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This paper develops an optimization model to control the excessive (MVAR) generation by super high voltage gird systems for maintaining the nodal voltages within the required acceptable margin. The model enhances an approximate solution on the condition of balanced real power. The pseudo-inverse method of optimization is implemented as an optimal optimization is implemented as an optimal means of solving non-square systems of equations based on LaGrange's theory of optimization. It recommends the locations and ratings of the minimum required reactors from the preinstalled ones that to be in service for optimum nodal reactive power and voltage controls.
A brief overview of causality analysis (CA) methods applied to MD simulations data for model biomolecular systems is presented. A CausalMD application for postprocessing of MD data was designed and implemented. MD simulations of two model systems, porphycene (ab initio MD) and HIV-1 protease (coarse-grained MD) were carried out and analyzed. Granger’s causality methodology based on a Multivariate Autoregressive (MVAR) formalism, followed by the Directed Transfer Function (DTF) analysis was applied. A novel approach based on the descriptors of local structure was also presented and prelim- inary results were reported. Casuality analyses are required for a better understanding of biomolecular functioning mechanisms. In particular, such analyses can link physics-based structural dynamics with functions inferred from molecular evolution processes. Current limitations and future developments of the presented methodologies are indicated.
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