Integrating geophysical data and upscaling techniques to support regional groundwater flow modelling: A practical example of the Lagan Valley, Northern Ireland

When studying heterogeneous aquifer systems, especially at regional scale, a degree of generalization is anticipated. This can be due to sparse sampling regimes, complex depositional environments or lack of accessibility to measure the subsurface. This can lead to an inaccurate conceptualization which can be detrimental when applied to groundwater flow models. It is important that numerical models are based on observed and accurate geological information and do not rely on the distribution of artificial aquifer properties. This can still be problematic as data will be modelled at a different scale to which it was collected. It is proposed here that integrating geophysics and upscaling techniques can assist in a more realistic and deterministic groundwater flow model. In this study, the sedimentary aquifer of the Lagan Valley in Northern Ireland is chosen due to intruding sub-vertical dolerite dykes. These dykes are of a lower permeability than the sandstone aquifer. The use of airborne magnetics allows the delineation of heterogeneities, confirmed by field analysis. Permeability measured at the field scale is then upscaled to different levels using a correlation with the geophysical data, creating equivalent parameters that can be directly imported into numerical groundwater flow models. These parameters include directional equivalent permeabilities and anisotropy. Several stages of upscaling are modelled in finite element. Initial modelling is providing promising results, especially at the intermediate scale, suggesting an accurate distribution of aquifer properties. This deterministic based methodology is being expanded to include stochastic methods of obtaining heterogeneity location based on airborne geophysical data. This is through the Direct Sample method of Multiple-Point Statistics (MPS). This method uses the magnetics as a training image to computationally determine a probabilistic occurrence of heterogeneity. There is also a need to apply the method to alternate geological contexts where the heterogeneity is of a higher permeability than the host rock.

(Re)-Introducing Themes of Religion and Spirituality to Professional Social Work Training in the Land of ‘Saints and Scholars’.

This chapter begins by alluding to Ireland’s historical reputation as the land of “Saints and Scholars” and then briefly charts its demise from this position. A parallel process in relation to religiously motivated provision of health and social care is outlined. The inclusion of themes of religion and spirituality within the current professional social work codes in the USA and Britain and the framework for social work training in Northern Ireland is noted. In this context the lack of any substantive inclusion of themes of religion and/or spirituality within the Bachelor of Social Work (BSW) degree at Queens University Belfast will be situated. A series of intersecting reasons for this lack of inclusion are proposed in terms of the experience of living through the recent troubled history of Northern Ireland and a variety of biases in academic thought.
A rationale for the re-introduction of inputs on religion and spirituality is articulated in terms of the widespread resurgence of these themes within health and social care and psychotherapy literature and the new emphasis on practicing in culturally sensitive ways in Britain. The first steps to re-introduce these themes under the higher context marker of “culturally competent practice” are described and an analysis of data from the students’ feedback presented along with illustrative quotations. The dissonance between the initial misgivings of staff and the overwhelmingly positive responses of students are highlighted. The chapter concludes with a discussion of lessons learned through the process with an emphasis on how the inclusion of these themes can result in better practice for service users, including those impacted by “the Troubles” in Northern Ireland.

Automatic Analysis of Digital Retinal Images for Glaucoma Detection

In this paper we propose a novel automated glaucoma detection framework for mass-screening that operates on inexpensive retinal cameras. The proposed methodology is based on the assumption that discriminative features for glaucoma diagnosis can be extracted from the optical nerve head structures,
such as the cup-to-disc ratio or the neuro-retinal rim variation. After automatically segmenting the cup and optical disc, these features are feed into a machine learning classifier. Experiments were performed using two different datasets and from the obtained results the proposed technique provides
better performance than approaches based on appearance. A main advantage of our approach is that it only requires a few training samples to provide high accuracy over several different glaucoma stages.

Massive MIMO with Optimal Power and Training Duration Allocation

We consider the uplink of massive multicell multiple-input multiple-output systems, where the base stations (BSs), equipped with massive arrays, serve simultaneously several terminals in the same frequency band. We assume that the BS estimates the channel from uplink training, and then uses the maximum ratio combining technique to detect the signals transmitted from all terminals in its own cell. We propose an optimal resource allocation scheme which jointly selects the training duration, training signal power, and data signal power in order to maximize the sum spectral efficiency, for a given total energy budget spent in a coherence interval. Numerical results verify the benefits of the optimal resource allocation scheme. Furthermore, we show that more training signal power should be used at low signal-to-noise ratio (SNRs), and vice versa at high SNRs. Interestingly, for the entire SNR regime, the optimal training duration is equal to the number of terminals.