Implementation of hospital-wide computer information system :a case study of this change process

This case study examines the impact of a computer information system as it was being implemented in one Ontario hospital. The attitudes of a cross section of the hospital staff acted as a barometer to measure their perceptions of the implementation process. With The Mississauga Hospital in the early stages of an extensive computer implementation project, the opportunity existed to identify staff attitudes about the computer system, overall knowledge and compare the findings with the literature. The goal of the study was to develop a greater base about the affective domain in the relationship between people and the computer system. Eight exploratory questions shaped the focus of the investigation. Data were collected from three sources: a survey questionnaire, focused interviews, and internal hospital documents. Both quantitative and qualitative data were analyzed. Instrumentation in the study consisted of a survey distributed at two points in time to randomly selected hospital employees who represented all staff levels.Other sources of data included hospital documents, and twenty-five focused interviews with staff who replied to both surveys. Leavitt's socio-technical system, with its four subsystems: task, structure, technology, and people was used to classify staff responses to the research questions. The study findings revealed that the majority of respondents felt positive about using the computer as part of their jobs. No apparent correlations were found between sex, age, or staff group and feelings about using the computer. Differences in attitudes, and attitude changes were found in potential relationship to the element of time. Another difference was found in staff group and perception of being involved in the decision making process. These findings and other evidence about the role of change agents in this change process help to emphasize that planning change is one thing, managing the transition is another.

The effectiveness of a computer program on career planning for university students

This study investigated the effectiveness of a computer program, PERSONAL CAREER DIRECTIONS (PC DIRECTIONS) (Anderson, Welborn, & Wright, 1983) on career planning and exploration for twenty-four Brock University students (18 women and 6 men) who requested career planning assistance at the Career/Placement Services of the Counselling Centre. A one-group pretest/posttest design was used in the study_ Progress in career planning and exploration was measured by Career Planning (CP) and Career Exploration (CE) scales of the Career Development Inventory (College and University Form) (Super, Thompson, Lindeman, Jordaan, & Myers, 1981). A paired samples 2-tailed t test for Career Development Attitudes (CDA) , the combined CP and CE scales, revealed the posttest scores were significantly higher than the pretest scores, t(23) = 3.74, 2 < .001. Student progress was also assessed by self-report lists of job titles which reflected positive changes after students used PC DIRECTIONS. In response to several questions, students' attitudes were more positive than negative toward the program. Implications are that PC DIRECTIONS is an effective component in promoting career planning for university students. Further studies may reveal that different types of students may benefit from different interventions in the career planning process.

The effects of computer assisted interactive fiction on the development of reading comprehension in grade five students

This study was undertaken in order to determine the effects of playing computer based text adventure games on the reading comprehension gains of students. Forty-five grade five students from one elementary school were randomly assigned to experimental and control groups, and were tested with regard to ability, achievement and reading skills. An experimental treatment, consisting of playing computer based interactive fiction games of the student's choice for fifteen minutes each day over an eight-week period, was administered. A comparison treatment engaged the control group in sustained silent reading of materials of the student's choice for an equal period of time. Following the experimental period all students were post-tested with an alternate form of the pre-test in reading skills, and gain scores were analysed. It was found that there were no significant differences in the gain scores of the experimental and control groups for overall reading comprehenSion, but the experimental group showed greater gains than the control group in the structural analysis reading sub-skill. Extreme variance in the data made generalization very difficult, but the findings indicated a potential for computer based interactive fiction as a useful tool for developing reading sl

Evolutionary synthesis of stochastic gene network models using feature-based search spaces

A feature-based fitness function is applied in a genetic programming system to synthesize stochastic gene regulatory network models whose behaviour is defined by a time course of protein expression levels. Typically, when targeting time series data, the fitness function is based on a sum-of-errors involving the values of the fluctuating signal. While this approach is successful in many instances, its performance can deteriorate in the presence of noise. This thesis explores a fitness measure determined from a set of statistical features characterizing the time series' sequence of values, rather than the actual values themselves. Through a series of experiments involving symbolic regression with added noise and gene regulatory network models based on the stochastic 'if-calculus, it is shown to successfully target oscillating and non-oscillating signals. This practical and versatile fitness function offers an alternate approach, worthy of consideration for use in algorithms that evaluate noisy or stochastic behaviour.

The Salmon Algorithm - A New Population Based Search Metaheuristic

This thesis introduces the Salmon Algorithm, a search meta-heuristic which can be used for a variety of combinatorial optimization problems. This algorithm is loosely based on the path finding behaviour of salmon swimming upstream to spawn. There are a number of tunable parameters in the algorithm, so experiments were conducted to find the optimum parameter settings for different search spaces. The algorithm was tested on one instance of the Traveling Salesman Problem and found to have superior performance to an Ant Colony Algorithm and a Genetic Algorithm. It was then tested on three coding theory problems - optimal edit codes, optimal Hamming distance codes, and optimal covering codes. The algorithm produced improvements on the best known values for five of six of the test cases using edit codes. It matched the best known results on four out of seven of the Hamming codes as well as three out of three of the covering codes. The results suggest the Salmon Algorithm is competitive with established guided random search techniques, and may be superior in some search spaces.

Comparison of classification ability of hyperball algorithms to neural network and k-nearest neighbour algorithms

The main focus of this thesis is to evaluate and compare Hyperbalilearning algorithm (HBL) to other learning algorithms. In this work HBL is compared to feed forward artificial neural networks using back propagation learning, K-nearest neighbor and 103 algorithms. In order to evaluate the similarity of these algorithms, we carried out three experiments using nine benchmark data sets from UCI machine learning repository. The first experiment compares HBL to other algorithms when sample size of dataset is changing. The second experiment compares HBL to other algorithms when dimensionality of data changes. The last experiment compares HBL to other algorithms according to the level of agreement to data target values. Our observations in general showed, considering classification accuracy as a measure, HBL is performing as good as most ANn variants. Additionally, we also deduced that HBL.:s classification accuracy outperforms 103's and K-nearest neighbour's for the selected data sets.

Enabling and Measuring Complexity in Evolving Designs using Generative Representations for Artificial Architecture

As the complexity of evolutionary design problems grow, so too must the quality of solutions scale to that complexity. In this research, we develop a genetic programming system with individuals encoded as tree-based generative representations to address scalability. This system is capable of multi-objective evaluation using a ranked sum scoring strategy. We examine Hornby's features and measures of modularity, reuse and hierarchy in evolutionary design problems. Experiments are carried out, using the system to generate three-dimensional forms, and analyses of feature characteristics such as modularity, reuse and hierarchy were performed. This work expands on that of Hornby's, by examining a new and more difficult problem domain. The results from these experiments show that individuals encoded with those three features performed best overall. It is also seen, that the measures of complexity conform to the results of Hornby. Moving forward with only this best performing encoding, the system was applied to the generation of three-dimensional external building architecture. One objective considered was passive solar performance, in which the system was challenged with generating forms that optimize exposure to the Sun. The results from these and other experiments satisfied the requirements. The system was shown to scale well to the architectural problems studied.

Generating Aesthetically Pleasing Images in a Virtual Environment using Particle Swarm Optimization

This research focuses on generating aesthetically pleasing images in virtual environments using the particle swarm optimization (PSO) algorithm. The PSO is a stochastic population based search algorithm that is inspired by the flocking behavior of birds. In this research, we implement swarms of cameras flying through a virtual world in search of an image that is aesthetically pleasing. Virtual world exploration using particle swarm optimization is considered to be a new research area and is of interest to both the scientific and artistic communities. Aesthetic rules such as rule of thirds, subject matter, colour similarity and horizon line are all analyzed together as a multi-objective problem to analyze and solve with rendered images. A new multi-objective PSO algorithm, the sum of ranks PSO, is introduced. It is empirically compared to other single-objective and multi-objective swarm algorithms. An advantage of the sum of ranks PSO is that it is useful for solving high-dimensional problems within the context of this research. Throughout many experiments, we show that our approach is capable of automatically producing images satisfying a variety of supplied aesthetic criteria.

The development of a Flanders Interaction Analysis style instrument for use in educational computer mediated communication research

Please consult the paper edition of this thesis to read. It is available on the 5th Floor of the Library at Call Number: Z 9999 E38 D56 1992

Heuristics for the Critical Node Detection Problem in Large Complex Networks

Complex networks have recently attracted a significant amount of research attention due to their ability to model real world phenomena. One important problem often encountered is to limit diffusive processes spread over the network, for example mitigating pandemic disease or computer virus spread. A number of problem formulations have been proposed that aim to solve such problems based on desired network characteristics, such as maintaining the largest network component after node removal. The recently formulated critical node detection problem aims to remove a small subset of vertices from the network such that the residual network has minimum pairwise connectivity. Unfortunately, the problem is NP-hard and also the number of constraints is cubic in number of vertices, making very large scale problems impossible to solve with traditional mathematical programming techniques. Even many approximation algorithm strategies such as dynamic programming, evolutionary algorithms, etc. all are unusable for networks that contain thousands to millions of vertices. A computationally efficient and simple approach is required in such circumstances, but none currently exist. In this thesis, such an algorithm is proposed. The methodology is based on a depth-first search traversal of the network, and a specially designed ranking function that considers information local to each vertex. Due to the variety of network structures, a number of characteristics must be taken into consideration and combined into a single rank that measures the utility of removing each vertex. Since removing a vertex in sequential fashion impacts the network structure, an efficient post-processing algorithm is also proposed to quickly re-rank vertices. Experiments on a range of common complex network models with varying number of vertices are considered, in addition to real world networks. The proposed algorithm, DFSH, is shown to be highly competitive and often outperforms existing strategies such as Google PageRank for minimizing pairwise connectivity.

Passive Solar Building Design Using Genetic Programming

Passive solar building design is the process of designing a building while considering sunlight exposure for receiving heat in winter and rejecting heat in summer. The main goal of a passive solar building design is to remove or reduce the need of mechanical and electrical systems for cooling and heating, and therefore saving energy costs and reducing environmental impact. This research will use evolutionary computation to design passive solar buildings. Evolutionary design is used in many research projects to build 3D models for structures automatically. In this research, we use a mixture of split grammar and string-rewriting for generating new 3D structures. To evaluate energy costs, the EnergyPlus system is used. This is a comprehensive building energy simulation system, which will be used alongside the genetic programming system. In addition, genetic programming will also consider other design and geometry characteristics of the building as search objectives, for example, window placement, building shape, size, and complexity. In passive solar designs, reducing energy that is needed for cooling and heating are two objectives of interest. Experiments show that smaller buildings with no windows and skylights are the most energy efficient models. Window heat gain is another objective used to encourage models to have windows. In addition, window and volume based objectives are tried. To examine the impact of environment on designs, experiments are run on five different geographic locations. Also, both single floor models and multi-floor models are examined in this research. According to the experiments, solutions from the experiments were consistent with respect to materials, sizes, and appearance, and satisfied problem constraints in all instances.

Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TM

Genetic Programming (GP) is a widely used methodology for solving various computational problems. GP's problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages for object classification is presented. Two main GP approaches for visual pattern classification, namely the block-classifiers and the pixel-classifiers, were studied. Results showed that the pixel-classifiers generally performed better. Using these results, a suitable language was selected for the real-time implementation. Synthetic video data was used in the experiments. The goal of the experiments was to evolve a unique classifier for each texture pattern that existed in the video. The experiments revealed that the system was capable of correctly tracking the textures in the video. The performance of the system was on-par with real-time requirements.

Postsecondary Artist Teachers' Responses to Computer Technology in Their Drawing Pedagogy

While the influence of computer technology has been widely studied in a variety of contexts, the drawing teaching studio is a particularly interesting context because of the juxtaposition of traditional medium and computer technology. For this study, 5 Canadian postsecondary teachers engaged in a 2-round Delphi interview process to discuss their responses to computer technology on their drawing pedagogy. Data sources included transcribed interviews. Findings indicated that artist teachers are both cautious to embrace and curious to explore appropriate use of computer technology on their drawing pedagogy. Artist teachers are both critical and optimistic about the influence of computer technology.

Disease-Gene Association Using a Genetic Algorithm

Understanding the relationship between genetic diseases and the genes associated with them is an important problem regarding human health. The vast amount of data created from a large number of high-throughput experiments performed in the last few years has resulted in an unprecedented growth in computational methods to tackle the disease gene association problem. Nowadays, it is clear that a genetic disease is not a consequence of a defect in a single gene. Instead, the disease phenotype is a reflection of various genetic components interacting in a complex network. In fact, genetic diseases, like any other phenotype, occur as a result of various genes working in sync with each other in a single or several biological module(s). Using a genetic algorithm, our method tries to evolve communities containing the set of potential disease genes likely to be involved in a given genetic disease. Having a set of known disease genes, we first obtain a protein-protein interaction (PPI) network containing all the known disease genes. All the other genes inside the procured PPI network are then considered as candidate disease genes as they lie in the vicinity of the known disease genes in the network. Our method attempts to find communities of potential disease genes strongly working with one another and with the set of known disease genes. As a proof of concept, we tested our approach on 16 breast cancer genes and 15 Parkinson's Disease genes. We obtained comparable or better results than CIPHER, ENDEAVOUR and GPEC, three of the most reliable and frequently used disease-gene ranking frameworks.

Optimal and Robust Designs of Step-stress Accelerated Life Testing Experiments for Proportional Hazards Models

Accelerated life testing (ALT) is widely used to obtain reliability information about a product within a limited time frame. The Cox s proportional hazards (PH) model is often utilized for reliability prediction. My master thesis research focuses on designing accelerated life testing experiments for reliability estimation. We consider multiple step-stress ALT plans with censoring. The optimal stress levels and times of changing the stress levels are investigated. We discuss the optimal designs under three optimality criteria. They are D-, A- and Q-optimal designs. We note that the classical designs are optimal only if the model assumed is correct. Due to the nature of prediction made from ALT experimental data, attained under the stress levels higher than the normal condition, extrapolation is encountered. In such case, the assumed model cannot be tested. Therefore, for possible imprecision in the assumed PH model, the method of construction for robust designs is also explored.