Animal behaviour is a state variable of the individual that deserves special attention given its determinant role in eco-evolutionary processes (Wolf et al. 2007 in Nature). The decomposition of the behavioural variation in between- and within-individual variability has revealed the existence of consistent between-individual differences referred to as personality or behavioural types (Dall et al. 2004 in Ecology Letters). Five axes of personality are usually recognized (exploration, aggressiveness, activity, sociability and boldness), and individual specificities along them tend to be correlated leading to what is known as behavioural syndromes. Recently, these patterns of covariation have been enlarged to accommodate movement behaviour within a personality-dependent spatial ecology theory (Spiegel et al. 2017 in Ecology Letters). Most animals tend to forage, reproduce and develop any activity within specific bounded space, which leads to the formation of home range (HR) areas (i.e., HR behaviour, Börger et al. 2008 in Ecology Letters). The increasing development of animal tracking technology is providing a huge amount of movement data revealing that HR behaviour is widespread among taxa and shows a large consistent variability, both at within- and between-individual level, which allows to define the existence of well-contrasted spatial behavioural types (SBTs). SBTs, as other personality traits, play an important role in selective processes as those impelled by harvesting activities. The Pace-of-Life-Syndrome (POLS) theory (Réale et al. 2010 in Philos. Trans. R. Soc. B Biol. Sci), hypothesises on how personality traits are expected to be correlated with life history (LH) traits along the fast-slow continuum (Stearns 1992 in Oxford Univ. Press) in the broadest sense. Accordingly, patterns of covariation between specific SBTs, physiology-related features and LHs would be expected to exist whenever they maximize the animal performance in a given environment. However, the way in which behavioural variation at the within-species level is translated to the wide range of LH traits remains a fundamental yet unresolved question, mainly due to the lack of a proper theoretical framework (Mathot & Frankenhuis, March 2018 in Behavioral Ecology and Sociobiology). Thus, unrevealing the mechanisms behind is certainly scientifically very exciting but also socially relevant. In such a context, this PhD thesis aimed to address from conceptual, empirical and theoretical perspectives cornerstone questions in behavioural ecology: what are the feasible mechanisms underpinning the establishment of HR areas and within-species variation, what are their consequences for animal functioning and performance (i.e., in. LH traits) at the individual and eco-evolutionary levels, or what are the implications for the assessment and conservation of wildlife of the existence of SBTs. The PhD thesis focusses in a fish heavily exploited by recreational fishers but it aims to provide general reasoning applicable to a wide range of wild animals. First, the PhD thesis proposes a mechanistic theory of personality-dependent movement behaviour based on dynamic energy budget models (i.e., a behavioural-bioenergetics theoretical model). Second, integrated in the field of animal personality (i.e., decomposition of behavioural variability into within- and between-individual’s components), it addresses empirically the study of behavioural variability in the main axis of personality for a marine fish species and looked for evidences of whether personality-mediated differences in energy acquisition may exist. Aiming to support empirically the possible connections between personality traits and space-use behaviour, the thesis provides some insights on the application of a novel-tracking algorithm to analyse the movement of individual fish submitted to different experimental conditions. Third, it provides two examples of how applying HR-related theoretical concepts may improve the management of natural resources: attending the properties of HR may facilitate the assessment of wildlife using fixed monitoring sampling stations, and considering SBTs may influence the assessment of the status of wild fish stocks. Finally, the adaptive value of the proposed behavioural-bioenergetics theory is explored by means of dynamic optimization to understand the eco-evolutionary consequences related with HR variability. In summary, this PhD thesis makes an important contribution to behavioural ecology by developing a unifying theory to test the generality and adaptive value of POLS based on dynamic energy budgets. This behavioural-bioenergetics model connects (1) personality traits (2) HR behaviour, (3) physiology and (4) LH traits through an interwoven of mass/energy fluxes, within which they interact and feedback with the ecological context. Overall, from an eco-evolutionary perspective, the proposed framework constitutes a powerful tool for exploring the ecological role of HR behaviour and predicting what combination of behavioural traits would be evolutionally favoured in a given ecological context. Moving forward to including managerial scenarios, this unifying theory provides scientifically founded knowledge that would promote to improve natural resource management by attending the behavioural component of animal populations.

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<p>Owing to the rise in fossil fuel prices, overall energy security concerns, and the current push towards green engineering; renewable and green fuels have seen an increase in interest in recent years. Two notable technologies in this green movement are the production of biodiesel from microalgae and the production of biogas from anaerobic digestion of waste biomass. Production of biodiesel from microalgae was studied extensively in the 80s through the early 90s and found to be economically infeasible given the technology of the time. However, recent literature has suggested that one possible method to improve the feasability of the process would be to combine it with an anaerobic digestor to provide nutrient and biomass recycling. For such a system, having accurate models of each process would be highly advantageous for optimal design and control. To this end this thesis moves towards this overall goal by examining and modelling the anaerobic digestion of the microalgae <em>Chlorella vulgaris</em>.</p> <p>Starting with a set of experimental data (anaerobic digestion of <em>Chlorella vulgaris</em>) provided by LBE-INRA, the minimum number of kinetic equations needed to predict the data are found using principal component style analysis. This number is found to be two to three reactions. Using this as a basis for model development, a mass balance model is developed around both two and three reaction cases. To date there is very little literature on the modelling of anaerobic digestion of microalgae and so kinetic laws are selected from the general anaerobic digestion models ``Anaerobic Digestor Model 1'' (ADM1) and ``Acidogenesis/Methanogenisis Model'' (AM2). Given that the kinetic laws were derived from general literature, model fitting is a must. To faciliate this process a novel systematic parameter identification procedure to locate identifiable parameter subsets within each model is presented. Applying this novel procedure to the provided data is seen to lead to promising identification results. Through these identification trials it is shown that the three reaction model best captures the dynamics of the system. This three reaction model serves as the basis for subsequent steady state optimality and sensitivity analysis. From these efforts it is shown that the predicted optimal curves match literature data very well but uncertainty in certain key parameter estimates lead to highly sensitive model predictions (and therefore low confidence). This leads to the conclusion that the developed model is capable of predicting the kinetics of <em>Chlorella</em> digestion but additional trials are needed to further refine the model fitting results.</p> <p>Coupling an anaerobic digester to a microalgal culture is currently considered one of the most promising avenues towards the production of renewable bioenergy, either in the form of biodiesel or biogas. Accurate mathematical models are crucial tools to assess the potential of such coupled biotechnological processes and help optimize their design, operation and control. This paper focuses on the compartment of anaerobic digestion of microalgae. Using experimental data for the anaerobic digestion of <em>Chlorella vulgaris</em>, a grey-box model is developed that allows good prediction capabilities and retains low complexity. The proposed methodology proceeds in two steps, namely a structural and a parametric identification steps. The fitted model is then used to conduct preliminary optimization for the production of biogas from <em>Chlorella vulgaris</em>. The results provide some insight into the potential for bioenergy production from the digestion of microalgae and, more generally, the coupled process.</p> / Master of Applied Science (MASc)

<p dir="ltr">Managing transient heat loads has become more challenging with the increasing electrification of ground, air, and marine vehicles. Doing so requires novel designs of thermal management systems, or in some cases, novel retrofits of legacy TMSs to accommodate the addition of more electrified subsystems. However, design tools that are well suited for examining and optimizing the dynamic response of TMS over candidate operation or mission profiles are limited. In this thesis, a principled methodology and associated tools for the enumeration and dynamic optimization of all feasible architectures of an air cycle machine are presented. Graph-based modeling is pivotal for exploring and optimizing ACM architectures, providing a structured representation of system components and interactions. By modeling the ACM as a graph, with vertices and edges representing components and interactions, respectively, various component configurations and performance metrics can be systematically analyzed. This approach enables efficient exploration of design alternatives and consideration of dynamic boundary conditions (representing, for example, a complex mission profile) during optimization. Another unique contribution of this thesis is a novel application of a multi-state graph-based modeling approach for developing dynamic models of turbomachinery components. By representing multiple states within each control volume or component and connecting them through power flows, this approach accurately captures both first and second law dynamics, enabling the computation of dynamic entropy generation rates. A detailed case study demonstrates the optimization of ACM architectures based on entropy generation minimization and dynamic bleed air flow rate minimization. This study highlights the trade-offs between different optimization criteria and the potential for generalizing the tool to more complex thermofluid systems in thermal management applications. The results underscore the importance of entropy-based analysis in comparing the thermodynamic losses across various system architectures.</p>

臺灣現有移轉計價之規範未有明確的罰則(Penalty)，因而衍生許多稅負規避的問題。本文採用動態最適化(Dynamic Optimization)的模型來觀察跨國企業移轉計價的行為，在面臨懲罰與兩國稅差時企業會如何利用移轉價格及數量來進行獲利移轉以規避稅負，進而分析政府調降稅率以降低稅差並吸引獲利移轉的稅率政策對企業移轉計價的影響，最後再探討罰則在法規制定上的必要性。結果顯示，預料到的稅率政策在長期能有效減少企業從事移轉價格操弄(Transfer Price Manipulation)，但在短期﹝除了宣告那一刻之外﹞反而更助長移轉價格操弄的發生，特別是當政策宣告至執行之期間過長時更為嚴重。此外，由先前的文獻可知無罰則下的最適移轉價格為一邊界解(Boundary Solution)，本文也證明了此邊界解亦可能出現於有罰則的情況下。然而，罰則的存在創造了內部解(Interior Solution)的可能性，此內部解較邊界解更趨近於常規交易價格，因此我們仍建議政府制定罰則。 / This paper employs a dynamic optimization model to determine the equilibrium price and quantity in a multinational firm (MNF) faced with a threat of a penalty. We analyze the impact on transfer pricing that arises from the unanticipated and anticipated permanent taxation policy of home country and host country. Anticipated taxation policy for reducing tax differentials can reduce transfer price manipulation in the long term. However, except for the moment of announcement, such reduction of transfer price manipulation does not occur in the short term, especially in the case of a large time lag of policy. We also show that the boundary solution is possible even though transfer price penalty exists and suggest that governments impose penalty which creates the possibility of interior solution.

Cette thèse s'intéresse au développement de stratégies de décalage de charge pouvant être appliquées à un parc de chauffe-eau Joule (CEJ).On propose une modélisation entrée-sortie du système que constitue le CEJ. L'idée est de concevoir un modèle précis et peu coûteux numériquement, qui pourrait être intégré dans un CEJ intelligent. On présente notamment un modèle phénoménologique multi-période d'évolution du profil de température dans le CEJ ainsi qu'un modèle de la demande en eau chaude. On étudie des stratégies d'optimisation pour un parc de CEJ dont la résistance peut être pilotée par un gestionnaire central. Trois cas de figures sont étudiés. Le premier concerne un petit nombre de ballons intelligents et présente une méthode de résolution d'un problème d'optimisation en temps discret. Puis, on s'intéresse à un parc de taille moyenne. Une heuristique gardant indivisibles les périodes de chauffe (pour minimiser les aléas thermo-hydrauliques) est présentée. Enfin, un modèle de comportement d'un nombre infini de ballon est présenté sous la forme d'une équation de Fokker-Planck. / This thesis focuses on the development of advanced strategies for load shifting of large groups of electric hot water tanks (EHWT).The first part of this thesis is dedicated to representing an EHWT as an input-output system. The idea is to design a simple, tractable and relatively accurate model that can be implemented inside a low-power computing unit embedded in a smart EHWT, for practical applications of optimization strategies. It includes in particular a phenomenological multi-period model of the temperature profile in the tank and a realistic domestic hot water consumption model.The second part focuses on the design of optimal control strategies for a group of tanks. Three use-cases are studied. The first one deals with a small number of smart and controllable EHWT for which we propose a discrete-time optimal resolution method. The second use-case adresses a medium-scale group of controllable tanks and proposes a heuristic which keeps the heating period undivided to minimize thermo-hydraulic hazards. Finally, we present the modelling of the behavior of a infinite population of tanks under the form of a Fokker-Planck equation.

Cette thèse porte sur l'optimisation et la commande prédictive des centrales de production d'énergie en utilisant des modèles physiques des installations. Les modèles sont réalisés à l'aide du langage Modelica, un langage équationnel adapté à la modélisation de systèmes multi-physiques. La modélisation de systèmes physiques dans ce langage est présentée dans une première partie, ainsi que les traitements symboliques réalisés par les compilateurs Modelica pour mettre les modèles sous une forme adaptée à l'optimisation. On présente dans une seconde partie le développement d'une méthode d'optimisation dynamique hybride pour les centrales de production d'énergie, qui fournit une trajectoire optimisée de l'installation sur un horizon long. Les trajectoires calculées incluent les trajectoires des commandes continues ainsi que les décisions d'engagement des différents équipements. L'algorithme d'optimisation combine la méthode de collocation et une méthode nommée Sum Up Rounding (SUR) pour la prise en compte des décisions d'engagement. Un algorithme de commande prédictive (MPC) est enfin introduit afin de garantir le suivi des trajectoires optimales et de prendre en compte en temps réel la présence de perturbations et les erreurs du modèle d'optimisation. L'algorithme MPC utilise des modèles linéarisés tangents générés automatiquement à partir du modèle non linéaire. / This thesis deals with hybrid optimal control and Model Predictive Control (MPC) of power plants by use of physical models. Models of the facilities are developped with Modelica, an equation based language tailored for modelling multi-physics systems. Modeling of physical systems with Modelica is introduced in a first part, as well as some of the symbolic processing done by Modelica compilers that transform the original model to a form suited for optimization. Then, a method to solve optimal control problems on hybrid systems (such as power plants) is presented. This methods provides an optimal trajectory for the power plant on a long horizon. The optimal trajectory computed by the method includes the trajectories of continuous inputs as well as switching decisions for components in the plant. The optimization algorithm combines the collocation method and a method named Sum Up Rounding (SUR) for dealing with switches. Finally, a Model Predictive Controller is developped in order to follow this optimal trajectory in real time, and to cope with disturbances on the actual system and modelling errors. The proposed MPC uses tangent linear models of the plant that are derived automatically from the nonlinear model.

As an alternative renewable source for bioenergy, forest biomass has recently drawn more attention from the U.S. government and the general public. Woody biomass policies have been adopted to encourage the new bioenergy industry. A variety of state policy incentives attempt to create a desirable legal climate and lure new firms, imposing two important questions regarding state government policies and the sustainable use of forest resources. This dissertation sheds some light on these questions. The first paper constructs a woody biomass policy index through scoring each statute and weighting different categories of policies from the vantage point of renewable energy investment. It analyzes the disparity in the strength of state government incentives in the woody biomass utilization. The second paper employs a conditional logit model (CLM) to explore the effects of woody biomass policies on the siting decisions of new bioenergy projects. In addition, significant state attributes influencing the births of new bioenergy firms are identified such as resource availability, business tax climate, delivered pulpwood price, and the average wage rate. The third paper uses the Sub-Regional Timber Supply (SRTS) model to examine the regional aggregate forest biomass feedstock potential in Tennessee and to predict the impacts of additional pulpwood demand on the regional roundwood market through 2030. The fourth paper includes the benefits of thinning and logging residues in a dynamic optimization model to analyze how bioenergy policies will impact forest stock, harvest levels, optimal rotation, and silvicultural effort. The results may have substantial implications regarding woody biomass policies, the creation of a new bioenergy industry, and sustainable forest resource management. A lucrative state woody biomass policy support and tax climate can attract new bioenergy businesses. States endowed with abundant forest resources may choose to provide strong tax incentives to spur the birth of new plants. However, overuse of forest biomass can impact roundwood markets and traditional wood processing industries. How government incentives will affect the sustainability of natural resources can be diverse. These findings offer constructive insights in the enactment and implementation of new woody biomass legislation.

Microsoft Research / Although runtime systems and the dynamic compilation model have revolutionized the process of application development and deployment, the associated performance overheads continue to be a cause for concern and much research. In the first part of this thesis, we describe the design and implementation of an adaptive recompilation framework for Rotor, a shared source implementation of the Common Language Infrastructure (CLI) that can increase program performance through intelligent recompilation decisions and optimizations based on the program's past behavior. Our extensions to Rotor include a low overhead runtime-stack based sampling profiler that identifies program hotspots. A recompilation controller oversees the recompilation process and generates recompilation requests. At the first-level of a multi-level optimizing compiler, code in the intermediate language is converted to an internal intermediate representation and optimized using a set of simple transformations. The compiler uses a fast yet effective linear scan algorithm for register allocation. Hot methods can be instrumented in order to collect basic-block, edge and call-graph profile information. Profile-guided optimizations driven by online profile information are used to further optimize heavily executed methods at the second level of recompilation. An evaluation of the framework using a set of test programs shows that performance can improve by a maximum of 42.3% and by 9% on average. Our results also show that the overheads of collecting accurate profile information through instrumentation to an extent outweigh the benefits of profile-guided optimizations in our implementation, suggesting the need for implementing techniques that can reduce such overheads. A flexible and extensible framework design implies that additional profiling and optimization techniques can be easily incorporated to further improve performance. As previously stated, fine-grained and accurate profile information must be available at low cost for advanced profile-guided optimizations to be effective in online environments. In this second part of this thesis, we propose a generic framework that makes it possible for instrumentation based profilers to collect profile data efficiently, a task that has traditionally been associated with high overheads. The essence of the scheme is to make the underlying hardware aware of instrumentation using a special set of profile instructions and tuned microarchitecture. This not only allows the hardware to provide the runtime with mechanisms to control the profiling activity, but also makes it possible for the hardware itself to optimize the process of profiling in a manner transparent to the runtime. We propose selective instruction dispatch as one possible controlling mechanism that can be used by the runtime to manage the execution of profile instructions and keep profiling overheads under check. We propose profile flag prediction, a hardware optimization that complements the selective dispatch mechanism by not fetching profile instructions when the runtime has turned profiling off. The framework is light-weight and flexible. It eliminates the need for expensive book-keeping, recompilation or code duplication. Our simulations with benchmarks from the SPEC CPU2000 suite show that overheads for call-graph and basic block profiling can be reduced by 72.7% and 52.4% respectively with a negligible loss in accuracy.