In this dissertation, research efforts mainly focused on exploring the applications of superparamagnetic iron oxide nanoparticles (SPIONs) in MR imaging and nanocatalysis via surface functionalization. A dopamine-based surface-functionalization strategy was established. The Simanek dendrons (G1 to G3), oligonucleotides and amino acids were loaded onto SPION surfaces via this approach to develop pH-sensitive MRI contrast agents, specific-DNA MR probes and a biomimetic hydrolysis catalyst. Dendron-SPION conjugates (G1 to G3) have good aqueous solubilities and high transverse relaxivities (>300 s-1*mM-1). They also showed interesting strong pH-sensitive R2 and R2* relaxivities, which were governed by the clustering states of dendron-SPIONs in different pH environments. Values of R2m and R2* m/R2m varied by over an order of magnitude around pH 5. The efficient cell-uptake (~3 million/cell) and low cytotoxicity of G1 to G3-SPIONs were demonstrated on HeLa cell cultures. The strong R2* effects were observed indicating the SPION clustering in HeLa cells. Two SPION-oligonuleotide conjugates were synthesized by coupling two half-match oligonucleotides onto domapine-capped SPIONs via SPDP linkers. They served as MR probes to detect a single-strand DNA with the same sequence to miRNA-21 based on the change of R2 values due to the DNA-bridged SPION clustering. The detection limit of the DNA could reach to 16.5 nM. A biomimetic hydrolysis nanocatalyst (i.e., Fe2O3-Asp-His complex) was developed by loading Asp and His-dopamine derivatives onto SPIONs. Paraoxon and nitrophenyl acetate were hydrolyzed under a mild condition (neutral pH, 37 °C) catalyzed by the Fe2O3-Asp-His complex. The two amino acids Asp and His cooperated with each other on the SPION surfaces to catalyze hydrolysis reactions. This catalyst could be recycled by a magnet and reused for four times without a significant loss of catalytic activity.

The problem of searching for objects of interest occurs in important applications ranging from rescue, security, transportation, to medicine. With the increasing use of autonomous vehicles as search platforms, there is a need for fast algorithms that can generate search plans for multiple agents in response to new information. In this dissertation, we develop new techniques for automated generation of search plans for different classes of search problems. First, we study the problem of searching for a stationary object in a discrete search space with multiple agents where each agent can access only a subset of the search space. In these problems, agents can fail to detect an object when inspecting a location. We show that when the probabilities of detection only depend on the locations, this problem can be reformulated as a minimum cost network optimization problem, and develop a fast specialized algorithm for the solution. We prove that our algorithm finds the optimal solution in finite time, and has worst-case computation performance that is faster than general minimum cost flow algorithms. We then generalize it to the case where the probabilities of detection depend on the agents and the locations, and propose a greedy algorithm that is 1/2-approximate. Second, we study the problem of searching for a moving object in a discrete search space with multiple agents where each agent can access only a subset of a discrete search space at any time and agents can fail to detect objects when searching a location at a given time. We provide necessary conditions for an optimal search plan, extending prior results in search theory. For the case where the probabilities of detection depend on the locations and the time periods, we develop a forward-backward iterative algorithm based on coordinate descent techniques to obtain solutions. To avoid local optimum, we derive a convex relaxation of the dynamic search problem and show this can be solved optimally using coordinate descent techniques. The solutions of the relaxed problem are used to provide random starting conditions for the iterative algorithm. We also address the problem where the probabilities of detection depend on the agents as well as the locations and the time periods, and show that a greedy-style algorithm is 1/2-approximate. Third, we study problems when multiple objects of interest being searched are physically scattered among locations on a graph and the agents are subject to motion constraints captured by the graph edges as well as budget constraints. We model such problem as an orienteering problem, when searching with a single agent, or a team orienteering problem, when searching with multiple agents. We develop novel real-time efficient algorithms for both problems. Fourth, we investigate classes of continuous-region multi-agent adaptive search problems as stochastic control problems with imperfect information. We allow the agent measurement errors to be either correlated or independent across agents. The structure of these problems, with objectives related to information entropy, allows for a complete characterization of the optimal strategies and the optimal cost. We derive a lower bound on the performance of the minimum mean-square error estimator, and provide upper bounds on the estimation error for special cases. For agents with independent errors, we show that the optimal sensing strategies can be obtained in terms of the solution of decoupled scalar convex optimization problems, followed by a joint region selection procedure. We further consider search of multiple objects and provide an explicit construction for adaptively determining the sensing actions.

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abstract: The spread of invasive species may be greatly affected by human responses to prior species spread, but models and estimation methods seldom explicitly consider human responses. I investigate the effects of management responses on estimates of invasive species spread rates. To do this, I create an agent-based simulation model of an insect invasion across a county-level citrus landscape. My model provides an approximation of a complex spatial environment while allowing the "truth" to be known. The modeled environment consists of citrus orchards with insect pests dispersing among them. Insects move across the simulation environment infesting orchards, while orchard managers respond by administering insecticide according to analyst-selected behavior profiles and management responses may depend on prior invasion states. Dispersal data is generated in each simulation and used to calculate spread rate via a set of estimators selected for their predominance in the empirical literature. Spread rate is a mechanistic, emergent phenomenon measured at the population level caused by a suite of latent biological, environmental, and anthropogenic. I test the effectiveness of orchard behavior profiles on invasion suppression and evaluate the robustness of the estimators given orchard responses. I find that allowing growers to use future expectations of spread in management decisions leads to reduced spread rates. Acting in a preventative manner by applying insecticide before insects are actually present, orchards are able to lower spread rates more than by reactive behavior alone. Spread rates are highly sensitive to spatial configuration. Spatial configuration is hardly a random process, consisting of many latent factors often not accounted for in spread rate estimation. Not considering these factors may lead to an omitted variables bias and skew estimation results. The ability of spread rate estimators to predict future spread varies considerably between estimators, and with spatial configuration, invader biological parameters, and orchard behavior profile. The model suggests that understanding the latent factors inherent to dispersal is important for selecting phenomenological models of spread and interpreting estimation results. This indicates a need for caution when evaluating spread. Although standard practice, current empirical estimators may both over- and underestimate spread rate in the simulation. / Dissertation/Thesis / M.S. Biology 2012

The aim of this paper is twofold: 1) to examine the determinants of residential energy expenditures and compare them on a regional level; and, 2) attempt to identify and measure the effect of possible principal-agent (PA) problems on residential energy efficiency in Austria. The results of this paper are partially based on findings from a master's thesis, which focused more directly on the PA problem. This paper expands on those results to include regional aspects in energy expenditures. A conditional demand model is regressed on a large number of variables representing housing characteristics, socioeconomic factors, occupancy type, and regional characteristics sourced from the EU Statistics on Income and Living Conditions dataset. The analysis indicates that significant regional differences exist in the determinants of residential energy expenditures and that PA problems appear to be an unimportant factor in energy efficiency in Austria, even at the regional level. The paper concludes with some possible explanations as to why this is the case.

Energy policy in the European Union (EU) is a patchwork of diverging interests and preferences. While the European Commission pushes for a common energy policy, Member States are responsible for their own separate energy policies. These divergences in interests and preferences might create a conflict situation between the Commission and Member States. This thesis explores the Commission’s behaviour vis-à-vis the Member States, investigating the conditions under which the Commission is likely to try to deviate from Member States’ preferences in the external dimension of the EU internal energy market. Adopting a Principal-Agent Model (PAM), this thesis conceptualizes the Member States as principals and the Commission as their agent. A qualitative case study approach and process-tracing method are applied to appreciate the variety of preferences of the actors involved, and provide a means to study the various shades of post-delegation agent’s behaviour. This thesis looks at four in-depth case studies: 1) Decision 994/2012 on establishing an information exchange mechanism with regard to intergovernmental agreements between Member States and third countries in the field of energy; 2) Directive 2009/73/EC on common rules for the internal market in gas; 3) Energy Community Treaty; and 4) Energy Charter Treaty. These were selected based on their relevance to the research question. Findings suggest that two factors are likely to affect the Commission’s deviation from Member States’ preferences: a) the preference alignment among the principals and b) the preference alignment between the principals and the agent. This thesis suggests that when the preferences between the agent and the principals are heterogeneous, the agent is more likely to deviate from the preferences of the principals. This thesis also suggests that the preference alignment among the principals only has a secondary effect on the agent’s deviation. Finally, this research contributes to the further development of the PAM offering a possible categorisation of post-delegation agent’s behaviour going beyond the dichotomy of deviation and non-deviation.

A inexistência de uma regra única que discipline a prevalência de norma constitucional federal sobre norma constitucional estadual, e vice-versa, no sistema federativo brasileiro, é tratada aqui enfocando tão somente os vínculos que dizem respeito aos agentes públicos. Embora as normas constitucionais federais devessem, em princípio, aterem-se à estrutura estatal, à divisão de competências e prerrogativas dos poderes, e às garantias individuais e sociais, os constituintes brasileiros preocuparam-se em regular matérias que recebem o status de constitucionais porque foram guindadas a tanto, seja pela força dos fatos sociais ou, até mesmo, por mero fisiologismo. Nesse terreno, mais próprio da legislação comum, é que está o epicentro das Emendas Constitucionais que têm reflexos nas Constituições Estaduais e em toda a legislação. Cuida-se aqui deste fenômeno e das suas conseqüências nas normas que regulam a Administração Pública e seus agentes. / The fact that there is not an unique rule about the predominance of the precept of the Federal Constitution over the State Constitution and vice versa in the brazilian federative system, is here treated focusing only the public agents relations. Although the federal constitutional rules should mainly deal with State structure, competence division and prerogatives, as well as individual and social garanties, brazilian constituent worried about themes that received the status of constitutional norms because they were turned to it, either by the social facts force, either by pure physiologism. In this field, peculiar to ordinary law, is situated the epicenter of the constitutional amendment that reflects in the State Constitutions and in all the ordinary law. Here this phenomenon is treated as well as its consequences in the laws that regulate Public Administration and its agents.

Competition has been considered as an effective means to improve business and economic competitiveness. However, competition in supply chain management (SCM) can be viewed as a source of uncertainty. Most recommended collaboration strategies in SCM literature tend to avoid the emergence of competition inside the supply chain, but, in reality, these strategies do not lead all supply chains to success. In addition, from strategic management perspective, these collaboration strategies are not believed to encourage firms to improve their performance. Both competition and collaboration are critical issues in achieving business success, but the effect of both factors on the market has not been explored concurrently in the literature. The complexity of this issue should be investigated using a comprehensive perspective, and it is hard to undertake by using an empirical approach.

In this paper we have introduced a concept of analyzing the performance of dynamic network organization (NO) in response to multiple input objectives of network organization (NO) and input set of unpredictable external environment, and we have correlated plasticity of NO with this performance measure. A three stage conceptual model of the process has been described which comprised of dynamic system of multi-agent network, multiple objectives and input goals of network driver (S n), unpredictable external environment (ϵ). Dynamic system of the network organization takes through its two phase and processes this based on time and system response variables. Processing of input information by the system variables gives a ratio of system response variable and input. Further analysis is based on the value of this performance values. This model suggests a performance measuring technique which takes an input set of objectives from network driver of the NO, a set of input from external environment (ϵ) state and processes this input based on the existing state of the dynamic system in the NO. This process output shows dynamic system performance in dynamic environment and how this performance coefficient correlates with the Plasticity of network organization. We have presented several examples, mathematical models and graph by analyzing true scenarios on UAV patrolling zone.

Cette thèse s'intéresse a l'apprentissage du comportement des avions dans le ciel. À partir de ces comportements l'objectif est de pouvoir générer du trafic aérien de manière autonome, légère et flexible pour alimenter une simulation. Les méthodes actuelles de simulation aériennes demandent beaucoup de préparation avant la simulation pour concevoir le scénario et d'interventions humaines pendant la simulation pour que le trafic aérien soit réaliste. Générer du trafic est une tâche complexe car le comportement des avions dépend de beaucoup de variables et des décisions de plusieurs d'acteurs : le contrôleur aérien décide de la trajectoire à suivre parmi toutes les possibilités qu'il perçoit, puis le pilote réagit plus ou moins rapidement de façon plus ou moins strict. Un système multi-agent adaptatif observe des trajectoires d'avions réelles pour apprendre comment les avions se comportent dans la réalité. Les différents agents impliquées coopèrent et modifient les liens qui les relient. Ce réseau entre les agents fini par représenter le comportement global de l'ensemble des avions et peut être interrogé par des agents avions en simulation pour savoir ce qu'ils doivent faire en fonction de leur situation courante. Nous présentons EVAA (Environnement Virtuel Auto-Adaptatif) capable d'apprendre le comportement des avions et de générer du trafic en fonction de ces comportements de manière totalement autonome. / This thesis is about learning the behavior of the aircrafts in the sky. With those behaviors the goal is to generate traffic in an autonomous and flexible way into a simulation. The current methods of air traffic simulation need to prepare the scenario before the simulation and the interventions of humans during the simulation to make the traffic realistic. Traffic generation is a complex task because the behaviors of the planes depends on many variables and several actors : the air traffic controller decide what trajectory to follow among many possibilities, then the pilot react , more or less promptly, to this order in a, more or less rigorous, way. An adaptive multi-agent system monitors trajectories of real aircrafts to learn how the planes behave in the real sky. The agents involved in this process cooperate and update the links between them to create a network representing the global behavior of all aircrafts. This network can then be queried by an aircraft agent in a simulation to know what it should do according to its current situation. We present EVAA (Self-Adaptive Virtual Environment) able to learn the behavior of aircrafts and to generate air traffic by using those behaviors in a autonomous way.