The bounded Fatou components for certain quadratic polynomials are attached to each other at the boundary and form chain-like structures called ``bubble rays". In the context of mating quadratic polynomials, these bubble rays can serve as a replacement for external rays. The main objective of this thesis is to apply this idea to the mating of starlike quadratics.

In the domain of Web Services, it is not uncommon to find redundant services that provide functionalities to the clients. Services with the same functionality can be clustered into a group of redundant services. Respectively, if a service offers different functionalities, it belongs to more than one group. Having various Web Services that are able to handle the client's request suggests the necessity of a mechanism that selects the most appropriate Web Service at a given moment of time. <p>This thesis presents an approach, Virtual Web Services Layer, for dynamic service selection based on virtualization on the server side. It helps managing redundant services in a transparent manner as well as allows adding services to the system at run-time. In addition, the layer assures a level of security since the consumers do not have direct access to the Web Services. <p>Several selection techniques are applied to increase the performance of the system in terms of load-balancing, dependability, or execution time. The results of the experiments show which selection techniques are appropriate when different QoS criteria of the services are known and how the correctness of this information influences on the decision-making process.

How to manage a dynamic environment and how to provide task partitioning are two key concerns when developing distributed computing applications. The emergence of Grid computing environments extends these problems. Conventional resource management systems are based on a relatively static resource model and a centralized scheduler that assigns computing resources to users. Distributed management introduces resource heterogeneity: not only the set of available resources, but even the set of resource types is constantly changing. Obviously this is unsuitable for the present Grid. In addition, the Grid provides users with the physical infrastructure to run parallel programs. Because of this increasing availability, there are more requirements for parallelization technologies. Therefore, based on problems outlined above, this thesis provides a novel scheduler which not only enables dynamic management but also provides skeleton library to support the task partition. Dynamic management is derived from the concept of reflectiveness, which allows the Grid to perform like an efficient market with some limited government controls. To supplement the reflective mechanism, this thesis integrates a statistical forecasting approach to predict the environment of the Grid in the next period. The task partitioning support is extended from the skeleton library in the parallel computing and cluster computing areas. The thesis shows how this idea can be applied in the Grid environment to simplify the user’s programming works. Later in this PhD thesis, a Petri-net based simulation methodology is introduced to examine the performance of the reflective scheduler. Moreover, a real testing environment is set up by using a reflective scheduler to run a geometry optimization application. In summary, by combining knowledge from economics, statistics, mathematics and computer science, this newly invented scheduler not only provides a convenient and efficient way to parallelize users’ tasks, but also significantly improves the performance of the Grid.

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<p>Neural networks are mathematical formulations that can be "trained" to perform certain functions. One particular application of these networks of interest in this thesis is to "model" a physical system using only input-output information. The physical system and the neural network are subjected to the same inputs. The neural network is then trained to produce an output which is the same as the physical system for any input. This neural network model so created is essentially a "blackbox" representation of the physical system. This approach has been used at the University of Saskatchewan to model a load sensing pump (a component which is used to create a constant flow rate independent of variations in pressure downstream of the pump). These studies have shown the versatility of neural networks for modeling dynamic and non-linear systems; however, these studies also indicated challenges associated with the morphology of neural networks and the algorithms to train them. These challenges were the motivation for this particular research.</p> <p>Within the Fluid Power Research group at the University of Saskatchewan, a "global" objective of research in the area of load sensing pumps has been to apply dynamic neural networks (DNN) in the modeling of loads sensing systems.. To fulfill the global objective, recurrent generalized neural network (RGNN) morphology along with a non-gradient based training approach called the complex algorithm (CA) were chosen to train a load sensing pump neural network model. However, preliminary studies indicated that the combination of recurrent generalized neural networks and complex training proved ineffective for even second order single-input single-output (SISO) systems when the initial synaptic weights of the neural network were chosen at random.</p> <p>Because of initial findings the focus of this research and its objectives shifted towards understanding the capabilities and limitations of recurrent generalized neural networks and non-gradient training (specifically the complex algorithm). To do so a second-order transfer function was considered from which an approximate recurrent generalized neural network representation was obtained. The network was tested under a variety of initial weight intervals and the number of weights being optimized. A definite trend was noted in that as the initial values of the synaptic weights were set closer to the "exact" values calculated for the system, the robustness of the network and the chance of finding an acceptable solution increased. Two types of training signals were used in the study; step response and frequency based training. It was found that when step response and frequency based training were compared, step response training was shown to produce a more generalized network.</p> <p>Another objective of this study was to compare the use of the CA to a proven non-gradient training method; the method chosen was genetic algorithm (GA) training. For the purposes of the studies conducted two modifications were done to the GA found in the literature. The most significant change was the assurance that the error would never increase during the training of RGNNs using the GA. This led to a collapse of the population around a specific point and limited its ability to obtain an accurate RGNN.</p> <p>The results of the research performed produced four conclusions. First, the robustness of training RGNNs using the CA is dependent upon the initial population of weights. Second, when using GAs a specific algorithm must be chosen which will allow the calculation of new population weights to move freely but at the same time ensure a stable output from the RGNN. Third, when the GA used was compared to the CA, the CA produced more generalized RGNNs. And the fourth is based upon the results of training RGNNs using the CA and GA when step response and frequency based training data sets were used, networks trained using step response are more generalized in the majority of cases.</p>

In the domain of Web Services, it is not uncommon to find redundant services that provide functionalities to the clients. Services with the same functionality can be clustered into a group of redundant services. Respectively, if a service offers different functionalities, it belongs to more than one group. Having various Web Services that are able to handle the client's request suggests the necessity of a mechanism that selects the most appropriate Web Service at a given moment of time. <p>This thesis presents an approach, Virtual Web Services Layer, for dynamic service selection based on virtualization on the server side. It helps managing redundant services in a transparent manner as well as allows adding services to the system at run-time. In addition, the layer assures a level of security since the consumers do not have direct access to the Web Services. <p>Several selection techniques are applied to increase the performance of the system in terms of load-balancing, dependability, or execution time. The results of the experiments show which selection techniques are appropriate when different QoS criteria of the services are known and how the correctness of this information influences on the decision-making process.

Measuring human behavioral patterns has broad application across different sciences. An individuals social, proximal and geographical contact patterns can have significant importance in Delay Tolerant Networking (DTN) and epidemiological modeling. Recent advances in computer science have not only provided the opportunity to record these behaviors with considerably higher temporal resolution and phenomenological accuracy, but also made it possible to record specific aspects of the behaviors which have been previously difficult to measure.<p> This thesis presents a data collection system using tiny sensors which is capable of recording humans proximal contacts and their visiting pattern to a set of geographical locations. The system also collects information on participants health status using weekly surveys. The system is tested on a population of 36 participants and 11 high-traffic public places. The resulting dataset offers rich information on human proximal and geographic contact patterns cross-linked with their health information.<p> In addition to the basic analysis of the dataset, the collected data is applied to two different applications. In DTNs the dataset is used to study the importance of public places as relay nodes, and described an algorithm that takes advantage of stationary nodes to improve routing performance and load balancing in the network. In epidemiological modeling, the collected dataset is combined with data on H1N1 infection spread over the same time period and designed a model on H1N1 pathogen transmission based on these data. Using the collected high-resolution contact data as the models contact patterns, this work represents the importance of contact density in addition to contact diversity in infection transmission rate. It also shows that the network measurements which are tied to contact duration are more representative of the relation between centrality of a person and their chance of contracting the infection.

The purpose of this thesis is to understand dynamic behaviors of carbon fiber composite golf shaft and the influence of different carbon fiber shaft flexes on club heads. To achieve the purpose, the researcher used the finite element method (FEM) software LS-DYNA and ANSYS to analyze the dynamic behaviors of carbon fiber composite golf shaft. He also applied three rigid bodies and two revote joint in a swing mode to simulate swing motion. In the same driving moment, the comparison provides golfer with a reference for selecting suitable carbon fiber composite golf shaft. It also offers other researchers an FEM model to do further analysis of dynamic behaviors of golf heads with the carbon fiber composite shaft.

The analysis of economic environment is the basis of well managerial decisions for enterprises. However, the relationship among economic variables in the real world is a kind of dynamic complexity problem. Economics usually take advantage of mathematics to solve this kind of problem. Therefore, it will be very difficult for business administrators to analyze the long-term dynamics competitive. This study integrated system dynamics thinking and dynamics of strategic maneuvering to build a systems feedback model of dynamic competitive environment, which can be used to analyze the long-term dynamics of competitive variables under different scenarios, including the dynamic thinking of growth and core competitive strategy. Linked with balanced scorecard interface, the model will become a decision support system of dynamic competitive analysis, which can provide business administrators with the analytic tool of dynamics competitive environment to improve their decision qualities concerning finance, production, and marketing.

Electronic commerce (E-commerce) innovations: Internet-enabled commerce (I-commerce), mobile commerce (M-commerce) and ubiquitous commerce (U-commerce) have posed technological and organizational changes. This study develops an E-commerce innovation hypercube model to investigate these innovations and the impact of the innovations on the E-commerce stakeholders¡Ð E-businesses, providers, customers, and complementors. The results indicate that the innovation from I-commerce to M-commerce is architectural for customers and E-businesses, incremental for providers, but disruptive for complementors. The innovation from M-commerce to U-commerce is modular to customers, architectural to complementors, and disruptive to E-businesses and providers. Thereafter, several core dynamic capabilities that necessary for E-business transformation from I-commerce to M-commerce and from M-commerce to U-commerce and the practical indicators in developing these dynamic capabilities are suggested, respectively.