Software-intensive systems are increasingly expected to operate under changing and uncertain conditions, including not only varying user needs and workloads, but also fluctuating resource capacity. Self-adaptation is an approach that aims to address this problem, giving systems the ability to change their behavior and structure to adapt to changes in themselves and their operating environment without human intervention. Self-adaptive systems tend to be reactive and myopic, adapting in response to changes without anticipating what the subsequent adaptation needs will be. Adapting reactively can result in inefficiencies due to the system performing a suboptimal sequence of adaptations. Furthermore, some adaptation tactics—atomic adaptation actions that leave the system in a consistent state—have latency and take some time to produce their effect. In that case, reactive adaptation causes the system to lag behind environment changes. What is worse, a long running adaptation action may prevent the system from performing other adaptations until it completes, further limiting its ability to effectively deal with the environment changes. To address these limitations and improve the effectiveness of self-adaptation, we present proactive latency-aware adaptation, an approach that considers the timing of adaptation (i) leveraging predictions of the near future state of the environment to adapt proactively; (ii) considering the latency of adaptation tactics when deciding how to adapt; and (iii) executing tactics concurrently. We have developed three different solution approaches embodying these principles. One is based on probabilistic model checking, making it inherently able to deal with the stochastic behavior of the environment, and guaranteeing optimal adaptation choices over a finite decision horizon. The second approach uses stochastic dynamic programming to make adaptation decisions, and thanks to performing part of the computations required to make those decisions off-line, it achieves a speedup of an order of magnitude over the first solution approach without compromising optimality. A third solution approach makes adaptation decisions based on repertoires of adaptation strategies— predefined compositions of adaptation tactics. This approach is more scalable than the other two because the solution space is smaller, allowing an adaptive system to reap some of the benefits of proactive latency-aware adaptation even if the number of ways in which it could adapt is too large for the other approaches to consider all these possibilities. We evaluate the approach using two different classes of systems with different adaptation goals, and different repertoires of adaptation strategies. One of them is a web system, with the adaptation goal of utility maximization. The other is a cyberphysical system operating in a hostile environment. In that system, self-adaptation must not only maximize the reward gained, but also keep the probability of surviving a mission above a threshold. In both cases, our results show that proactive latency-aware adaptation improves the effectiveness of self-adaptation with respect to reactive time-agnostic adaptation.

Nesta tese investigamos modelos irreversíveis dentro do contexto da mecânica estatística de não-equilíbrio motivados por alguns problemas de dinâmicas de populações biológicas. Procuramos identificar a existência de transições de fase e as classes de universalidade às quais os modelos pertencem. Além disso, buscamos modelos que capturem as principais características dos sistemas biológicos que procuramos descrever. Encontramos a solução analítica exata para o modelo suscetível-infectado-recuperado (SIR) em uma rede unidimensional. Investigamos o modelo suscetível-infectado-recuperado com infecção recorrente. Mostramos que o modelo pertence à classe de universalidade da percolação isotrópica, salvo pelos parâmetros em que se torna o processo de contato. Obtivemos também a linha de transição entre as fases em que há e não há propagação da epidemia, através de aproximações de campo médio e por simulações de Monte Carlo do modelo na rede quadrada. Investigamos uma dinâmica para duas espécies biológicas e dois nichos ecológicos; para tanto introduzimos um modelo estocástico irreversível de quatro estados. Concluímos que o modelo oferece uma descrição para as oscilações temporais das populações das espécies e para a alternância de dominância entre estas. Para chegar a esta conclusão, utilizamos simulações de Monte Carlo do modelo na rede quadrada, aproximações de campo médio e a abordagem da equação mestra de nascimento e morte, a qual, para grandes populações, pode ser aproximada por uma equação de Fokker-Planck que é associada a um conjunto de equações de Langevin. Por fim, usando simulações de Monte Carlo, analisamos a dinâmica de duas espécies biológicas e dois nichos ecológicos incluindo difusão. Novamente verificamos que o modelo gera cenários com oscilações temporais das populações das espécies e alternância de dominância entre estas. Ademais, concluímos que modelo pertence à classe de universalidade da percolação direcionada e obtivemos o diagrama de fase. / In this thesis we investigate irreversible models within the context of nonequilibrium statistical mechanics motivated by some problems of biological population dynamics. We look for dentifying the existence of phase transition and the universality classes to which the models belong. In addition to that, we look for models that capture the main characteristics of the biological systems which we are interested in describing. We found the exact analytic solution of the susceptible-infected-recovered (SIR) model on one-dimensional lattice. We investigated the susceptible-infected-recovered model with recurrent infection. We showed that the model belongs to the isotropic percolation universality class, except for the parameters that make the model become a contact process. We obtained the transition line between the phases in which there is propagation of the epidemic and in which there is not, by means of mean-field approximations and Monte Carlo simulations on a square lattice. Furthermore, we investigated a dynamic for two biological species and ecological niches; for this purpose we introduced an irreversible stochastic model with four states. We conclude that the modoffers a description of time oscillations of the species populations and of the alternating dominance between them. To achieve this conclusion we used Monte Carlo simulations of this model on a square lattice, mean-field approximation, and the birth and death master equation approach, which for large populations can be approximated by a Fokker-Planck equation that is associated to a set of Langevin equations. Finally, using Monte Carlo simulations, we analyzed a dynamic for two biological species and ecological niches including diffusion. Again, we verified that the model generates scenarios with time oscillations of the species populations and with alternating dominance between them. Also, we conclude that the model belongs to the directed percolation universality class and we found the phase diagram.

A ubiquitous problem for all foragers is the trade-off between acquiring food energy while simultaneously avoiding the risk of predation. In central montane Alberta I modelled how ungulate forage changes with succession within cutblocks and the implications for forage availability to ungulates under current harvest regimes. Because cutblocks are discrete patches in space, I developed a dynamic state variable model for an ungulate to explore under what conditions an individual forager could (1) behaviourally avoid predation within a patch through inactivity, (2) overcome patch isolation when confronted with predation during transit between patches, and (3) alter patch use across a home range to optimize fitness. The model includes the requirement to process forage into energy through rumination behaviour that constrains foraging, and compares outcomes under a time-minimizing (sigmoid) and energy maximizing (linear) fitness functions. When an ungulate is in high energetic state, inactivity provides an effective behavioural refuge, or animals prioritize safety over energy gain, individuals avoid predation within patches reducing the need to move between patches. When energy acquisition is prioritized, individuals are at a low energetic state, or within patch anti-predator behaviours are ineffective, individuals move among patches to avoid predators in space, and configuration of the patches influences fitness. When model results were qualitatively compared to activity patterns and cutblock use of female, GPS-collared elk appear to follow a time minimizing strategy in their patch use across the home range and with their activity within riskier patches. I discuss the implications of these findings for the management of elk and cutblocks in west central Alberta.

Power converters are used in many distributed energy resources (DER) applications. With proper controls, DER systems can reduce losses and achieve higher energy efficiency if various power sources and loads are integrated through DC bus. High voltage electric vehicle (EV) DC charging station is becoming popular in order to reduce charging time and improve energy efficiency. A DC EV charging station model involving photovoltaic, energy storage system (ESS), fuel cell and DC loads is studied in this work. A dynamic programming technique that considers various uncertainties involved in the system is adopted to obtain optimal dispatch of ESS and fuel cell system. The effects of different tariffs, demand response programs and contract capacities of demand in the power scheduling are investigated and the results are presented.

No description available.

Over the last 20 years, electricity markets around the world have gradually been deregulated, creating wholesale markets in which generating companies compete for the right to supply electricity, through an offering system. This thesis considers the optimisation of the offering process from the perspective of an individual generator, subject to intertemporal constraints including fuel limitations, correlated rest-of-market behaviour patterns and unit operational decisions. Contributions from the thesis include a Pre-Processing scheme that results in considerable computational benefits for a two-level Dynamic Programming method, in addition to the development of a new process that combines the techniques of Decision Analysis and Dynamic Programming.

The Energy Management Strategy (EMS) has a huge effect on the performance of any hybrid vehicle because it determines the operating point of almost every component associated with the powertrain. This means that its optimisation is an incredibly complex task which must consider a number of objectives including the fuel consumption, drive-ability, component degradation and straight-line performance. The EMS is of particular importance for Fuel Cell Hybrid Electric Vehicles (FCHEVs), not only to minimise the fuel consumption, but also to reduce the electrical stress on the fuel cell and maximise its useful lifetime. This is because the durability and cost of the fuel cell stack is one of the major obstacles preventing FCHEVs from being competitive with conventional vehicles. In this work, a novel EMS is developed, specifcally for Fuel Cell Hybrid Electric Vehicles (FCHEVs), which considers not only the fuel consumption, but also the degradation of the fuel cell in order to optimise the overall running cost of the vehicle. This work is believed to be the first of its kind to quantify effect of decisions made by the EMS on the fuel cell degradation, inclusive of multiple causes of voltage degradation. The performance of this new strategy is compared in simulation to a recent strategy from the literature designed solely to optimise the fuel consumption. It is found that the inclusion of the degradation metrics results in a 20% increase in fuel cell lifetime for only a 3.7% increase in the fuel consumption, meaning that the overall running cost is reduced by 9%. In addition to direct implementation on board a vehicle, this technique for optimising the degradation alongside the fuel consumption also allows alternative vehicle designs to be compared in an unbiased way. In order to demonstrate this, the novel optimisation technique is subsequently used to compare alternative system designs in order to identify the optimal economic sizing of the fuel cell and battery pack. It is found that the overall running cost can be minimised by using the smallest possible fuel cell stack that will satisfy the average power requirement of the duty cycle, and by using an oversized battery pack to maximise the fuel cell effciency and minimise the transient loading on the stack. This research was undertaken at Loughborough University as part of the Doctoral Training Centre (DTC) in Hydrogen, Fuel Cells and Their Applications in collaboration with the University of Birmingham and Nottingham University and with sponsorship from HORIBA-MIRA (Nuneaton, UK). A Microcab H4 test vehicle has been made available for use in testing for this research which was previously used for approximately 2 years at the University of Birmingham. The Microcab H4 is a small campus based vehicle designed for passenger transport and mail delivery at low speeds as seen on a university campus. It has a top speed of approximately 30mph, and is fitted with a 1.2kW fuel cell and a 2kWh battery pack.

Nesta tese investigamos modelos irreversíveis dentro do contexto da mecânica estatística de não-equilíbrio motivados por alguns problemas de dinâmicas de populações biológicas. Procuramos identificar a existência de transições de fase e as classes de universalidade às quais os modelos pertencem. Além disso, buscamos modelos que capturem as principais características dos sistemas biológicos que procuramos descrever. Encontramos a solução analítica exata para o modelo suscetível-infectado-recuperado (SIR) em uma rede unidimensional. Investigamos o modelo suscetível-infectado-recuperado com infecção recorrente. Mostramos que o modelo pertence à classe de universalidade da percolação isotrópica, salvo pelos parâmetros em que se torna o processo de contato. Obtivemos também a linha de transição entre as fases em que há e não há propagação da epidemia, através de aproximações de campo médio e por simulações de Monte Carlo do modelo na rede quadrada. Investigamos uma dinâmica para duas espécies biológicas e dois nichos ecológicos; para tanto introduzimos um modelo estocástico irreversível de quatro estados. Concluímos que o modelo oferece uma descrição para as oscilações temporais das populações das espécies e para a alternância de dominância entre estas. Para chegar a esta conclusão, utilizamos simulações de Monte Carlo do modelo na rede quadrada, aproximações de campo médio e a abordagem da equação mestra de nascimento e morte, a qual, para grandes populações, pode ser aproximada por uma equação de Fokker-Planck que é associada a um conjunto de equações de Langevin. Por fim, usando simulações de Monte Carlo, analisamos a dinâmica de duas espécies biológicas e dois nichos ecológicos incluindo difusão. Novamente verificamos que o modelo gera cenários com oscilações temporais das populações das espécies e alternância de dominância entre estas. Ademais, concluímos que modelo pertence à classe de universalidade da percolação direcionada e obtivemos o diagrama de fase. / In this thesis we investigate irreversible models within the context of nonequilibrium statistical mechanics motivated by some problems of biological population dynamics. We look for dentifying the existence of phase transition and the universality classes to which the models belong. In addition to that, we look for models that capture the main characteristics of the biological systems which we are interested in describing. We found the exact analytic solution of the susceptible-infected-recovered (SIR) model on one-dimensional lattice. We investigated the susceptible-infected-recovered model with recurrent infection. We showed that the model belongs to the isotropic percolation universality class, except for the parameters that make the model become a contact process. We obtained the transition line between the phases in which there is propagation of the epidemic and in which there is not, by means of mean-field approximations and Monte Carlo simulations on a square lattice. Furthermore, we investigated a dynamic for two biological species and ecological niches; for this purpose we introduced an irreversible stochastic model with four states. We conclude that the modoffers a description of time oscillations of the species populations and of the alternating dominance between them. To achieve this conclusion we used Monte Carlo simulations of this model on a square lattice, mean-field approximation, and the birth and death master equation approach, which for large populations can be approximated by a Fokker-Planck equation that is associated to a set of Langevin equations. Finally, using Monte Carlo simulations, we analyzed a dynamic for two biological species and ecological niches including diffusion. Again, we verified that the model generates scenarios with time oscillations of the species populations and with alternating dominance between them. Also, we conclude that the model belongs to the directed percolation universality class and we found the phase diagram.

In a time where people tend to retire earlier and live longer in combination with an augmented personal responsibility of allocating or at least choosing adequately composed pension funds, the importance of a deeper understanding of long term investment strategies is inevitably accentuated. On the background of discrepancies in suggested pension fund strategies by influential fund providers, professional advisers and previous literature, this thesis aims at addressing foremost one particular research question: How should an investor optimally allocate between risky and risk-less assets in a pension fund depending on age? In order to answer the question the sum of Human wealth, defined as the present value of all expected future incomes, and ordinary Financial wealth is maximized by applying a mean-variance and a expected utility approach. The latter, and mathematically more sound method yields a strategy suggesting 100% of available capital to be invested in risky assets until the age of 47 whereafter the portion should be gradually reduced and reach the level of 32% at the last period before retirement. The strategy is clearly favorable to solely holding a risk-free asset and it just outperforms the commonly applied "100 minus age"-strategy.

Adaptive clinical trials for new drugs or treatment options promise substantial benefits to both the pharmaceutical industry and the patients, but complicate resource allocation decisions. In this dissertation, we focus on sequential adaptive clinical trials with binary response, which allow for early termination of drug testing for benefit or futility at interim analysis points. The option to stop the trial early enables the trial sponsor to mitigate investment risks on ineffective drugs, and to shorten the development time line of effective drugs, hence reducing expenditures and expediting patient access to these new therapies. In this setting, decision makers need to determine a testing schedule, or the number of patients to recruit at each interim analysis point, and stopping criteria that inform their decision to continue or stop the trial, considering performance measures that include drug misclassification risk, time-to-market, and expected profit. In the first manuscript, we model current practices of sequential adaptive trials, so as to quantify the magnitude of drug misclassification risk. Towards this end, we build a simulation model to realistically represent the current decision-making process, including the utilization of the triangular test, a widely implemented sequential methodology. We find that current practices lead to a high risk of incorrectly terminating the development of an effective drug, thus, to unrecoverable expenses for the sponsor, and unfulfilled patient needs. In the second manuscript, we study the sequential resource allocation decision, in terms of a testing schedule and stopping criteria, so as to quantify the impact of interim analyses on the aforementioned performance measures. Towards this end, we build a stochastic dynamic programming model, integrated with a Bayesian learning framework for updating the drug’s estimated efficacy. The resource allocation decision is characterized by endogenous uncertainty, and a trade-off between the incentive to establish that the drug is effective early on (exploitation), due to a time-decreasing market revenue, and the benefit from collecting some information on the drug’s efficacy prior to committing a large budget (exploration). We derive important structural properties of an optimal resource allocation strategy and perform a numerical study based on realistic data, and show that sequential adaptive trials with interim analyses substantially outperform traditional trials. Finally, the third manuscript integrates the first two models, and studies the benefits of an optimal resource allocation decision over current practices. Our findings indicate that our optimal testing schedules outperform different types of fixed testing schedules under both perfect and imperfect information. / Ph. D. / Adaptive clinical trials for new drugs or treatment options have the potential to reduce pharmaceutical research and development costs, and to expedite patient access to new therapies. Sequential adaptive clinical trials allow investigators and trial sponsors to terminate drug testing “early,” at interim analysis points, either for benefit or futility reasons. In the first manuscript, we model current practices of sequential adaptive trials, so as to quantify the risk of terminating the development of an effective drug incorrectly. Towards this end, we build a simulation model to realistically represent the current decision-making process. In the second manuscript, we study the financial investment decisions made by the trial sponsor, such as pharmaceutical firms, so as to quantify the impact of interim analyses on a series of performance measures relevant to the firm and the patients. Towards this end, we build a a mathematical optimization model that incorporates elements representing the knowledge gained by decision makers on the drug’s efficacy, which is unknown to them at the beginning of the trial. As a result of our analysis, we obtain an optimal strategy to allocate financial resources in a sequential adaptive trial. In the third and final manuscript, we compare the performance of our optimal resource allocation strategy against the performance of the triangular test, a well-known and widely implemented sequential testing methodology, as measured by the aforementioned performance measures.