Electropermanent magnetic anchoring for surgery and endoscopy

The use of magnets for anchoring of instrumentation in minimally invasive surgery and endoscopy has become of increased interest in recent years. Permanent magnets have significant advantages over electromagnets for these applications; larger anchoring and retraction force for comparable size and volume without the need for any external power supply. However, permanent magnets represent a potential hazard in the operating field where inadvertent attraction to surgical instrumentation is often undesirable. The current work proposes an interesting hybrid approach which marries the high forces of permanent magnets with the control of electromagnetic technology including the ability to turn the magnet OFF when necessary. This is achieved through the use of an electropermanent magnet, which is designed for surgical retraction across the abdominal and gastric walls. Our electropermanent magnet, which is hand-held and does not require continuous power, is designed with a center lumen which may be used for trocar or needle insertion. The device in this application has been demonstrated successfully in the porcine model where coupling between an intraluminal ring magnet and our electropermanent magnet facilitated guided insertion of an 18 Fr Tuohy needle for guidewire placement. Subsequent investigations have demonstrated the ability to control the coupling distance of the system alleviating shortcomings with current methods of magnetic coupling due to variation in transabdominal wall thicknesses. With further refinement, the magnet may find application in the anchoring of endoscopic and surgical instrumentation for minimally invasive interventions in the gastrointestinal tract.

Theory and optimisation of 1.3 and 1.55 μm (Al)InGaAs metamorphic quantum well lasers

The use of InGaAs metamorphic buffer layers (MBLs) to facilitate the growth of lattice-mismatched heterostructures constitutes an attractive approach to developing long-wavelength semiconductor lasers on GaAs substrates, since they offer the improved carrier and optical confinement associated with GaAs-based materials. We present a theoretical study of GaAs-based 1.3 and 1.55 μm (Al)InGaAs quantum well (QW) lasers grown on InGaAs MBLs. We demonstrate that optimised 1.3 μm metamorphic devices offer low threshold current densities and high differential gain, which compare favourably with InP-based devices. Overall, our analysis highlights and quantifies the potential of metamorphic QWs for the development of GaAs-based long-wavelength semiconductor lasers, and also provides guidelines for the design of optimised devices.

User interaction monitoring and analysis framework

An overview is given of a user interaction monitoring and analysis framework called BaranC. Monitoring and analysing human-digital interaction is an essential part of developing a user model as the basis for investigating user experience. The primary human-digital interaction, such as on a laptop or smartphone, is best understood and modelled in the wider context of the user and their environment. The BaranC framework provides monitoring and analysis capabilities that not only records all user interaction with a digital device (e.g. smartphone), but also collects all available context data (such as from sensors in the digital device itself, a fitness band or a smart appliances). The data collected by BaranC is recorded as a User Digital Imprint (UDI) which is, in effect, the user model and provides the basis for data analysis. BaranC provides functionality that is useful for user experience studies, user interface design evaluation, and providing user assistance services. An important concern for personal data is privacy, and the framework gives the user full control over the monitoring, storing and sharing of their data.

A pro-active and dynamic prediction assistance using BaranC framework

Monitoring user interaction activities provides the basis for creating a user model that can be used to predict user behaviour and enable user assistant services. The BaranC framework provides components that perform UI monitoring (and collect all associated context data), builds a user model, and supports services that make use of the user model. In this case study, a Next-App prediction service is built to demonstrate the use of the framework and to evaluate the usefulness of such a prediction service. Next-App analyses a user's data, learns patterns, makes a model for a user, and finally predicts based on the user model and current context, what application(s) the user is likely to want to use. The prediction is pro-active and dynamic; it is dynamic both in responding to the current context, and also in that it responds to changes in the user model, as might occur over time as a user's habits change. Initial evaluation of Next-App indicates a high-level of satisfaction with the service.

Learning curves: analysing pace and challenge in four successful puzzle games

The pace at which challenges are introduced in a game has long been identified as a key determinant of both the enjoyment and difficulty experienced by game players, and their ability to learn from game play. In order to understand how to best pace challenges in games, there is great value in analysing games already demonstrated as highly engaging. Play-through videos of four puzzle games (Portal, Portal 2 Co-operative mode, Braid and Lemmings), were observed and analysed using metrics derived from a behavioural psychology understanding of how people solve problems. Findings suggest that; 1) the main skills learned in each game are introduced separately, 2) through simple puzzles that require only basic performance of that skill, 3) the player has the opportunity to practice and integrate that skill with previously learned skills, and 4) puzzles increase in complexity until the next new skill is introduced. These data provide practical guidance for designers, support contemporary thinking on the design of learning structures in games, and suggest future directions for empirical research.