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This paper presents how the Erlangization method can be employed to estimate probabilities for the time-dependent behaviour of the level in a Markov modulated fluid flow. Formulas are given for estimating the probability of transitions to any fluid level, starting from an initial level in either an increasing or a decreasing stage in the phase process, prior to a finite time-point of interest. This methodology is applied to a model developed to study the evolution of uncontrolled fire perimeter for forest fires in Ontario, Canada. The model estimates the relative effectiveness of fire suppression under various fire-weather and fuel type scenarios, focusing on containment and escape probabilities. Numerical results are provided for illustrative purposes. [ABSTRACT FROM AUTHOR]

Pediatric obesity is an urgent and complex public health issue. Approximately one-third of Canadian children are overweight or obese, a proportion that highlights the need for effective and accessible services to improve short- and long-term health risks. In our experience, we have encountered a number of challenges common in pediatric obesity management across our clinical and research centers. For the purpose of this review, these challenges and our real-world experiences are grouped as issues that span (i) caring for children, adolescents, and families, (ii) collaborating with colleagues and (iii) working within the health care system. Collectively, we highlight a number of lessons learned from our years of experience and detail ongoing initiatives designed to optimize health services for managing obesity for children and adolescents in Canada. [ABSTRACT FROM AUTHOR]

This study investigates dynamically different data-driven methods, specifically a statistical downscaling model (SDSM), a time lagged feedforward neural network (TLFN), and an evolutionary polynomial regression (EPR) technique for downscaling numerical weather ensemble forecasts generated by a medium range forecast (MRF) model. Given the coarse resolution (about 200-km grid spacing) of the MRF model, an optimal use of the weather forecasts at the local or watershed scale, requires appropriate downscaling techniques. The selected methods are applied for downscaling ensemble daily precipitation and temperature series for the Chute-du-Diable basin located in northeastern Canada. The downscaling results show that the TLFN and EPR have similar performance in downscaling ensemble daily precipitation as well as daily maximum and minimum temperature series whatever the season. Both the TLFN and EPR are more efficient downscaling techniques than SDSM for both the ensemble daily precipitation and temperature. [ABSTRACT FROM AUTHOR]