As there is no significant correlation of IV with VKF
, we propose that athletes should concentrate on hip extension in the leg-lowering period rather than knee flexion.
Accordingly, this study considers both VKF and HKF in order to better comprehend the specific impact of MIA of an MNC's knowledge inflows to its subsidiaries.
However, it is asserted that the positive relationship between MIA and HKF is lower than that between MIA and VKF.
Prior studies have pointed out that VKF is the most common type of knowledge inflow to a subsidiary (Mudambi 2002).
Based on the above arguments, we can claim that HQ would have a higher level of mandate on the VKF than on the HKF.
Hypothesis 1-2: The level of an MNC's MIA is positively related to the level of its subsidiary's knowledge inflows from other subsidiaries; however, the level of the positive relationship between MIA and HKF is lower than that between MIA and VKF.
In this paper, we show how VKF ideas can be used in the ensemble filtering context to overcome some problems related to existing ensemble methods.
In the VKF method, introduced in , the minimization is done with the LBFGS optimization method, that produces both the state estimate and a low-storage approximation of the covariance (inverse hessian at the minimizer).
Note that while VKF can solve the storage problem related to EKF, it requires a way to evolve the covariance in time (step 1(b) in the algorithm above).
Here we follow the VKF ideas and show how they can be implemented in the ensemble filtering context, resulting in a filter that we call the variational ensemble Kalman filter (VEnKF).
Testing in the VKF
tunnels is much the same, except for a different process of installing the models and making configuration changes.
Installation of door leaves with VKF
approval in existing frames