This Army-wide analysis reveals higher BCT-related injury rates for both men and women than prior studies and identifies risk factors for injuries during BCT. The large data set allows adjustment for many covariates, but because statistical analysis may yield significant findings for small differences, results must be interpreted based on minimally important differences determined by military and medical professionals. Results provide information that may be used to adapt training or medical screening and examination procedures for basic trainees.
Abstract:A virtual machine placement optimization model based on optimized ant colony algorithm is proposed. The model is able to determine the physical machines suitable for hosting migrated virtual machines. Thus, it solves the problem of redundant power consumption resulting from idle resource waste of physical machines. First, based on the utilization parameters of the virtual machine, idle resources and energy consumption models are proposed. The models are dedicated to quantifying the features of virtual resource utilization and energy consumption of physical machines. Next, a multi-objective optimization strategy is derived for virtual machine placement in cloud environments. Finally, an optimal virtual machines placement scheme is determined based on feature metrics, multi-objective optimization, and the ant colony algorithm. Experimental results indicate that compared with the traditional genetic algorithms-based MGGA model, the convergence rate is increased by 16%, and the optimized highest average energy consumption is reduced by 18%. The model exhibits advantages in terms of algorithm HI¿FLHQF\ DQG HI¿FDF\
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