Ryerson University

About: Ryerson University is a education organization based out in Toronto, Ontario, Canada. It is known for research contribution in the topics: Population & Poison control. The organization has 7671 authors who have published 20164 publications receiving 394976 citations. The organization is also known as: Ryerson Polytechnical Institute & Ryerson Institute of Technology.

Reservoir permeability prediction by neural networks combined with hybrid genetic algorithm and particle swarm optimization

Abstract: Reservoir characterization involves describing different reservoir properties quantitatively using various techniques in spatial variability Nevertheless, the entire reservoir cannot be examined directly and there still exist uncertainties associated with the nature of geological data Such uncertainties can lead to errors in the estimation of the ultimate recoverable oil To cope with uncertainties, intelligent mathematical techniques to predict the spatial distribution of reservoir properties appear as strong tools The goal here is to construct a reservoir model with lower uncertainties and realistic assumptions Permeability is a petrophysical property that relates the amount of fluids in place and their potential for displacement This fundamental property is a key factor in selecting proper enhanced oil recovery schemes and reservoir management In this paper, a soft sensor on the basis of a feed-forward artificial neural network was implemented to forecast permeability of a reservoir Then, optimization of the neural network-based soft sensor was performed using a hybrid genetic algorithm and particle swarm optimization method The proposed genetic method was used for initial weighting of the parameters in the neural network The developed methodology was examined using real field data Results from the hybrid method based soft sensor were compared with the results obtained from the conventional artificial neural network A good agreement between the results was observed, which demonstrates the usefulness of the developed hybrid genetic algorithm and particle swarm optimization in prediction of reservoir permeability

Toronto’s Urban Heat Island—Exploring the Relationship between Land Use and Surface Temperature

Abstract: The urban heat island effect is linked to the built environment and threatens human health during extreme heat events. In this study, we analyzed whether characteristic land uses within an urban area are associated with higher or lower surface temperatures, and whether concentrations of “hot” land uses exacerbate this relationship. Zonal statistics on a thermal remote sensing image for the City of Toronto revealed statistically significant differences between high average temperatures for commercial and resource/industrial land use (29.1 °C), and low average temperatures for parks and recreational land (25.1 °C) and water bodies (23.1 °C). Furthermore, higher concentrations of either of these land uses were associated with more extreme surface temperatures. We also present selected neighborhoods to illustrate these results. The paper concludes by recommending that municipal planners and decision-makers formulate policies and regulations that are specific to the problematic land uses, in order to mitigate extreme heat.

Top management support of enterprise systems implementations

Abstract: Despite the general consensus regarding the critical role of top management in the information systems (ISs) implementation process, the literature has not yet provided a clear and compelling understanding of the top management support (TMS) concept. Applying metastructuring (Orlikowski et al., 1995) as a guiding framework for understanding TMS behaviors, this paper attempts to address the gap by focusing on two key questions: (1) What supportive actions do top managers engage in during IS implementations? (2) How do these actions affect IS implementation outcomes? Analyses of in-depth case studies at two Canadian universities that had implemented a large-scale enterprise system revealed three distinct types of TMS actions: TMS – resource provision (TMSR – actions related to supplying key resources such as funds, technologies, staff, and user training programs); TMS – change management (TMSC – actions related to fostering organizational receptivity of a new IS); and TMS – vision sharing (TMSV – actions related to ensuring that lower-level managers develop a common understanding of the core objectives and ideals for the new system). Results suggest that different support behaviors exercise different influences on implementation outcomes, and that top managers need to adjust their support actions to achieve the desired outcomes. In particular, TMSR affected project completion, TMSC impacted formation of user skills and attitudes, and TMSV influenced middle manager buy-in. Theoretical and practical implications of these findings are discussed.