Context. Data leakage prevention (DLP), a system designed to prevent leakage and loss of secret sensitive data and at the same time not affect employees workflow. The aim is to have a system covering every possible leakage point that exist. Even if these are covered, there are ways of hiding information such as obfuscating a zip archive within an image file, detecting this hidden information and preventing it from leaking is a difficult task. Companies pay a great deal for these solutions and yet, as we uncover, the information is not safe. Objectives. In this thesis we evaluate four different existing types of DLP systems out on the market today, disclosing their weaknesses and found ways of improving their security. Methods. The four DLP systems tested in this study cover agentless, agent based, hybrids and regular expression DLP tools. The test cases simulate potential leakage points via every day used file transfer applications and media such as USB, Skype, email, etc. Results. We present a hypothetical solution in order to amend these weaknesses and to improve the efficiency of DLP systems today. In addition to these evaluations and experiments, a complementing proof of concept solution has been developed that can be integrated with other DLP solutions. Conclusions. We conclude that the exisiting DLP systems are still in need of improvement, none of the tested DLP solutions fully covered the possible leakage points that could exist in the corporate world. There is a need for continued evaluation of DLP systems, aspects and leakage points not covered in this thesis as well as a follow up on our suggested solution.

Context. Connected vehicles use sensors such as cameras or radars to collect data about surrounding environments automatically and share these data with each other or with road side infrastructure using short-range wireless communication. Due to the large amount of information generated, strategies are required to minimize information redundancy when important information is propagated among connected vehicles. Objectives. This research aims to develop an information propagation strategy in connected vehicle systems using software agent-based coordination strategies to reduce unnecessary message broadcast and message propagation delay. Methods. A review of related work is used to acquire a deep insight as well as knowledge of the state-of-the-art and the state-of-practice from relevant studies in the subject area. Based on the review of related work, we propose an agent-based coordination strategy for information propagation in connected vehicle systems, in which connected vehicles coordinate their message broadcast activities using auctions. After that, a simulation experiment is conducted to evaluate the proposed strategy by comparing it with existing representative strategies. Results. Results of simulation experiments and statistical tests show that the proposed agent-based coordination strategy manifest some improvements in reducing unnecessary message broadcast and message propagation delay compared to other strategies involved in the simulation experiments. Conclusions. In this research, we suggest a new strategy to manage the propagation of information in connected vehicle systems. According to the small scale simulation analysis, the use of auctions to select message transmitters enables our proposed strategy to achieve some improvements in reducing unnecessary message broadcast and propagation delay than existing strategies. Thus, with the help of our proposed strategy, unnecessary message broadcast can be minimized and the communication resources of connected vehicle systems can be utilized effectively. Also, important safety messages can be propagated to drivers faster, negative traffic events could be averted. / 0707708513

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Context: Due to an increase in inter-terminal transportation (ITT) volume within a container port; complexity of transportation processes between the terminals has also increased. Problems with the current way of handling ITT resources are expected to rise in the near future. Different types of vehicles are already in place for transporting containers between different terminals in a container port. However, there need to be an efficient and effective use of these vehicle types in order to get maximum benefits out of these resources. Objectives: In this thesis, we investigate and propose a solution model for ITT considering the combination of both manned (MTS, Trucks) and unmanned (AGV) vehicles. An agent based model is proposed for ITT focusing on three ITT vehicle types. Objective of proposed model is to investigate the capabilities and combination of different vehicles for transporting containers between different container terminals in a port.  Methods: A systematic literature review is conducted to identify the problems and methods and approaches for solving those problems in the domain of container transportation. As a case, an agent-based model is proposed for the Maasvlakte area of the Rotterdam port. Simulations are performed on different scenarios to compare three different road vehicle types, i.e., AGV, MTS, and truck, in a network comprising of ten terminals. Results: Literature review results indicate that heuristics is the most commonly used method to solve different problems of container transportation in the recent past. The review also depicts that limited research has been published focusing on ITT when compared to intra-terminal transportation. Simulation results of our proposed model indicate that AGVs outperforms trucks in terms of loading/unloading time and number of vehicles required to handle the given volume of all scenarios. In most of the cases, it is observed that the number of trucks required are twice as much as compared to AGVs in order to transport containers between different terminals. Results also show that lower number MTS vehicles (as compared to AGVs) are required for handling containers in certain scenarios; however, the loading/unloading time for MTS is much higher than that of AGVs. Conclusions: Using agent-based simulation experiments, we propose a model that can help in estimating the required resources (vehicles) to handle the ITT containers volume and improve the utilization of different resources in a network of terminals. From comparison of three road vehicle types, it was concluded that trucks are incapable to handle higher container volume in an ITT. It was also concluded that AGVs can be an appropriate choice if automated operations are supported in the terminals, otherwise MTS is the best choice concerning the number of vehicles required to handle containers. Our simulation results may help the ITT planners in better estimations and planning of ITT to meet current and future challenges of transporting high containers volume.

This dissertation presents a collection of manuscripts that describe development of models and model implementation to analyze impact of potential A(H7N9) pandemic influenza outbreak in the U.S. Though this virus is still only animal-to-human transmittable, it has potential to become human-to-human transmittable and trigger a pandemic. This work is motivated by the negative impact on human lives that this virus has already caused in China, and is intended to support public health officials in preparing to protect U.S. population from a potential outbreak of pandemic scale. An agent-based (AB) simulation model is used to replicate the social dynamics of the contacts between the infected and the susceptible individuals. The model updates at the end of each day the status of all individuals by estimating the infection probabilities. This considers the contact process and the contagiousness of the infected individuals given by the disease natural history of the virus. The model is implemented on sample outbreak scenarios in selected regions in the U.S. The sampling results are used to estimate disease burden for the whole U.S. The results are also used to examine the impact of various virus strengths as well as the efficacy of different intervention strategies in mitigating a pandemic burden. This dissertation, also characterizes the infection time during a A(H7N9) influenza pandemic. Continuous distributions including exponential, Weibull, and lognormal are considered as possible candidates to model the infection time. Based on the negative likelihood, lognormal distribution provides the best fit. Such characterization is important, as many critical questions about the pandemic impact can be answered from using the distribution. Finally, the dissertation focuses on assessing community preparedness to deal with pandemic outbreaks using resilience as a measure. Resilience considers the ability to recover quickly from a pandemic outbreak and is defined as a function of the percentage of healthy population at any time. The analysis, estimations, and metrics presented in this dissertation are new contributions to the literature and they offer helpful perspectives for the public health decision makers in preparing for a potential threat of A(H7N9) pandemic.

Supply chain management is a complex process requiring the coordination of numerous decisions in the attempt to balance often-conflicting objectives such as quality, cost, and on-time delivery. To meet these and other objectives, a focal company must develop organized systems for establishing and managing its supplier relationships. A reliable, decision-support tool is needed for selecting the best procurement strategy for each supplier, given knowledge of the existing sourcing environment. Supplier segmentation is a well-established and resource-efficient tool used to identify procurement strategies for groups of suppliers with similar characteristics. However, the existing methods of segmentation generally select strategies that optimize performance during normal operating conditions, and do not explicitly consider the effects of the chosen strategy on the supply chain’s ability to respond to disruption. As a supply chain expands in complexity and scale, its exposure to sources of major disruption like natural disasters, labor strikes, and changing government regulations also increases. With increased exposure to disruption, it becomes necessary for supply chains to build in resilience and robustness in the attempt to guard against these types of events. This work argues that the potential impacts of disruption should be considered during the establishment of day-to-day procurement strategy, and not solely in the development of posterior action plans. In this work, a case study of a laser printer supply chain is used as a context for studying the effects of different supplier segmentation methods. The system is examined using agent-based modeling and simulation with the objective of measuring disruption impact, given a set of initial conditions. Through insights gained in examination of the results, this work seeks to derive a set of improved rules for segmentation procedure whereby the best strategy for resilience and robustness for any supplier can be identified given a set of the observable supplier characteristics.

Organizations, once established, tend not to change, typically going obsolete as society continues to evolve. This makes adaptability an important issue. Organizational members must make sense to each other, or coordination suffers. They must also make sense to environmental actors, or the organization will not achieve the support it needs to survive. This sense-making is a process of institutionalization, of constructing a shared understanding of the organization's enterprise, business and environment. When environmental actors adopt new priorities, ideas and modes of thinking, organizations must develop a new understanding of reality or go obsolete. To stay adaptable, organizations needs to contain competing perspectives. But the pressures to conform and to coordinate make it difficult for established organizations to adapt, trapped by the very benefits of increased efficiency. Using an agent-based model of organizational institutionalization, I show that an organization's adaptability is highly dependent on structural elements that affect the member interaction frequencies, and that organizations that leverage the strength of weak ties between member groups can maintain adaptability. The effects of changes to any of these elements are decidedly non-linear, however, which helps explain why it is difficult to design effective organizations. Organizational structure is one part of a tri-partite framework of institutional development in organizations and of organizational adaptability, where the other two parts are the individual characteristics of the members and the content of the organizational culture.

Deforestation and land use change are widespread in western Uganda. However, the spatial patterns and time-series of change and the reasons why it is occurring remain to be fully investigated. In this work a combination of satellite imagery and social surveys is used to quantify forest gains and loss over the last three decades in the region close to Lake Albert, whilst also providing an account of possible drivers of change. This area proves to be interesting as it covers regions with both formally protected areas (gazetted regions) and un-protected forest, the latter being largely under private ownership. Remote sensing data from the Landsat satellites were gathered for forest change detection, and were processed using standard remote sensing techniques, then quantified using GIS and regression methods. Fieldwork allowed these data to be ground truthed while gathering (quantitative) household surveys and (qualitative) key informant interviews. Quantitative surveys were analysed using Principal Components Analysis (PCA) and cluster analysis, and were compared qualitatively with the satellite analysis and stakeholder interviews. The results show that forest cover declined significantly outside gazetted areas at the expense of varying local?scale processes, although the protection of the gazetted forests was remarkably successful. In forest corridors outside gazetted regions, losses exceeded 90% (p<0.05). Survey data suggest that rural poor households were more likely to be situated in forested regions, and were more dependent on forest resources for their livelihoods. However, the drivers of change were spatially variable, with expansion of sugarcane farming being a likely driver in the northern areas, but small?scale agricultural expansion a significant factor in the more southern parts of the study region. While there is wide agreement within the data that the patterns of forest cover and land use changes are anthropogenically driven, more specific drivers are swamped by intricacies of the bio-physical and socio-economic preconditions that are inseparable in both space and time, although agricultural expansion and population growth were evident and pervasive. The analyses provide insights into complex anthropogenic processes at various spatial scales, and policy recommendations provided are widely applicable for developing countries struggling to conserve nature whilst boosting economic growth.

Introduction: Tuberculosis (TB) is an infectious disease that causes significant morbidity and mortality. Despite the fact that the total burden of TB has decreased dramatically, the distribution of that burden across the Canadian population has not changed. A century ago, the Indigenous population of Canada had a significantly higher TB mortality than the non-Indigenous population. This gap still exists today. TB is a disease of poverty, and understanding the role of the social determinants of health (SDH) may provide insights into the causes of persistence of TB in the Indigenous population. Research questions: This thesis tackles three questions: 1) Can a TB outbreak that took place over a century ago be reconstructed? 2) What can we learn about the relationship between the disease, the population it afflicted, and the environment in which the outbreak took place? 3) How can reconstruction of a TB outbreak be used to evaluate policy interventions? Area studied: Analyses were limited to the Qu’Appelle Agency, located in Southeastern Saskatchewan. Methodology: An agent-based model of socioeconomic environment of the Qu’Appelle Agency was developed to study the relationship between TB and SDH. Data on TB mortality, demographics, agricultural production, material circumstances, and economic factors of production were used to study the relationship between TB and SDH at the aggregate level. Results: 1) Extensive aggregate data analyses were carried out and an agent-based model of TB transmission and of the socioeconomic environment of the Qu’Appelle Agency was developed. 2) Results of these analyses identify a number of important parameters responsible for the high TB mortality in the Agency. These parameters include biological factors, housing, social characteristics, agricultural output, and policies of the Department of Indian Affairs. Conclusions: This research demonstrates that reconstruction of an outbreak of an infectious disease that took place over a century ago is a complex undertaking that hinges on availability of data and significant expertise in a variety of fields, such as health sciences, economics, mathematics, and modelling approaches. The further one goes into the past, the more one is forced to rely on assumptions, which make the reconstructed web of relationships between agent, host, and environment that caused the outbreak less certain. Despite the inherent uncertainty, the process of outbreak reconstruction provides a deep and multi-faceted understanding of the interactions among the agent, the host, and the environment. The resulting model is a useful way of studying policy interventions that could be applied in other contexts as well – to other infectious diseases or TB outbreaks on other reserves. Keywords: [population health, epidemiology, tuberculosis, Indigenous peoples, agent-based modelling, social determinants of health]

This diploma thesis deals with the usage of agents in business process modelling. The aim is to identify possible uses in the problem area and to evaluate their potential. Intended usage is analysed on the basis of the objectives of process modelling. Described in detail, it focuses on potential application of agents in a process modelling itself, in deploying executable and flexible processes, process improvements and finally in dynamic planning. A further objective is to provide an overview of current research and literature in problem area. To fulfil the goal available literature is analysed and basis approaches are demonstrated in case study. Paper does not address the implementation of multi-agent systems (MAS). Following introduction reader is guided though basic elements of the processes and their modelling, concept of agents and modelling of MAS. As a process modelling notation BPMN in planned 2.0 release was selected. Next section is devoted to the usage of agents. The last part of the work is a case study discussing the possibilities of agents application in company operating public car auctions. In addition the process model (BPMN) transformation to agent model is presented and further developed as a interconnection between process modelling and agent-based systems. Combination of agent approach with business rules and hybrid simulation are proposed as the most promising usages.