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ABSTRACT:Background:E-learning has become commonplace in medical education. Incorporation of multimedia, clinical cases, and interactive elements has increased its attractiveness over textbooks. Although there has been an expansion of e-learning in medicine, the feasibility of e-learning in pediatric neurology is unclear. This study evaluates knowledge acquisition and satisfaction using pediatric neurology e-learning compared to conventional learning.Methods:Residents of Canadian pediatrics, neurology, and pediatric neurology programs and medical students from Queens University, Western University, and the University of Ottawa were invited to participate. Learners were randomly assigned two review papers and two ebrain modules in a four-topic crossover design. Participants completed pre-tests, experience surveys, and post-tests. We calculated the median change in score from pre-test to post-test and constructed a mixed-effects model to determine the effect of variables on post-test scores.Results:In total, 119 individuals participated (53 medical students; 66 residents). Ebrain had a larger positive change than review papers in post-test score from pre-test score for the pediatric stroke learning topic but a smaller positive change for Duchenne muscular dystrophy, childhood absence epilepsy, and acute disseminated encephalomyelitis. Learning topics showed statistical relationship to post-test scores (p= 0.04). Depending on topic, 57–92% (N= 59–66) of respondents favored e-learning over review article learning.Conclusions:Ebrain users scored higher on post-tests than review paper users. However, the effect is small and it is unclear if it is educationally meaningful. Although the difference in scores may not be substantially different, most learners preferred e-learning. Future projects should focus on improving the quality and efficacy of e-learning modules.

We present a statistically enhanced version of the GreenHouse gas emissions of current Oil Sands Technologies model that facilitates characterization of variability of greenhouse gas (GHG) emissions associated with mining and upgrading of bitumen from Canadian oil sands. Over 30 years of publicly available project-specific operating data are employed as inputs, enabling Monte Carlo simulation of individual projects and the entire industry, for individual years and project life cycles. We estimate that median lifetime GHG intensities range from 89 to 137 kg CO2eq/bbl synthetic crude oil (SCO) for projects that employ upgrading. The only project producing dilbit that goes directly to a refinery has a median lifetime GHG intensity of 51 kg CO2eq/bbl dilbit. As SCO and dilbit are distinct products with different downstream processing energy requirements, a life cycle assessment (“well to wheel”) is needed to properly compare them. Projects do not reach steady-state in terms of median GHG intensity. Projects with broader distributions of annual GHG intensities and higher median values are linked to specific events (e.g., project expansions). An implication for policymakers is that no specific technology or operating factor can be directly linked to GHG intensity and no particular project or year of operation can be seen as representative of the industry or production technology.

Previous transportation fuel life cycle assessment studies have not fully accounted for the full variability in the crude oil transport stage, for example, transporting a light crude through a high-diameter pipeline, vs transporting a heavy crude through a small-diameter pipeline. We develop a first-principles, fluid mechanics-based crude oil pipeline transportation emissions model (COPTEM) that calculates the greenhouse gas (GHG) emissions associated with pipeline transport as a function of crude oil parameters, pipeline dimensions, and external factors. Additionally, we estimate the emissions associated with the full life cycle of pipeline construction, maintenance, and disposal. This model is applied to an inventory of 62 major Canadian and U.S. pipelines (capacity greater than 100 000 barrels/day) to estimate the variability of GHG emissions associated with pipeline transportation. We demonstrate that pipeline GHG emissions intensities range from 0.23 to 20.3 g CO2e/(bbl·km), exhibiting considerably greater variability than data reported in other studies. A sensitivity analysis demonstrates that the linear velocity of crude transport and pipeline diameter are the most impactful parameters driving this variability. To illustrate one example of how COPTEM can be used, we develop an energy efficiency gap analysis to investigate the possibilities for more efficient pipeline transport of crude oil.