Browsing by Author "Yong, Alan"
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- ItemMacEwan University WiFi analysis(2016) Prince, James; Yong, Alan; Anton, CristinaOne of the worst feelings in the world is waiting for a slow Internet connection. While this may be more a reflection of our impaired society than a faulty modem, this study will shed some light as to soothe these pains while on MacEwan University Campus. MacEwan University has recently undergone a "WiFi Renovation", with many new WiFi units installed all throughout the school. The goal of this experiment is to find where in the school are the strongest and weakest connections. This will be an interesting reflection on the new system effectiveness and coverage. The factors that will be tested for are the Location in the school, Time of Day, Day of the Week and Type of Device used, and blocking will be done on the last four factors. To measure the connection quality, a file of a pre-determined size will be downloaded and the time taken will be recorded. Alan will be using an Apple Iphone, and James will be using a Samsung Galaxy S3, which will eliminate the chance of a newer device being different than an older one, or an Apple device vs an Android. This study will determine which factors are significant, and which factor combination yields the best results in terms of WiFi connectivity. This method of mapping a WiFi system will be useful to students and to the IT management of the University because the results of this study will provide the school with information which will help plan for future changes to WiFi layout. An easy extension of the methodology of this experiment could be developed and used to assess any WiFi or cellular device service. The results from this experiment alone will be interesting, but a larger application of the method could be groundbreaking.
- ItemStochastic dynamics and survival analysis of a cell population model with random perturbations(2018) Anton, Cristina; Yong, AlanWe consider a model based on the logistic equation and linear kinetics to study the effect of toxicants with various initial concentrations on a cell population. To account for parameter uncertainties, in our model the coefficients of the linear and the quadratic terms of the logistic equation are affected by noise. We show that the stochastic model has a unique positive solution and we find conditions for extinction and persistence of the cell population. In case of persistence we find the stationary distribution. The analytical results are confirmed by Monte Carlo simulations.
- ItemThe influence of noise in cytoxicity assessment(2016) Yong, Alan; Anton, CristinaIndustrial activity produces many chemicals that may be hazardous to human health or the environment. Traditionally, experiments can be carried out on live subjects (in vivo), but this is both expensive and raises significant ethical concerns. Rather than conducting these assays, we might try using mathematical or computational models to assess the effect of these toxicants (1). With in vitro assays at the Alberta Centre of Toxicology using the xCelligence Real-Time Cell Analysis HT system, time-dependent response curves (TCRCs) were generated. These experimentally-derived curves reflect the response of human cells to these toxicants. The goal was to find a mathematical model that could accurately reproduce these curves (2). Depending on the value of various parameters of the toxicant – such as its toxicity and how fast cells absorb it – there are generally two possible equilibria dependent on the toxicant's initial external concentration: a cell line may persevere and survive; or the concentration may be large enough to cause extinction of the cell population. Data from the TCRCs were used to generate a deterministic model. That is, a given set of parameter values will always generate the same cell fate. However, there is inherently uncertainty in the value of these parameters. To assess the influence of this noise on the external concentration of toxicant at which some population of cells would reach survival or extinction equilibria, a new model was created with additional variables for this uncertainty. Several simulations were run with this extended model. The information generated will be useful for planning further experiments regarding cytotoxicity, and for numerically generating TCRCs for clustering and classification.