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Introduction A great deal of time and resources have been spent on developing and implementing evidence-based return to work programs over the past few decades, compelling researchers to better understand the factors associated with more favorable outcomes. Using data collected as part of a participant tracking system trial for the Canadian Armed Forces (CAF) Return to Duty (RTD) program, analyses were conducted to better understand the trajectories of program participants and identify the factors associated with RTD. Materials and Methods Participants included 205 Regular Force CAF members from a single military base located in Eastern Canada who entered the RTD program during the trial period between April 2018 and March 2020. The health condition they were facing was mostly recent (i.e. onset within the past 6 months; 43%) and involved their mental health (67%). Data were collected on various demographic, military, health, and program characteristics using the RTD Data Collection Tool, which was updated periodically by program coordinators. Using data gathered by the Tool, a cumulative incidence function was generated to estimate the overall marginal probability of RTD over the duration of the program. Associations between RTD and a range of factors that were captured using the Tool were also examined in a series of competing-risks regressions. Results Findings indicated that the rate of RTD among program participants increased at around 3 months and began to level off around 9 months, suggesting that the likelihood of RTD after this window is diminished. Of the many factors that were considered, only years of service and work placement status at 3 months were found to be associated with RTD. Specifically, lower rates of RTD were observed among participants with 15 or more years of service compared to those with less than 5 years of service in the CAF and among those who were not yet assigned a work placement at 3 months relative to those who were. Conclusion This study represents a first step in addressing the gap in our current knowledge about the characteristics of CAF members participating in the RTD program and the factors associated with RTD. Several recommendations are made for improving the participant tracking system in view of enhancing the level and quality of information that is available to assess participants' trajectories and inform further development of the program. [ABSTRACT FROM AUTHOR]

• Halphen distribution family of heavy-tailed three-parameter distributions is introduced to model wind speed. • Hourly wind speed datasets at 126 locations in Eastern Canada that passed modified Mann-Kendall test were used. • Halphen B showed best-fit according to Kolmorogov-Smirnov statistic. • HDF provides fit superior to the two-parameter Weibull distribution in 100% of the stations according to the Akaike information criterion. • 87 stations were classified as class D tail behavior. We introduce the family of three-parameter heavy-tailed distributions, the Halphen distribution family (HDF), to model the mean wind speed for the purpose of wind energy estimation. The HDF has a number of properties favorable to model wind speed data, such as lower bound at zero (absence of location parameter), flexibility to cover a large range of shapes, and an explicit form of moment generating functions. We examined 126 stations in Eastern Canada (125 stations were heavy-tailed with positive excess kurtosis) and found that HDF provides fit superior to the most commonly used distribution for this purpose, the two-parameter Weibull distribution, in 100% of the stations according to the Akaike information criterion (AIC). HDF was compared against 4 two-parameter models (Gaussian, Weibull, Gamma, and inverse Gamma) and 3 three-parameter models (generalized extreme value, generalized Gamma and Burr). The most common best-fit distribution for the stations in the Eastern Canada case study is the HDF (46% according to AIC). The results of the case study show that wind observations cannot be fit by the Halphen inverse type B, but can be modelled by Halphen A and especially well by Halphen B (minimum of Kolmogorov-Smirnov statistic among candidate distributions). 87 stations exhibited class D tail behavior. No correlation between tail behavior class and best-fit distribution was observed. We encourage the use of Halphen A and Halphen B as candidate distributions for wind resource estimation studies that are based on mean wind speed estimation. [ABSTRACT FROM AUTHOR]

Abstract: Sreedhar et al. [V.C. Sreedhar, G.R. Gao, Y.-F. Lee, A new framework for elimination-based data flow analysis using DJ graphs, ACM Trans. Program. Lang. Syst. 20 (2) (1998) 388–435; V.C. Sreedhar, Efficient program analysis using DJ graphs, PhD thesis, School of Computer Science, McGill University, Montréal, Québec, Canada, 1995] have presented an elimination-based algorithm to solve data flow problems. A thorough analysis of the algorithm shows that the worst-case performance is at least quadratic in the number of nodes of the underlying graph. In contrast, Sreedhar reports a linear time behavior based on some practical applications. In this paper we prove that for goto-free programs, the average case behavior is indeed linear. As a byproduct our result also applies to the average size of the so-called dominance frontier. A thorough average case analysis based on a graph grammar is performed by studying properties of the j-edges in DJ graphs. It appears that this is the first time that a graph grammar is used in order to analyze an algorithm. The average linear time of the algorithm is obtained by classic techniques in the analysis of algorithms and data structures such as singularity analysis of generating functions and transfer lemmas. [Copyright &y& Elsevier]

This paper presents the implementation of stochastic dual dynamic programming (SDDP) to generate water-value functions for operations planning of the British Columbia (BC) Hydro system. An inflow model is developed to generate stochastic seasonal volumes forecasts and monthly inflows for the Peace River and upper Columbia River in Canada. A model is developed to model storage spaces for flood control and storage account operations to comply with the Columbia River Treaty (CRT) and subsequent agreements between Canada and the United States. Two novel algorithms are developed. The first uses SDDP and an updating process to generate the end of planning horizon water-value function, and the second uses SDDP to generate monthly water-value functions with a new stopping criterion for SDDP. Analysis results of the water-value functions illustrate the importance of globally optimizing reservoirs and storage accounts and modeling stochastic inflows. Results of simulation studies show significant benefits of using water-value functions over currently used methods and the need for modeling inflow uncertainties and storage account operations. [ABSTRACT FROM AUTHOR]