More bang for your buck: Super-adiabatic quantum engines

The practical untenability of the quasi-static assumption makes any realistic engine intrinsically irreversible and its operating time finite, thus implying friction effects at short cycle times. An important technological goal is thus the design of maximally efficient engines working at the maximum possible power. We show that, by utilising shortcuts to adiabaticity in a quantum engine cycle, one can engineer a thermodynamic cycle working at finite power and zero friction. Our findings are illustrated using a harmonic oscillator undergoing a quantum Otto cycle.

Big Ears – sonic art for public ears: Reflections on collaborative training

This text critically reflects on the higher education public engagement training program, entitled ‘Big Ears – sonic art for public ears’. The authors detail the objectives and aims as well as the benefits of this initiative for the enhancement of the student learning experience. We consider Schmidt’s (Schmidt, 2012) notion of mis-listening and Christopher Small’s concept of ‘musicking’ (Small, 1998), and develop a critical argument on how public engagement has changed researchers’ views and attitudes about their own research. The text explores how the creative interaction with a young audience has had great impact on the students’ learning experience as well as on their employability/transferable skills, because Big Ears stresses the importance of pulling practice as research away from the academic argument of why artists should be supported inside an institution, and into the realm of the real – what to do when making art, how to make it relevant and applicable to audiences.

Gerodontology--how big is the challenge in Ireland?

Population trends suggest that the Irish population is ageing, and that this population will have substantial treatment needs. These patients will be better informed than previous generations, and will demand treatment aimed at preserving a natural dentition. This will impact upon delivery of oral healthcare and manpower planning needs to consider how to address the increased demand for dental care. Poor oral health is associated with systemic health problems, including cardiovascular disease, respiratory disease and diabetes mellitus. It also has a negative impact upon quality of life, and the World Health Organisation has encouraged public healthcare administrators and decision makers to design effective and affordable strategies for better oral health and quality of life of older adults, which, in turn, are integrated into general health management programmes. Treatment concepts such as minimally invasive dentistry and the shortened dental arch concept are discussed in the context of these demographic changes and recommendations.

Towards a general framework for social media research using big data

The increasing adoption of cloud computing, social networking, mobile and big data technologies provide challenges and opportunities for both research and practice. Researchers face a deluge of data generated by social network platforms which is further exacerbated by the co-mingling of social network platforms and the emerging Internet of Everything. While the topicality of big data and social media increases, there is a lack of conceptual tools in the literature to help researchers approach, structure and codify knowledge from social media big data in diverse subject matter domains, many of whom are from nontechnical disciplines. Researchers do not have a general-purpose scaffold to make sense of the data and the complex web of relationships between entities, social networks, social platforms and other third party databases, systems and objects. This is further complicated when spatio-temporal data is introduced. Based on practical experience of working with social media datasets and existing literature, we propose a general research framework for social media research using big data. Such a framework assists researchers in placing their contributions in an overall context, focusing their research efforts and building the body of knowledge in a given discipline area using social media data in a consistent and coherent manner.

Supervised Aggregative Feature Extraction for Big Data Time Series Regression

In many applications, and especially those where batch processes are involved, a target scalar output of interest is often dependent on one or more time series of data. With the exponential growth in data logging in modern industries such time series are increasingly available for statistical modeling in soft sensing applications. In order to exploit time series data for predictive modelling, it is necessary to summarise the information they contain as a set of features to use as model regressors. Typically this is done in an unsupervised fashion using simple techniques such as computing statistical moments, principal components or wavelet decompositions, often leading to significant information loss and hence suboptimal predictive models. In this paper, a functional learning paradigm is exploited in a supervised fashion to derive continuous, smooth estimates of time series data (yielding aggregated local information), while simultaneously estimating a continuous shape function yielding optimal predictions. The proposed Supervised Aggregative Feature Extraction (SAFE) methodology can be extended to support nonlinear predictive models by embedding the functional learning framework in a Reproducing Kernel Hilbert Spaces setting. SAFE has a number of attractive features including closed form solution and the ability to explicitly incorporate first and second order derivative information. Using simulation studies and a practical semiconductor manufacturing case study we highlight the strengths of the new methodology with respect to standard unsupervised feature extraction approaches.