Learning-assisted optimization for transmission switching.

The design of new strategies that exploit methods from machine learning to facilitate the resolution of challenging and large-scale mathematical optimization problems has recently become an avenue of prolific and promising research. In this paper, we propose a novel learning procedure to assist in the solution of a well-known computationally difficult optimization problem in power systems: The Direct Current Optimal Transmission Switching (DC-OTS) problem. The DC-OTS problem consists in finding the configuration of the power network that results in the cheapest dispatch of the power generating units.

Anatomic and neurophysiological methods for the targeting and lesioning of the subthalamic nucleus: Cuban experience and review.

Objective: To develop a method to place a lesion precisely in the subthalamic nucleus (STN) and evaluate its effectiveness.

Methods: A retrospective study of targeting data collected during stereotactic planning to lesion the STN in 31 patients with Parkinson's disease and of results in more than 50 procedures was performed. The targeting method was based on computed tomographic imaging together with semimicroelectrode recording digital processing and electrical stimulation.