalização para cada projeto, mas é importante investigar a experiência prévia e preferências dos profissionais de forma a aprimorar a ferramenta. A ferramenta proposta pode servir como referência para as empresas que desejam adequar modelos de gestão para seus diferentes tipos de projetos, combinando as práticas das diferentes abordagens de gerenciamento. O estudo também agrega ao desenvolvimento do tema de gestão hibrida na área de gerenciamento de projetos, trazendo uma inovação em relação as propostas encontradas na literatura. / Organizations have recently developed new project management practices, called agile and coexisting with previous practices, called Plan-driven. Instead of choosing between one and the other, the idea of combining them is gaining strength. However, the uniqueness of the projects makes it difficult to identify the most appropriate combination of practices to each case. The present research proposes a configuration tool that relates the characteristics and context of a project with management practices, based on a morphological matrix, and whose purpose is to support the choice of practices and configuration of a specific management model for the project. The study was limited to the planning and control of the scope and time in project management. The research began with a systematic literature review to identify similar tools and hybrid models available in the project management area. Then, it comprised a theoretical proposition phase of the tool and its test through the case study method in a software development company, involving developers and project managers with experience in agile management. The application of the tool resulted in different proposals for different project situations. An analysis of the professionals\' justifications for their choices showed that the experiences of the organization\'s professionals, as well as their personal preferences, influence the choices and not just the context of the project. There are, therefore, indications that it is feasible to choose a customization for each project, but it is important to investigate the previous experience and preferences of the professionals in order to improve the tool. The proposed tool can serve as a reference for companies that want to adapt management models to their different types of projects, combining the practices of the different management approaches. The study also adds to the development of hybrid management topic in the project management area, bringing an innovation in relation to the proposals found in the literature.

Product development has become essential in today’s technology driven marketplace. Currently, pointbased models such as the stage-gate model are applied in practice of several incumbent firms in the automotive industry. However, stage-gate models have been criticized for being too linear and plan focused as external pressures and internal complexity demand for faster and more flexible development, such as agile development. Although product development has a central role in the success of a firm, suppliers play a central role in helping firms achieve their product development goals. As a result, firms may undertake suppler development and integration initiatives to ensure quality and efficiency at the supplier. Utilizing supplier integration has shown positive effects on several performance outcomes. However, limited research on the effects of suppliers being integrated to deficient development practices has been published. Firms that integrate suppliers into point-based, documentation heavy processes could therefore potentially hinder internal development of flexible practices. Therefore, the aim of this study is to investigate how supplier integration challenges suppliers in adopting agile methods in product development and how these challenges can be addressed.  This study is based a qualitative, exploratory case study approach, including 13 interviews with various stakeholders within the case company together with extensive data from observations. The empirical data in combination with theories on agile development, traditional development, and supplier integration resulted in a proposition for adopting agile development approaches for integrated suppliers being locked-in to traditional development models. The findings indicate that integrated suppliers may face challenges in adopting agile methods due to requirements from the integrating firm on using traditional development methods. However, a possibility to utilize agile practices on the microperspective while retaining traditional development methods on the macro-perspective through a hybrid model enables adoption of agile methods for integrated suppliers. Although the possibility to adopt agile methods exist, the methods yield little benefits unless an agile leadership and vision is established. Therefore, a transition towards agile development also requires a transition in organizational culture to promote collaboration, distributed responsibility and collective intelligence. / Produktutveckling har blivit väsentlig på dagens teknikdrivna marknadsplats. För närvarande är traditionella modeller som stage-gate vanligt förekommande i flera etablerade företag inom bilindustrin, även om dessa modeller har kritiserats för att vara för linjära och planfokuserade när omgivningen och intern komplexitet kräver snabbare och mer flexibel utveckling. Även om produktutveckling har en fundamental roll i framgången hos ett företag så är leverantörer centrala i att hjälpa företag att uppnå sina produktutvecklingsmål. Som ett resultat av ökad outsourcing kan företag vidta mer utvecklings och integrationsinitiativ för att säkerställa kvalitet och effektivitet hos leverantören. Att använda leverantörsintegration har visat positiva effekter på flera nyckeltal. Emellertid har begränsad forskning om effekterna av att integrera leverantörer i ineffektiva produktutvecklingsmetoder publicerats. Företag som integrerar leverantörer i linjära, dokumentationstunga processer kan därför potentiellt hindra den interna utvecklingen av flexibla tillvägagångssätt. Därmed är syftet med denna studie att undersöka hur leverantörsintegration kan försvåra tillämpning av agila metoder i produktutveckling för leverantörer och hur dessa utmaningar kan adresseras. Denna studie bygger på en kvalitativ, explorativ fallstudie som inkluderar 13 intervjuer med anställda inom det observerade företaget tillsammans med omfattande data från observationer. Empiriska data i kombination med teorier om agil utveckling, traditionell utveckling och leverantörsintegration resulterade i ett förslag till integrerade leverantörer, som är bundna till traditionella utvecklingsmodeller, att adoptera agila utvecklingsmetoder. Resultaten tyder på att integrerade leverantörer kan möta utmaningar när det gäller att använda agila metoder på grund av krav att använda traditionella utvecklingsmetoder från det integrerande företaget. Där emot finns möjlighet att tillämpa agila metoder i mikroperspektivet samtidigt som traditionella utvecklingsmetoder bibehålls i makroperspektivet genom en hybridmodell. Även om möjligheten att adoptera agila metoder finns ger metoderna små fördelar utan adekvat agilt ledarskap och tankesätt. Därmed kräver en övergång till agil produktutveckling också en förändring i organisationskultur för att främja samarbete, distribuerat ansvar och kollektiv intelligens.