Leadership and innovation lessons from professional services firms

This paper compares and contrasts higher education with professional services firms. It considers what (if anything) leaders in higher education may gain from reflecting on how other sectors are evolving and the extent to which lessons can be learned by looking outwards. We structure the paper by outlining the world of PSFs, its many manifestations and some of the current challenges in Section 2. We then move on to compare and interpret one particular leadership framework we developed to understand PSFs, and use this to identify some potential questions for higher education leaders. Finally, we conclude by exploring how, in the light of some of the current drivers of change, our view of the higher education sector may evolve as a new ‘eco-system’ emerges

Disruptive innovation and the higher education ‘eco-system’ post-2012

Disruptive innovations in business sectors are arguably triggered by the arrival of new competitors who disturb, or punctuate, an existing equilibrium. They can be aided by changes to a wider context. The eco-system of professional services has seen major disruptions over the last 10 or 15 years. We extend our review of managerial lessons from a previous essay to present speculative scenarios of pending disruptions in higher education. We present a strategic map of the sector which hints at disruption and differentiation as an ongoing process, albeit with a large, undifferentiated middle. We write not to predict but to, hopefully, provoke thought. The challenges posed by the potential disruptors will, we argue, require many institutions to respond in new and innovative ways. Innovation in higher education which spread well beyond traditional research, knowledge transfer and the curriculum interpretations will be required to a much greater extent than in the past

Managing Access to Location Information

This publication describes systems and techniques directed to managing access to location information. A wireless-communication device, such as a smartphone, includes a sequestered location integrated circuit (IC) component that includes a location security manager application stored in memory circuitry of the sequestered location IC component. The wireless-communication device, using the sequestered location IC component, performs a technique that includes generating, from received data, a set of location information corresponding to a determined security-access level, encrypting the set of location information, and generating, for the encrypted set of location information, a security key that corresponds to the determined security-access level. The technique also includes determining that an application is allowed access to the encrypted set of location information corresponding to the determined security-access level and providing, to the application, the security key

A Neural Network Approach to Estimate Buoy Mooring Line Sensor Deflection

Instrumented moorings are often used to measure characteristics, such as temperature and current, over the water column. However, the moorings deflect from the effects of currents and waves, which could lead to innacurate measurements. In this work, a computationally efficient method to compensate for mooring sensor position errors is developed. The two-step process first uses a hydrodynamic model of the buoy and mooring line system to create estimated mooring line deflections in a steady current. A neural network model is trained to approximate the hydrodynamic model’s mooring line displacement given the spatial location of the buoy and current profile measurements. The method is illustrated using the Mackinac Straits West buoy that is part of the Upper Great Lakes Observing System (UGLOS). Its mooring line is instrumented with 10 thermistors, attached to the mooring line at varying intervals. Since the approach naturally provides interpolation, it allows researchers, with access to publicly available UGLOS data, to request temperatures at any depth. While the vertical deflection compensation method is illustrated here is for a particular mooring system, the process involved is applicable to a wide class of instrumented mooring systems. It was found that access to the current data of a mooring line increases the accuracy of the Neural Network, but knowing the position of the buoy in relation to the anchor can still give adequate results