This thesis investigates logical formalizations of Castelfranchi and Falcone's (C&F) theory of trust [9, 10, 11, 12]. The C&F theory of trust defines trust as an essentially mental notion, making the theory particularly well suited for formalizations in multi-modal logics of beliefs, goals, intentions, actions, and time. Three different multi-modal logical formalisms intended for multi-agent systems are compared and evaluated along two lines of inquiry. First, I propose formal definitions of key concepts of the C&F theory of trust and prove some important properties of these definitions. The proven properties are then compared to the informal characterisation of the C&F theory. Second, the logics are used to formalize a case study involving an Internet forum, and their performances in the case study constitute grounds for a comparison. The comparison indicates that an accurate modelling of time, and the interaction of time and goals in particular, is integral for formal reasoning about trust. Finally, I propose a Horn fragment of the logic of Herzig, Lorini, Hubner, and Vercouter [25]. The Horn fragment is shown to be too restrictive to accurately express the considered case study.

Temporal and spatial molecular imaging of disease biomarkers using non-invasive MRI with high resolution is largely limited by lack of MRI contrast agents with high sensitivity, high specificity, optimized biodistribution and pharmacokinetics. In this dissertation, I report my Ph. D. work on the development of protein-based MRI contrast agents (ProCAs) specifically targeting different cancer biomarkers, such as grastrin-releasing peptide receptor (GRPR), prostate specific membrane antigen (PSMA), and vascular endothelial growth factor receptor-2 (VEGFR-2). Similar to non-targeted ProCAs, these biomarker-targeted ProCAs exhibit 5 - 10 times higher r1 and r2 relaxivites than that of clinical MRI contrast agents. In addition, these biomarker-targeted ProCAs have high Gd3+ binding affinities and metal selectivities. The highest binding affinity of the three GRPR-targeted contrast reagents obtained by grafting a GRPR ligand binding moiety into ProCA32 for GRPR is 2.7 x 10-9 M. We further demonstrate that GRPR-targeted ProCAs were able to semi-quantitatively evaluate GRPR expression levels in xenograft mice model by MRI. In addition, we have also created a PSMA-targeted ProCA which has a binding affinity to PSMA biomarker of 5.2 x 10-7 M. Further, we developed VEGFR-targeted contrast agent which is able to image VEGFR2 in mice models using T1-weighted and T2-weighted sequences. Moreover, the relaxivities and coordination water numbers of ProCAs can be tuned by protein design of ProCA4. Since disease biomarkers are expressed in various tumors and diseases, our results may have strong preclinical and clinical implications for the diagnosis and therapeutics of cancer and other type of diseases.

Many problems facing policymakers are complex and cannot be understood by reducing them to their component parts. However, many of the policy responses to complex problems continue to be based on simple, reductionist methods. Agent-based modeling (ABM) is one alternative method for informing policy that is well-suited to analyzing complex problems. ABM has practical implications for different stages of the policy process, such as testing alternatives, assisting with evaluation by setting up a counterfactual, and agenda setting. The objective of the research presented in this dissertation is to explore the opportunity for using ABM to examine complex problems of relevance for policy. To do so, three separate models were developed to investigate different aspects of food safety inspection systems. Complex problems involve interrelated feedback loops, many actors, exponential growth, asymmetric information, and uncertainty in outcomes and data, and food safety exhibits these traits, providing an interesting case study for the use of ABM. The first model explores three inspection scenarios incorporating access to information. The main finding was that the number of sick consumers is greatly reduced by giving consumers and inspectors more information about whether a retail outlet is contaminated, even if that information may be uncertain. The second model incorporated theories on risk and the role of transparency in encouraging consumer trust by giving consumers access to inspection scores. Overall, the findings were more nuanced: having access to restaurant inspection scores results in a slightly higher mean number of sick consumers, but less variation overall in the number of sick consumers. As well, a greater number of compliant restaurants results in fewer sick consumers. Rather than investigating the structure of the inspection system, the third model examines the potential for mobile technology to crowdsource information about suspected foodborne illness. This model illustrates the potential for health-oriented mobile technologies to improve the surveillance system for foodborne illness. Overall, the findings from the three models support using stylized ABMs to study various aspects of food safety inspection systems, and show that these models can be used to generate insight for policy choices and evidence-based decision making in this area.

Therapeutic efficacy of drugs depends on their ability to reach the treatment target. Drugs that exert their effect within cells are constrained by an inability to cross the cell membrane. Methods are being developed to overcome this barrier including biochemical and biophysical strategies. The application of ultrasound with microbubbles increases the permeability of cell membranes allowing molecules, which otherwise would be excluded, to enter the intracellular space of cells; a phenomenon known as sonoporation. This thesis describes studies aimed at improving our understanding of the mechanism underpinning sonoporation and of the exposure parameters affecting sonoporation efficiency. Cancer cells (KHT-C) in suspension were exposed to ultrasound and microbubbles – total of 97 exposure conditions. The effects on cells were assessed through uptake of cell-impermeable molecules (10 kDa to 2 MDa FITC-dextran), cell viability and microscopic observations of the plasma membrane using flow cytometry, colony assay and electron microscopy techniques. Sonoporation was a result of the interaction of ultrasound and microbubbles with the cell membrane. Disruptions (30-100 nm) were generated on the cell membrane allowing cell impermeable molecules to cross the membrane. Molecules up to 2 MDa in size were delivered at high efficiency (~70% permeabilisation). Sonoporation was short lived; cells re-established their barrier function within one minute, which allowed compounds to remain inside the cell. Following uptake, cells remained viable; ~50% of sonoporated cells proliferated. Sonoporation efficiency depended on ultrasound and microbubble exposure conditions. Microbubble disruption was a necessary but insufficient indicator of ultrasound-induced permeabilisation. The exposure conditions can be tailored to achieve a desired effect; cell permeability of ~70% with ~25% cell death versus permeability of ~35% with ~2% cell death. In addition, sonoporation depended on position in the cell cycle. Cells in later stages were more prone to being permeabilised and killed by ultrasound and microbubbles. This study indicated that sonoporation can be controlled through exposure parameters and that molecular size may not be a limiting factor. However, the transient nature may necessitate that the drug be in close vicinity to target cells in sonoporation-mediated therapies. Future work will extend the investigation into in vivo models.

Freight transport is the output of an economic market, which converts commodity flows into vehicle flows. Interactions in this market influence vehicle flows and since freight market characteristics (product differentiation and economies of scale/scope) violate perfect competition conditions, the output of this market cannot be predicted directly, unless these interactions are represented in the forecasting models. Traditional freight modelling frameworks do not consider these interactions and consequently they may provide inaccurate freight flow forecasts. In this dissertation, a freight modelling framework is proposed using simulation of freight agent interactions in the economic market to forecast freight flows. The framework is named FREMIS (FREight Market Interactions Simulation). The FREMIS framework consists of two demand models to represent shipper decisions (bundling of shipments and carrier selection) in the market and functions based on profit maximizing behaviour to simulate carrier proposals for contracts. Besides that, learning models are proposed to simulate agent learning processes based on their interactions. The framework was developed aiming to create a realistic representation of freight markets using feasible data collection methods. To illustrate the feasibility of the data collection, a customized web survey was implemented with shippers and carriers in a freight market. Two probabilistic models were developed using the data. The first model, a shipment bundling model was proposed combining a probabilistic model and a vehicle routing algorithm. The results of the probabilistic model are presented in this dissertation, where the locations of shipments (origin and destination) influence the probability of bundling them. Second, three carrier selection models were developed aiming to analyse the nonresponse bias and non-attendance problem in the survey. All of these models assumed heteroskedasticity (different scale or variance) in shipper behaviour. In all models, the hypothesis of agents’ heteroskedasticity cannot be rejected. Besides that, nonresponse bias and non-attendance problem were identified in the survey. In conclusion, the models obtained from the survey were consistent with their behavioural assumptions and therefore they can be adopted during FREMIS implementation.

A new class of polymer stabilized gadolinium trifluoride nanoparticles (NPs) have been developed as contrast agents for magnetic resonance imaging (MRI) and computed tomography (CT), with potential long term goals in targeted imaging and anti-cancer therapy. The NPs are comprised of a 90/10 mixture of GdF3/EuF3 and are coated with linear polyacrylic acid (PAA) chains consisting of 25 repeating units. The resulting aggregates are stable in serum and possess unprecedented mass relaxivities [i.e. ~100-200 s-1(mg/mL)-1]. Electron microscopy images reveal various NP morphologies which depend on the exact synthesis protocol. These include highly cross-linked oblong clusters with 30-70 nm cross sections, extensively cross-linked aggregates with 100-300 nm cross sections, and distinct polymer stabilized nanocrystals with 50 nm diameters. Their application as contrast agents in T1-weighted MRI studies, CT imaging at various X-ray energies, and preliminary rat brain perfusion studies was also tested. NP contrast enhancement was compared to Gd-DPTA (Magnevist®) and iopramide (Ultravist 300®) to demonstrate their high contrasting properties and potential as multimodal contrast agents.

This thesis focuses on deadbeat disturbance rejection for discrete-time linear multi-agent systems. The multi-agent systems, on which Spieser and Shams’ decentralized deadbeat output regulation problem is based, are extended by including disturbance agents. Specifically, we assume that there are one or more disturbance agents interacting with the plant agents in some known manner. The disturbance signals are assumed to be unmeasured and, for simplicity, constant. Control agents are introduced to interact with the plant agents, and each control agent is assigned a target plant agent. The goal is to drive the outputs of all plant agents to zero in finite time, despite the presence of the disturbances. In the decentralized deadbeat output regulation problem, two analysis schemes were introduced: targeting analysis, which is used to determine whether or not control laws can be found to regulate, not all the agents, but only the target agents; and growing analysis, which is used to determine the behaviour of all the non-target agents when the control laws are applied. In this thesis these two analyses are adopted to the deadbeat disturbance rejection problem. A new necessary condition for successful disturbance rejection is derived, namely that a control agent must be connected to the same plant agent to which a disturbance agent is connected. This result puts a bound on the minimum number of control agents and constraints the locations of control agents. Then, given the premise that both targeting and growing analyses succeed in the special case where the disturbances are all ignored, a new control approach is proposed for the linear case based on the idea of integral control and the regulation methods of Spieser and Shams. Preliminary studies show that this approach is also suitable for some nonlinear systems.

The few past years have witnessed a huge leap in the field of the smart grid communication networks in which many theories are being developed, and many applications are being evolved to accommodate the implementation of the smart grid concepts. Distribution power systems are considered to be one of the first leading fields having the strong desire of applying the smart grid concepts; resulting in the emersion of the smart distribution power systems, which are the future visualization of the distribution systems having both the ability of smart acting, and the capabilities of automation, self-healing, and decentralized control. For the sake of the real implementation of the smart distribution power systems, the main functions performed by the traditional systems have to be performed by the new smart systems as well, taking into account the new features and properties of those smart systems. One of those main functions is the ability of power networks optimal reconfiguration to minimize the system’s power loss while preserving the system radial topology. The proposed reconfiguration methodology targets the utilization of a hybrid genetic algorithm with two fuzzy controllers that could converge to the global optimal network configuration with the fastest convergence rate consuming the least computational time. The first fuzzy controller is designed to reject any infeasible system configurations that might show up in the population of the genetic algorithm and violate the system radial topology, while the second fuzzy controller is designed to adapt the mutation rate of the genetic algorithm. Consequently, a novel multi-agent system is proposed and designed to perform the reconfiguration application in smart distribution power systems employing the concepts of distributed processing and decentralized control demanded by those systems. A multi-agent system employs a group of intelligent agents that have the capabilities of autonomy, reactivity, pro-activity, and sociality. Those agents cooperate with each other in order to perform a certain function through their powerful abilities to communicate, socialize, and make a common decision in a decentralized fashion based on the information retrieved from the surrounding environment and compiles with their ultimate objective.

Multiagent simulation is gaining popularity due to its intuitiveness and ability in coping with domain complexity. HLA, being a distributed simulation architecture standard, is a good candidate for implementing a multiagent simulation infrastructure on, provided that agent communication can be implemented. HLA, being a standard designed towards a wide coverage of simulation system architectures and styles, is not an easy system to master. In this thesis, an abstraction layer called the Federate Abstraction Layer (FAL) is described for better engineering of software systems participating in an HLA simulation, providing lower project risks for the project manager and ease of use for the C++ programmers. The FAL is in use in project SAVMOS in Modelling and Simulation Laboratory. Discussion of FAL is followed by discussion of the study for realizing KQML for use in multiagent architectures to be built on top of HLA as the data transfer medium. The results are demonstrated with 10 federates implemented using the FAL.

The few past years have witnessed a huge leap in the field of the smart grid communication networks in which many theories are being developed, and many applications are being evolved to accommodate the implementation of the smart grid concepts. Distribution power systems are considered to be one of the first leading fields having the strong desire of applying the smart grid concepts; resulting in the emersion of the smart distribution power systems, which are the future visualization of the distribution systems having both the ability of smart acting, and the capabilities of automation, self-healing, and decentralized control. For the sake of the real implementation of the smart distribution power systems, the main functions performed by the traditional systems have to be performed by the new smart systems as well, taking into account the new features and properties of those smart systems. One of those main functions is the ability of power networks optimal reconfiguration to minimize the system’s power loss while preserving the system radial topology. The proposed reconfiguration methodology targets the utilization of a hybrid genetic algorithm with two fuzzy controllers that could converge to the global optimal network configuration with the fastest convergence rate consuming the least computational time. The first fuzzy controller is designed to reject any infeasible system configurations that might show up in the population of the genetic algorithm and violate the system radial topology, while the second fuzzy controller is designed to adapt the mutation rate of the genetic algorithm. Consequently, a novel multi-agent system is proposed and designed to perform the reconfiguration application in smart distribution power systems employing the concepts of distributed processing and decentralized control demanded by those systems. A multi-agent system employs a group of intelligent agents that have the capabilities of autonomy, reactivity, pro-activity, and sociality. Those agents cooperate with each other in order to perform a certain function through their powerful abilities to communicate, socialize, and make a common decision in a decentralized fashion based on the information retrieved from the surrounding environment and compiles with their ultimate objective.