The pursuit of academic excellence and business engagement: Is it irreconcilable?

Universities currently need to satisfy the demands of different audiences. In light of the increasing policy emphasis on "third mission" activities, universities are attempting to incorporate these into their traditional missions of teaching and research. University strategies to accomplishing its traditional missions are well-honed and routinized, but the incorporation of the third mission is posing important strategic and managerial challenges for universities. This study explores the relationship between university-business collaborations and academic excellence in order to examine the extent to which academic institutions can balance these objectives. Based on data from the UK Research Assessment Exercise 2001 at the level of the university department, we find no systematic positive or negative relationship between scientific excellence and engagement with industry. Across the disciplinary fields reported in the 2001 Research Assessment Exercise (i. e. engineering, hard sciences, biomedicine, social sciences and the humanities) the relationship between academic excellence and engagement with business is largely contingent on the institutional context of the university department. This paper adds to the growing body of literature on university engagement with business by examining this activity for the social sciences and the humanities. Our findings have important implications for the strategic management of university departments and for higher education policy related to measuring the performance of higher education research institutions. © 2013 Akadémiai Kiadó, Budapest, Hungary.

Identification of the steering control behaviour of five test subjects following a randomly curving path in a driving simulator

The paper is concerned with the identification of theoretical preview steering controllers using data obtained from five test subjects in a fixed-base driving simulator. An understanding of human steering control behaviour is relevant to the design of autonomous and semi-autonomous vehicle controls. The driving task involved steering a linear vehicle along a randomly curving path. The theoretical steering controllers identified from the data were based on optimal linear preview control. A direct-identification method was used, and the steering controllers were identified so that the predicted steering angle matched as closely as possible the measured steering angle of the test subjects. It was found that identification of the driver's time delay and noise is necessary to avoid bias in identification of the controller parameters. Most subjects' steering behaviour was predicted well by a theoretical controller based on the lateral/yaw dynamics of the vehicle. There was some evidence that an inexperienced driver's steering action was better represented by a controller based on a simpler model of the vehicle dynamics, perhaps reflecting incomplete learning by the driver. Copyright © 2014 Inderscience Enterprises Ltd.

Model-based compressive Harmonic-Aware Matching Pursuit: An evaluation

This paper addresses devising a reliable model-based Harmonic-Aware Matching Pursuit (HAMP) for reconstructing sparse harmonic signals from their compressed samples. The performance guarantees of HAMP are provided; they illustrate that the introduced HAMP requires less data measurements and has lower computational cost compared with other greedy techniques. The complexity of formulating a structured sparse approximation algorithm is highlighted and the inapplicability of the conventional thresholding operator to the harmonic signal model is demonstrated. The harmonic sequential deletion algorithm is subsequently proposed and other sparse approximation methods are evaluated. The superior performance of HAMP is depicted in the presented experiments. © 2013 IEEE.