Inventors and entrepreneurs in academia: What types of skills and experience matter?

This paper aims to improve our understanding of the attributes of academic researchers that influence the capacity to contribute to technical advance, by adding to the pool of technological opportunities available to industry or engaging in the exploitation of entrepreneurial opportunities. We investigate a number of factors associated with the skills developed by academic researchers. We find that contributions to the pool of technological opportunities and exploitation of entrepreneurial opportunities involve different sets of skills and expertise of scientists. Our results show that the former is driven by academic scientists research excellence and discovery of earlier technological opportunities and the latter is driven by previous collaboration with industry partners, scientific breadth and experience of technological discovery. © 2011 Elsevier Ltd. All rights reserved.

Adaptation to stable and unstable dynamics achieved by combined impedance control and inverse dynamics model.

This study compared adaptation in novel force fields where trajectories were initially either stable or unstable to elucidate the processes of learning novel skills and adapting to new environments. Subjects learned to move in a null force field (NF), which was unexpectedly changed either to a velocity-dependent force field (VF), which resulted in perturbed but stable hand trajectories, or a position-dependent divergent force field (DF), which resulted in unstable trajectories. With practice, subjects learned to compensate for the perturbations produced by both force fields. Adaptation was characterized by an initial increase in the activation of all muscles followed by a gradual reduction. The time course of the increase in activation was correlated with a reduction in hand-path error for the DF but not for the VF. Adaptation to the VF could have been achieved solely by formation of an inverse dynamics model and adaptation to the DF solely by impedance control. However, indices of learning, such as hand-path error, joint torque, and electromyographic activation and deactivation suggest that the CNS combined these processes during adaptation to both force fields. Our results suggest that during the early phase of learning there is an increase in endpoint stiffness that serves to reduce hand-path error and provides additional stability, regardless of whether the dynamics are stable or unstable. We suggest that the motor control system utilizes an inverse dynamics model to learn the mean dynamics and an impedance controller to assist in the formation of the inverse dynamics model and to generate needed stability.