Power and control: managing agents for international student recruitment in higher education

This multiple case-based study investigates the relationship between recruiting agents and the UK universities who act as their principals. The current extensive use of agents in UK higher education may be seen as an indicator of the financial impact made by international students. The study analyses the practice of agent management and explores the manner in which power and control interact. The study employed semi-structured interviews and group discussions involving up to 6 respondents from each of the 20 UK case institutions. The qualitative data reveal a considerable variation in the manner in which the universities manage their agency relationships. Through the joint consideration of control measures and use of power, five distinctive approaches have been identified. The study also reveals that over-dependence on agents reduces the power of the principal, and consequently, the principal’s ability to exercise control, particularly in highly competitive global and national markets.

Prediction of Parkinson’s Disease tremor onset using a radial basis function neural network based on particle swarm optimization

Deep Brain Stimulation (DBS) has been successfully used throughout the world for the treatment of Parkinson's disease symptoms. To control abnormal spontaneous electrical activity in target brain areas DBS utilizes a continuous stimulation signal. This continuous power draw means that its implanted battery power source needs to be replaced every 18–24 months. To prolong the life span of the battery, a technique to accurately recognize and predict the onset of the Parkinson's disease tremors in human subjects and thus implement an on-demand stimulator is discussed here. The approach is to use a radial basis function neural network (RBFNN) based on particle swarm optimization (PSO) and principal component analysis (PCA) with Local Field Potential (LFP) data recorded via the stimulation electrodes to predict activity related to tremor onset. To test this approach, LFPs from the subthalamic nucleus (STN) obtained through deep brain electrodes implanted in a Parkinson patient are used to train the network. To validate the network's performance, electromyographic (EMG) signals from the patient's forearm are recorded in parallel with the LFPs to accurately determine occurrences of tremor, and these are compared to the performance of the network. It has been found that detection accuracies of up to 89% are possible. Performance comparisons have also been made between a conventional RBFNN and an RBFNN based on PSO which show a marginal decrease in performance but with notable reduction in computational overhead.

Optimising the feedback loop of a millimetre-wave absolute photoacoustic power meter using H-infinity control synthesis

An H-infinity control strategy has been developed for the design of controllers used in feedback controlled electrical substitution measurements (FCESM). The methodology has the potential to provide substantial improvements in both response time and resolution of a millimetre-wave absolute photoacoustic power meter.

Power control requirements in linear decorrelating detectors for CDMA

This paper addresses the effects of synchronisation errors (time delay, carrier phase, and carrier frequency) on the performance of linear decorrelating detectors (LDDs). A major effect is that all LDDs require certain degree of power control in the presence of synchronisation errors. The multi-shot sliding window algorithm (SLWA) and hard decision method (HDM) are analysed and their power control requirements are examined. Also, a more efficient one-shot detection scheme, called “hard-decision based coupling cancellation”, is proposed and analysed. These schemes are then compared with the isolation bit insertion (IBI) approach in terms of power control requirements.