No abstract

The rate stability has significant influence on project cost variance and should be taken into consideration when operating simulations on cost estimation. This, however, has been largely neglected by most previous studies. The rate stability, the main issue of this article, is the phenomenon that the rate vibrates in a fairly narrow range. Compared with the fluctuation of the rate, the continuous vibration can be deemed as a constant rate on the time axis. A lack of awareness of the rate stability results in the inaccurate estimate of project cost variance. To tackle this problem, the multi-interval cost rate model, MICOR, is proposed to explicitly evaluate the variation due to the rate stability of the project cost. Furthermore, a new simulation mechanism, which effortlessly integrates the rate stability into the simulations of the construction project's cost, is designed to facilitate the MICOR after the theoretical feasibility of the MICOR is analytically verified. This article indicates that the rate stability does influence the project cost variation significantly and should be involved in the stimulations for the cost estimate of a construction project.

Many initial movements require subsequent corrective movements but how motor cortex transitions to make corrections and how similar the encoding is to initial movements is unclear. We recorded a large population of neurons during a precision reaching task across multiple sessions to examine the neural space during not only initial movements but also subsequent corrective movements. AutoLFADS, an auto-encoder based deep-learning model, was applied to analyze individual corrective movements unique to any given trial and to stitch across sessions into a single, common neural space. Several locations in the neural space where corrective submovements originated after the initial reaches were identified and were different than the baseline firing rate before initial movements. The neural trajectories for corrective submovements were organized according to both their starting position as well as reach direction but varied with several local domains rather than a single global space. Decoding of reach velocity generalized poorly from initial to corrective submovements and corrective decoding was relatively poor. To improve corrective movement decoding, we demonstrate that a state-dependent decoder incorporating where in the neural space a correction was initiated improved performance, highlighting the diverse neural features of corrective movements during a precision reaching task.