Development and deployment of an autonomous micro-drilling system for cochleostomy

This thesis describes the design and development of an autonomous micro-drilling system capable of accurately controlling the penetration of complaint tissues and its application to the drilling of the cochleostomy; a key stage in the cochlea implant procedure. The drilling of the cochleostomy is a precision micro-surgical task in which the control of the burr penetration through the outer bone tissue of the cochlea is vital to prevent damage to the structures within and requires a high degree of skill to perform successfully. The micro-drilling system demonstrates that the penetration of the cochlea can be achieved consistently and accurately. Breakthrough can be detected and controlled to within 20µm of the distal surface and the hole completed without perforation of the underlying endosteal membrane, leaving the membranous cochlea intact. This device is the first autonomous surgical tool successfully deployed in the operating theatre. The system is unique due to the way in which it uses real-time data from the cutting tool to derive the state of the tool-tissue interaction. Being a smart tool it uses this state information to actively control the way in which the drilling process progresses. This sensor guided strategy enables the tool to self-reference to the deforming tissue and navigate without the need for pre-operative scan data. It is this capability that enables the system to operate in circumstances where the tissue properties and boundary conditions are unknown, without the need to restrain the patient.

Self-affirmation promotes physical activity

The present study tests whether a self-affirmation intervention (i.e., requiring an individual to focus on a valued aspect of their self-concept, such as honesty) can increase physical activity and change theory of planned behavior (TPB) variables linked to physical activity. Eighty young people completed a longitudinal intervention study. Baseline physical activity was assessed using the Godin Leisure-Time Physical Activity Questionnaire (LTPAQ). Next, participants were randomly allocated to either a self-affirmation or a nonaffirmation condition. Participants then read information about physical activity and health, and completed measures of TPB variables. One week later, participants again completed LTPAQ and TPB items. At follow up, self-affirmed participants reported significantly more physical activity, more positive attitudes toward physical activity, and higher intentions to be physically active compared with nonaffirmed participants. Neither attitudes nor intentions mediated the effects of self-affirmation on physical activity. Self-affirmation can increase levels of physical activity and TPB variables. Self-affirmation interventions have the potential to become relatively simple methods for increasing physical activity levels. © 2014 Human Kinetics, Inc.

A survey of self-awareness and its application in computing systems

Novel computing systems are increasingly being composed of large numbers of heterogeneous components, each with potentially different goals or local perspectives, and connected in networks which change over time. Management of such systems quickly becomes infeasible for humans. As such, future computing systems should be able to achieve advanced levels of autonomous behaviour. In this context, the system's ability to be self-aware and be able to self-express becomes important. This paper surveys definitions and current understanding of self-awareness and self-expression in biology and cognitive science. Subsequently, previous efforts to apply these concepts to computing systems are described. This has enabled the development of novel working definitions for self-awareness and self-expression within the context of computing systems.

Self-adaptive probabilistic roadmap generation for intelligent virtual agents

Agents inhabiting large scale environments are faced with the problem of generating maps by which they can navigate. One solution to this problem is to use probabilistic roadmaps which rely on selecting and connecting a set of points that describe the interconnectivity of free space. However, the time required to generate these maps can be prohibitive, and agents do not typically know the environment in advance. In this paper we show that the optimal combination of different point selection methods used to create the map is dependent on the environment, no point selection method dominates. This motivates a novel self-adaptive approach for an agent to combine several point selection methods. The success rate of our approach is comparable to the state of the art and the generation cost is substantially reduced. Self-adaptation therefore enables a more efficient use of the agent's resources. Results are presented for both a set of archetypal scenarios and large scale virtual environments based in Second Life, representing real locations in London.

Self-organization in social tagging systems

Individuals often imitate each other to fall into the typical group, leading to a self-organized state of typical behaviors in a community. In this paper, we model self-organization in social tagging systems and illustrate the underlying interaction and dynamics. Specifically, we introduce a model in which individuals adjust their own tagging tendency to imitate the average tagging tendency. We found that when users are of low confidence, they tend to imitate others and lead to a self-organized state with active tagging. On the other hand, when users are of high confidence and are stubborn to change, tagging becomes inactive. We observe a phase transition at a critical level of user confidence when the system changes from one regime to the other. The distributions of post length obtained from the model are compared to real data, which show good agreement. © 2011 American Physical Society.

Self-adapting parallel metric-space search engine for variable query loads

This research focuses on automatically adapting a search engine size in response to fluctuations in query workload. Deploying a search engine in an Infrastructure as a Service (IaaS) cloud facilitates allocating or deallocating computer resources to or from the engine. Our solution is to contribute an adaptive search engine that will repeatedly re-evaluate its load and, when appropriate, switch over to a dierent number of active processors. We focus on three aspects and break them out into three sub-problems as follows: Continually determining the Number of Processors (CNP), New Grouping Problem (NGP) and Regrouping Order Problem (ROP). CNP means that (in the light of the changes in the query workload in the search engine) there is a problem of determining the ideal number of processors p active at any given time to use in the search engine and we call this problem CNP. NGP happens when changes in the number of processors are determined and it must also be determined which groups of search data will be distributed across the processors. ROP is how to redistribute this data onto processors while keeping the engine responsive and while also minimising the switchover time and the incurred network load. We propose solutions for these sub-problems. For NGP we propose an algorithm for incrementally adjusting the index to t the varying number of virtual machines. For ROP we present an ecient method for redistributing data among processors while keeping the search engine responsive. Regarding the solution for CNP, we propose an algorithm determining the new size of the search engine by re-evaluating its load. We tested the solution performance using a custom-build prototype search engine deployed in the Amazon EC2 cloud. Our experiments show that when we compare our NGP solution with computing the index from scratch, the incremental algorithm speeds up the index computation 2{10 times while maintaining a similar search performance. The chosen redistribution method is 25% to 50% faster than other methods and reduces the network load around by 30%. For CNP we present a deterministic algorithm that shows a good ability to determine a new size of search engine. When combined, these algorithms give an adapting algorithm that is able to adjust the search engine size with a variable workload.

From the workplace to the classroom:examining the impact of self-leadership learning strategies on higher educational attainment and success

Self-leadership is a concept from the organisational and management literature broadly combining processes of self-goal setting, self-regulation and self-motivation. Research has typically focused on the impact of self-leadership on work performance outcomes, with little attention to potential benefits for learning and development. In this paper, we employ a longitudinal design to examine the association of a number of processes of self-leadership with higher educational attainment in a sample of business students (N = 150). Self-reported use of strategies related to behavioural, cognitive and motivational aspects of self-leadership were measured in the first semester of the academic year, and correlated with end-of year grade point average. We found that in particular, self-goal setting, pro-active goal-related behaviour, behaviour regulation and direction, motivational awareness, and optimism were all significant predictors of educational attainment. We discuss implications for educational research and for teachers and tutors in practice.