This conceptual study aimed at refining the conceptual rigor of D. W. Sue’s tricomponential model of multicultural counselling competency, and enhancing with an addition of new attitude component. This study anchored its theoretical basis on a concept of nomological network (Cronbach & Meehi, 1955). Construct explication approach (Murphy & Davidshofer, 1998) was taken to develop full explication of four componential model of MCC, containing attitude-awareness-knowledge-skills components. Comprehensive literature review was conducted in the area of multicultural counselling competency to develop working definitions of awareness-knowledge-skills component. Another review was conducted to develop a working definition and a conceptual model of attitude. Under the four-componential framework, a total of 284 characteristic descriptions previously developed under the tricomponential model were conceptually re-examined and re-categorized. Result of the analyses revealed a total of 13 subcategories under the four components. Full construct explication of the four componential model was developed. Research implications of the new model to MCC measurement studies and practical applications to training models will be discussed.

No description available.

The principal motivations for studying epidemics and their dynamics are understanding the biological characteristics of the epidemic agents and reducing the economical and social costs originating from epidemic outbreaks. The most commonly used epidemic models have important assumptions such as the law of mass action, and the latent and infectious periods being exponentially distributed with xed parameters. Under this kind of suppositions the models are analyzed with well known algorithms as the Euler and Euler-Maruyama, and methodology and results from the theory of Markovian processes. However, these assumptions are selected largely for their analytic convenience and in many cases are far from describing the agent's transmissibility attributes in the population and its biological characteristics in a host. The epidemic models studied here relax two important epidemic assumptions. The first to be relaxed is the one that susceptible individuals are equally likely to acquire the disease. A structure for the kind of individual contacts that can result in the infection transmission is incorporated in the population. This contact structure can be non-homogeneous and it is modeled as a random graph whose edges describe the contacts between individuals. The second assumption that is generalized, is the distribution of the latent and infectious period in the host individuals. This research work allows the latent and infectious period to have a distribution other than the exponential and hence the epidemic process is more general than a Markovian process. As in most stochastic models, the infectious contact is modeled as a random variable with Poisson distribution. However, to introduce the individual variations, the transmission rate is assumed to be a non negative random variable. This work extends the epidemic models suggested by Newman (2002) in two directions. The first, studies the hierarchical networks that have a more complex network structure, involving the interaction of populations. The second direction examines the evolution in times for outbreaks in networks. In this work, results for discrete and continuous time are obtained. The results for the continuous time model considers the infectious process to be a bivariate Markovian process. However, the results for the final outbreaks size and the developed simulation program include the general case were the latent and infectious period can have a distribution other than exponential. This research work also analyze the effect of four control measures in the contact structure, and using the simulation program and Monte Carlo-likelihood methodology, it estimates the parameters for measles and influenza. The results here obtained can be directly applied to study the dynamics of other kind of “agents” such as information and ideas. For example, the dynamics can involve the spread of computer viruses, rumors, eating habits and personal positions regarding a fact or idea.

A fundamental challenge in evolutionary biology and medical genetic research is to connect the phenotype (a disease in humans or an adaptive trait in animals or plants) with the genotype. Using a classical example of an adaptive trait with a strong Mendelian genetic basis - warfarin resistance in the Norway rat (Rattus norvegicus), my dissertation tests the main hypothesis that speculated ‘simple’ adaptive trait has a more complex genetic architecture. Warfarin is an anticoagulant rodenticide used since the 1950s, and also is a widely prescribed blood-thinning drug in human. As a rodenticide, warfarin has initially been very effective. However, resistant rodents have evolved quickly and Vkorc1 (vitamin K epoxide reductase complex subunit 1) is the known resistance gene. As a popular drug, warfarin has a narrow therapeutic window with several genes VKORC1, CYP2C9, CYP4F2 established as biomarkers predicting warfarin dose in humans, suggesting a complex genetic architecture of warfarin resistance in rodents. In my thesis I performed network-guided genomic association studies (NetGWAS) and gene expression analysis to identify candidate genes involved in warfarin resistance based on a sample of ~600 wild rats from 19 populations in Germany. My thesis work revealed that the resistance mutation in Vkorc1 likely is under balancing selection and was recently introduced to the rat population in our study area. A key innovation of my thesis is adopting a NetGWAS approach to prioritize true associations and conducting co-expression network analysis to detect expression changes related to warfarin. My work shows that additional candidate genes are connected to the vitamin K pathway of which Vkorc1 is an essential component. While the validation of identified genes remains a challenge, the value of my thesis for future investigation is shown: one candidate gene Calu (Calumenin) is associated with warfarin resistance in multiple populations and is an essential part of the vitamin K cycle. Finally, my thesis briefly examines the genetics underlying a newly postulated cost of resistance, arterial calcification. This dissertation provides us an innovative framework in which we learned the genetic architecture of an adaptive trait in multiple dimensions: nucleotide or expression variation, genomic distribution and gene-gene interactions.

Designing a secured network is the most important task in any enterprise or organization development. Securing a network mainly involves applying policies and procedures to protect different network devices from unauthorized access. Servers such as web servers, file servers, mail servers, etc., are the important devices in a network. Therefore, securing these servers is the first and foremost step followed in every security implementation mechanism. To implement this, it is very important to analyse and study the security mechanisms provided by the operating system. This makes it easier for security implementation in a network. This thesis work demonstrates the tasks needed to enhance the network security in Linux environment. The various security modules existing in Linux makes it different from other operating systems. The security measures which are mainly needed to enhance the system security are documented as a baseline for practical implementation. After analysing the security measures for implementing network security, it is important to understand the role of network monitoring tools and Linux inbuilt log management in maintaining the security of a network. This is accomplished by presenting a detailed discussion on network monitoring tools and log management in Linux. In order to test the network security, a network is designed using Linux systems by configuring different servers and application firewall for packet filtering. The security measures configured on each server to enhance its security are presented as part of the implementation. The results obtained while an unauthorized user accessing the servers from the external network are also documented along with attack information retrieved by different network monitoring tools and Linux inbuilt log messages.

Android, a platform for smartphones and mobile devices, is becoming more and more present in the market. Nevertheless, the battery runtime of smartphones is short and strongly influenced by the network usage. Some proposals exist to reduce the energy consumption associated to the network usage and increase the smartphone runtime. But for adjusting them for a real improvement it is required to study the network utilisation triggered by the smartphone applications. With this analysis the applications communication patterns can be obtained and used to predict the network usage and the amount of data expected. In order to gather network statistics of the running applications, a logger application is implemented for the Android platform to log network statistics of running applications. The statistics are analysed on a PC computer to obtain the applications' communication patterns. A number of applications are selected, sorted by the rankings of downloads and type. A detailed analysis of the network usage is presented. This analysis identifies some of their patterns, some application characteristics and groups of applications from the determined network usage. The network usages for applications with similar functionalities are compared and lessons learnt from the analysis are discussed. Finally, some improvements for our logger application and analysis are discussed.

<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>

The principal motivations for studying epidemics and their dynamics are understanding the biological characteristics of the epidemic agents and reducing the economical and social costs originating from epidemic outbreaks. The most commonly used epidemic models have important assumptions such as the law of mass action, and the latent and infectious periods being exponentially distributed with xed parameters. Under this kind of suppositions the models are analyzed with well known algorithms as the Euler and Euler-Maruyama, and methodology and results from the theory of Markovian processes. However, these assumptions are selected largely for their analytic convenience and in many cases are far from describing the agent's transmissibility attributes in the population and its biological characteristics in a host. The epidemic models studied here relax two important epidemic assumptions. The first to be relaxed is the one that susceptible individuals are equally likely to acquire the disease. A structure for the kind of individual contacts that can result in the infection transmission is incorporated in the population. This contact structure can be non-homogeneous and it is modeled as a random graph whose edges describe the contacts between individuals. The second assumption that is generalized, is the distribution of the latent and infectious period in the host individuals. This research work allows the latent and infectious period to have a distribution other than the exponential and hence the epidemic process is more general than a Markovian process. As in most stochastic models, the infectious contact is modeled as a random variable with Poisson distribution. However, to introduce the individual variations, the transmission rate is assumed to be a non negative random variable. This work extends the epidemic models suggested by Newman (2002) in two directions. The first, studies the hierarchical networks that have a more complex network structure, involving the interaction of populations. The second direction examines the evolution in times for outbreaks in networks. In this work, results for discrete and continuous time are obtained. The results for the continuous time model considers the infectious process to be a bivariate Markovian process. However, the results for the final outbreaks size and the developed simulation program include the general case were the latent and infectious period can have a distribution other than exponential. This research work also analyze the effect of four control measures in the contact structure, and using the simulation program and Monte Carlo-likelihood methodology, it estimates the parameters for measles and influenza. The results here obtained can be directly applied to study the dynamics of other kind of “agents” such as information and ideas. For example, the dynamics can involve the spread of computer viruses, rumors, eating habits and personal positions regarding a fact or idea.

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 objective of this research is to design and develop a reconfigurable string matching co-processor using field-programmable gate array (FPGA) technology that is capable of matching thousands of complex patterns at gigabit network rates for network intrusion detection systems (NIDS). The motivation for this work is to eliminate the most significant bottleneck in current NIDS software, which is the pattern matching process. The tasks involved with this research include designing efficient, high-performance hardware circuits for pattern matching and integrating the pattern matching co-processor with other NIDS components running on a network processor. The products of this work include a system to translate standard intrusion detection patterns to FPGA pattern matching circuits that support all the functionality required by modern NIDS. The system generates circuits efficient enough to enable the entire ruleset of a popular NIDS containing over 1,500 patterns and 17,000 characters to fit into a single low-end FPGA chip and process data at an input rate of over 800 Mb/s. The capacity and throughput both scale linearly, so larger and faster FPGA devices can be used to further increase performance. The FPGA co-processor allows the task of pattern matching to be completely offloaded from a NIDS, significantly improving the overall performance of the system.