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Information about forecast uncertainty is vital for optimal decision making in many domains that use weather forecasts. However, it is not available in the immediate output of deterministic numerical weather prediction systems. In this paper, we investigate several learning methods to train and evaluate prediction interval models of weather forecasts. The uncertainty models of weather predictions are trained from a database of historical forecasts/observations. They are developed to investigate prediction intervals of weather forecasts using various quantile regression methods as well as cluster-based probabilistic forecasts using fuzzy methods. To compare and verify probabilistic forecasts, a novel score is developed that accounts for sampling variation effects on forecast verification statistics. The impact of various feature sets and model parameters in forecast uncertainty modeling is also investigated. The results show superior performance of the non-linear quantile regression models in comparison with clustering methods.

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This paper uses dynamic thermal line rating methods and a probabilistic prediction methodology to forecast line clearance and conductor temperature and evaluate the risk of clearance encroachment. A transient model is used to predict conductor temperature at different prediction levels. Clearance is estimated using a relationship model, developed based on historical measured clearance and conductor temperature data. Additional sensitivity analysis is performed to determine the applicability of ambient-adjusted predictions when considering a lightly loaded line compared to a heavily loaded line. The developed methodology enables utilities to make decisions on line loading in advance of real-time operation with information on confidence associated with that decision.

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Spinal cord injury (SCI) results in permanent loss of myelin forming oligodendrocytes that significantly contribute to white matter degeneration. Despite the existence of multipotent neural precursor cells (NPCs) inside the spinal cord, replacement of oligodendrocytes is severely limited after SCI. Interestingly, NPCs mainly contribute to oligodendrocyte turnover in the normal spinal cord; however, in SCI activated NPCs predominantly differentiate into astrocytes and contribute to scar formation. To date, we have only a poor understanding of the extracellular events that modulate NPCs in their post‐SCI niche. After SCI, local microenvironment undergoes profound modifications that limit the regenerative capacities of NPCs. Optimizing the SCI microenvironment is therefore critical not only to promote NPCs cell replacement, but also to attenuate the otherwise non‐constructive effects of NPCs activation after SCI. We have recently identified several mechanisms that influence the behaviour of NPCs after SCI. Our data suggest a role for the matrix molecules chondroitin sulfate proteoglycans (CSPGs) in limiting the activation and differentiation of NPCs after SCI. We also show that the inadequate oligodendrocyte cell replacement in SCI may be attributed to the impaired expression of neureglulin‐1/ErbB signalling in the spinal cord. This talk will discuss our recent research findings in these areas