Search

The AI and Society discourse has previously drawn attention to the ways that digital systems embody the values of the technology development community from which they emerge through the development and deployment process. Research shows how this effect leads to a particular treatment of gender in computer systems development, a treatment which lags far behind the rich understanding of gender that social studies scholarship reveals and people across society experience. Many people do not relate to the narrow binary gender options of male or female, and many people express their gender identity in much richer ways than the sex/gender binary female/woman and male/man Boolean terms will allow. We ask: are "born-digital" gendered datasets in digital systems experienced as marginalising by those who express their identity beyond the male/female binary? Case Study: Ireland. To answer this universal question, this paper presents the findings of an empirical case study of people in Ireland with diverse gender identities and expressions, and their experiences with public data systems and new technologies. In spite of great social changes in Ireland which have led to constitutional change in favour of LGBTQI + people, born-digital systems were experienced by respondents as embodying socio-cultural values which were no longer accepted in society at large. For many of the respondents, digital technologies routinely marginalise them in all kinds of ways. These systems keep alive violence and oppression long after civil rights have been enshrined in constitutional law. This study is just one example of the way assumptions about digital are disengaged from society-at-large. It is a call to arms to all who are passionate about socially-responsible technology. [ABSTRACT FROM AUTHOR]

Artificial neural networks (ANNs) are generally regarded to behave as black-box systems. Recent research explores various methods that can provide an insight into the internal connections and relationships existing within the network. Various methodologies that understand the input variable contribution are analyzed in detail, and rule extraction approaches for a trained artificial neural network are addressed. To understand the contribution of input variables to rainfall-runoff model combination systems, this paper for the first time investigates knowledge extraction from artificial neural network, which is used to combine the results obtained from different competing rainfall-runoff models, using three different approaches: (1) Garson's algorithm; (2) neural interpretation diagram (NID); and (3) sensitivity analysis (SA). For the purpose of investigating knowledge extraction techniques, the trained multilayer perceptron neural network to combine the results from four different rainfall-runoff models for the Brosna Catchment located in Ireland has been chosen. The results of the three approaches obtained in this study indicate that they can be used to reduce the complexity of rainfall-runoff model combination systems by eliminating the least significant contributing input variables. Based on these approaches, the paper helps to provide guidance in the optimal number of rainfall-runoff models that best perform in a combination system. [ABSTRACT FROM AUTHOR]

This paper proposes a framework using a community maturity level metric to determine the integration readiness of interface elements in a particular network cluster. As a proof of concept this methodology is applied to an aircraft seating system to assess the readiness of complex interfaces before proceeding to full‐scale production and systems integration. A multi‐objective genetic algorithm, MOGA‐Net, is coupled with the Newman‐Girvan modularity metric as a clustering algorithm. This algorithm identifies system elements grouped by common interfaces, referred to as community clusters. The TRL and IRL values for these elements is then used to calculate an overall community maturity level. The achieved performance in these clusters is then compared to the target performance to determine overall maturity of the interfaces. This is compared to other system readiness metrics and interface readiness metrics as applied to the aircraft seating system and was found to be more consistent with subject matter expert evaluations during the critical design review. This gives a better representation of the true readiness of system interfaces before entering design reviews to reduce overall integration risks. [ABSTRACT FROM AUTHOR]

Aim: The aim of this study was to: (1) use cognitive task analysis to describe final year nursing students situation awareness in recognising, responding and escalating care of deteriorating patients in ward settings; and (2) make recommendations for training and practice. Design: A mixed methods cognitive task analysis with a convergent triangulation design. Method: Data collection involved observations of 33 final year nursing students in simulated deteriorating patient scenarios and retrospective cognitive interviews. A process tracing technique was applied to identify the cues to deterioration participants perceived; how cue perception altered as situational demands increased; the extent that participants made connections between perceived cues and reached a situational understanding; and the factors that influenced and constrained participants situation awareness. Qualitative and quantitative findings are woven together and presented using descriptive statistics, illustrative quotations and timeline extractions. Results: The median cue perception was 65.4% and 57.6% in the medical and surgical scenarios, respectively. Perception was negatively influenced by incomplete vital sign monitoring as situations escalated; limited physical assessments; passive scanning behaviours; poor task automaticity; and excessive cognitive demands. Incomplete perception, poor cue integration and underdeveloped mental models influenced situational understanding. Escalation calls did not always accurately reflect situations and a reporting mindset was evident. Clinical exposure to deteriorating patients was described as variable and opportunistic. Reporting Method: The study is reported in accordance with the Good Reporting of a Mixed Methods Study (GRAMMS) checklist. Patient or Public Contribution: Patients and public were not involved in this research. [ABSTRACT FROM AUTHOR]

Inverse reinforcement learning (IRL) aims to estimate the reward function of optimizing agents by observing their response (estimates or actions). This paper considers IRL when noisy estimates of the gradient of a reward function generated by multiple stochastic gradient agents are observed. We present a generalized Langevin dynamics algorithm to estimate the reward function R(θ); specifically, the resulting Langevin algorithm asymptotically generates samples from the distribution proportional to exp(R(θ)). The proposed adaptive IRL algorithms use kernel-based passive learning schemes. We also construct multi-kernel passive Langevin algorithms for IRL which are suitable for high dimensional data and achieve variance reduction. The performance of the proposed IRL algorithms are illustrated on examples in adaptive Bayesian learning, logistic regression (high dimensional problem) and constrained Markov decision processes. We prove weak convergence of the proposed IRL algorithms using martingale averaging methods. We also analyze the tracking performance of the IRL algorithms in non-stationary environments where the utility function R(θ) has a hyper-parameter that jump changes over time as a slow Markov chain which is not known to the inverse learner. In this case, martingale averaging yields a Markov switched diffusion limit as the asymptotic behavior of the IRL algorithm. [ABSTRACT FROM AUTHOR]

Highlights • Co-simulation and building energy models provide a framework to test control algorithms. • Machine learning algorithms are effective to develop predictive optimisation models. • Predictive controls for residential can reduce heating systems costs up to 40%. • Smart predictive control in buildings results in lower carbon footprint up to 39%. • The smart grid dynamically controls dwellings to balance electricity supply/demand. Abstract This paper assesses the performance of control algorithms for the implementation of demand response strategies in the residential sector. A typical house, representing the most common building category in Ireland, was fully instrumented and utilised as a test-bed. A calibrated building simulation model was developed and used to assess the effectiveness of demand response strategies under different time-of-use electricity tariffs in conjunction with zone thermal control. Two demand response algorithms, one based on a rule-based approach, the other based on a predictive-based (machine learning) approach, were deployed for control of an integrated heat pump and thermal storage system. The two algorithms were evaluated using a common demand response price scheme. Compared to a baseline reference scenario, the following reductions were observed: electricity end-use expenditure (20.5% rule-based and 41.8% predictive algorithm), utility generation cost (18.8% rule-based and 39% predictive algorithm), carbon emissions (20.8% rule-based and 37.9% predictive algorithm). [ABSTRACT FROM AUTHOR]

Background the aim of this study was to retrospectively operationalise the World Guidelines for Falls Prevention and Management (WGFPM) falls risk stratification algorithm using data from The Irish Longitudinal Study on Ageing (TILDA). We described how easy the algorithm was to operationalise in TILDA and determined its utility in predicting falls in this population. Methods participants aged ≥50 years were stratified as 'low risk', 'intermediate' or 'high risk' as per WGFPM stratification based on their Wave 1 TILDA assessments. Groups were compared for number of falls, number of people who experienced one or more falls and number of people who experienced an injury when falling between Wave 1 and Wave 2 (approximately 2 years). Results 5,882 participants were included in the study; 4,521, 42 and 1,309 were classified as low, intermediate and high risk, respectively, and 10 participants could not be categorised due to missing data. At Wave 2, 17.4%, 43.8% and 40.5% of low-, intermediate- and high-risk groups reported having fallen, and 7.1%, 18.8% and 18.7%, respectively, reported having sustained an injury from falling. Conclusion the implementation of the WGFPM risk assessment algorithm was feasible in TILDA and successfully differentiated those at greater risk of falling. The high number of participants classified in the low-risk group and lack of differences between the intermediate and high-risk groups may be related to the non-clinical nature of the TILDA sample, and further study in other samples is warranted. [ABSTRACT FROM AUTHOR]

Aims: To identify the costs associated with nurse sensitive adverse events and the impact of these events on patients' length of stay. Design: Retrospective cohort study using administrative hospital data. Methods: Data were sourced from patient discharge information (N = 5544) from six acute wards within three hospitals (July 2016-October 2017). A retrospective patient record review was undertaken by extracting data from the hospitals' administrative systems on inpatient discharges, length of stay and diagnoses; eleven adverse events sensitive to nurse staffing were identified within the administrative system. A negative binomial regression is employed to assess the impact of nurse sensitive adverse events on length of stay. Results: Sixteen per cent of the sample (n = 897) had at least one nurse sensitive adverse event during their episode of care. The model revealed when age, gender, admission type and complexity are controlled for, each additional nurse sensitive adverse event experienced by a patient was associated with an increase in the length of stay beyond the national average by 0.48 days (p = .001). Applying this to the daily average cost of inpatient stay per patient (€1456), we estimate the average cost associated with each nurse sensitive adverse event to be €694. Extrapolating this nationally, the economic cost of nurse sensitive adverse events to the health service in Ireland is estimated to be €91.3 million annually. Conclusion: These potentially avoidable events are associated with a significant economic burden to health systems. The estimates provided here can be used to inform and prepare the way for future economic evaluations of nurse staffing initiatives that aim to improve care and safety. Impact: As many of these nurse sensitive adverse events are avoidable, in addition to patient benefits, there is a potential substantial financial return on investment from strategies such as improved nurse staffing that can reduce their occurrence. [ABSTRACT FROM AUTHOR]

Background Physical activity (PA) interventions capable of producing health benefits cost effectively are a public health priority across the Western world. 'Men on the Move' (MOM), a community-based PA intervention for men, demonstrated significant health benefits up to 52-weeks (W) post-baseline. This article details the economic evaluation of MOM with a view to determining its cost-effectiveness as a public health intervention to be rolled out nationally in Ireland. Methods Cost-effectiveness was determined by comparing the costs (direct and indirect) of the programme to its benefits, which were captured as the impact on quality-adjusted life-years (QALYs). For the benefits, cost–utility analysis was conducted by retrospectively adapting various health-related measures of participants to generate health states using Brazier et al.'s (2002) short form-6D algorithm. This in turn allowed for 'utility measures' to be generated, from which QALYs were derived. Results Findings show MOM to be cost-effective in supporting an 'at risk' cohort of men achieves significant improvements in aerobic fitness, weight loss and waist reduction. The total cost per participant (€125.82 for each of the 501 intervention participants), the QALYs gained (11.98 post-12-W intervention, or 5.3% health improvement per participant) and estimated QALYs ratio costs of €3723 represents a cost-effective improvement when compared to known QALY guidelines. Conclusions The analysis shows that the cost per QALY achieved by MOM is significantly less than the existing benchmarks of £20 000 and €45 000 in the UK and Ireland respectively, demonstrating MOM to be cost-effective. [ABSTRACT FROM AUTHOR]

We examined recent atmospheric mercury concentrations measured with a high temporal resolution of 15 min at Mace Head, a GAW station on the western coast of Ireland. We attributed a direct contribution of 34 % (0.44 ngm-3) to primary sources. Additionally, a steep decline (0.05 ngyr-1) in mercury concentrations was observed between 2013 and 2018. Using a stereo algorithm we reconstructed 99.9 % of the atmospheric mercury. A conservative analysis demonstrated no decrease in total gaseous mercury (TGM) associated with atmospheric species typically used as tracers for oceanic emissions. The results show that the atmospheric mercury mass is mainly loaded in a baseline factor with an ongoing decline. Moreover, we exploit temporal variation and wind pattern effects in the measured atmospheric species; the results show that the diurnal variation and seasonality in TGM observed in Mace Head are closely related to other species linked to primary sources and can be explained by transport from continental areas. [ABSTRACT FROM AUTHOR]

Purpose: Older people with several chronic conditions require multiple drugs from different classes to be adequately treated. This study aims to: (i) measure medication adherence across multiple conditions and therapeutic drug groups in older community-dwelling patients, and (ii) examine the effect of multimorbidity on adherence.Methods: This is a retrospective cohort study of medication adherence in 855 community-dwelling patients aged ≥ 70 years from 15 practices in Ireland using the Health Service Executive Primary Care Reimbursement Service (HSE-PCRS) pharmacy claims database. Multimorbidity was measured using the RxRisk-V and by the number of different drug classes. The RxRisk-V algorithm classifies prescription drug fills into 45 chronic disease classes for older populations based on the WHO Anatomical Therapeutic Chemical classification system. Adherence to medications was assessed by: (i) calculating the average medication possession ratio (MPR) per patient and (ii) an MPR< 80%.Results: The overall median MPR for the cohort was 0.83 (IQR 0.69, 0.91). The conditions with the highest MPRs were hypothyroidism (mean MPR = 0.88, SD = 0.20) and type 2 diabetes (mean MPR = 0.83, SD = 0.19), followed by heart disease. On average, 20–40% of patients were non-adherent (MPR < 80%) across all conditions. There was an inverted U-shaped relationship between the mean MPR and number of morbidities and drug classes. Adherence varied per patients’ morbidity burden, with higher adherence for certain combinations of chronic conditions.Conclusion: In total, 31% of older patients with multimorbidity were non-adherent to their medication but adherence levels varied across treatment categories and chronic conditions. [ABSTRACT FROM AUTHOR]

A common feature of the Hungarian, Irish, Spanish and Turkish higher education admission systems is that students apply for programmes and are ranked according to their scores. Students who apply for a programme with the same score are tied. Ties are broken by lottery in Ireland, by objective factors in Turkey (such as date of birth) and by other precisely defined rules in Spain. In Hungary, however, an equal treatment policy is used, students applying for a programme with the same score are all accepted or rejected together. In such a situation there is only one decision to make, whether or not to admit the last group of applicants with the same score who are at the boundary of the quota. Both concepts can be described in terms of stable score-limits. The strict rejection of the last group with whom a quota would be violated corresponds to the concept of H-stable (i.e. higher-stable) score-limits that is currently used in Hungary. We call the other solutions based on the less strict admission policy as L-stable (i.e. lower-stable) score-limits. We show that the natural extensions of the Gale-Shapley algorithms produce stable score-limits, moreover, the applicant-oriented versions result in the lowest score-limits (thus optimal for students) and the college-oriented versions result in the highest score-limits with regard to each concept. When comparing the applicant-optimal H-stable and L-stable score-limits we prove that the former limits are always higher for every college. Furthermore, these two solutions provide upper and lower boundaries for any solution arising from a tie-breaking strategy. Finally we show that both the H-stable and the L-stable applicant-proposing score-limit algorithms are manipulable. [ABSTRACT FROM AUTHOR]

Browne D, Whelton H, O'Mullane D, Tavener J, Flannery E. The aesthetic impact of enamel fluorosis on Irish adolescents. Community Dent Oral Epidemiol 2011; 39: 127-136. © 2011 John Wiley & Sons A/S To assess the impact of differing degrees of enamel fluorosis on dental aesthetics according to Irish adolescents. The same participants also aesthetically rated other variations in dental appearances including a carious lesion, bleached teeth and a demarcated opacity. One hundred and fifty adolescents examined seven identical template photographs of an attractive dental smile displaying varying levels of enamel fluorosis (TF1, TF2, TF3), a demarcated opacity, no fluorosis (TF0), anterior caries and very white or bleached teeth. By indicating their level of agreement or disagreement with five statements on a five-point Likert scale, the participants rated the aesthetic acceptability of each of the photographs. Using paired t-tests with the Bonferroni correction, it was found that the photographs depicting the very white teeth and anterior caries were rated as the most and least aesthetically pleasing images, respectively. There was no significant difference in the ratings of the photographs displaying TF0, TF1 and TF2 levels of fluorosis indicating that these photographs were viewed similarly ( P > 0.002). The remaining two photographs (TF3 and the demarcated opacity) were rated similarly and significantly worse ( P < 0.002) than the photographs showing no or low grades of fluorosis (TF0, TF1 and TF2). TF3 level of fluorosis represented the break point at which enamel fluorosis became aesthetically objectionable to these participants. Low grades of fluorosis (TF1 and TF2) were rated similarly to the photograph depicting no fluorosis (TF0). [ABSTRACT FROM AUTHOR]

Two non-parametric estimation techniques were tested in two study areas in Ireland. For each area, plot level estimates of standing volume per hectare and basal area per hectare were computed from the National Forest Inventory field data and combined with SPOT 4 XS satellite imagery and a digital elevation model to form a set of observations. These observations were then used to predict variables across the satellite image using k-Nearest Neighbour (kNN) estimation and a Random Forest algorithm. Comparisons between the two techniques were assessed based on the estimation errors primarily using the Root Mean Square Error (RMSE) and relative mean deviation (bias). In both study areas it was found that the RMSE was lower for kNN than for RF. Overall, the RMSEs and mean deviations were lower in Study Area 1 when compared to Study Area 2, largely due to a difference in the number of available NFI reference plots. [ABSTRACT FROM AUTHOR]

As part of the Vertical Offshore Reference Frames (VORF) project sponsored by the U. K. Hydrographic Office, a new model for Sea Surface Topography (SST) around the British Isles has been developed. For offshore areas (greater than 30 km from the coast), this model is largely derived from satellite altimetry. However, its accuracy and level of detail have been enhanced in coastal areas by the inclusion of not only the 60 PSMSL tide gauges with long-term records around the coasts of the United Kingdom and Ireland but also some 385 gauges established at different epochs and for different observation spans by the U. K. Admiralty. All tide gauge data were brought into a common reference frame by a combination of datum models and direct GPS observations, but a more significant challenge was to bring all short-term sea level observations to an unbiased value at a common epoch. This was achieved through developing a spatial-temporal correlation model for the variations in mean sea level around the British Isles, which in turn meant that gauges with long-term observation spans could be used as control points to improve the accuracy of Admiralty gauges. It is demonstrated that the latter can contribute point observations of mean sea level (MSL) with a precision of 0.078 m. A combination of least squares collocation and interpolation was developed to merge the coastal point and offshore gridded data sets, with particular algorithms having to be developed for different configurations of coastal topology. The resulting model of sea surface topography is shown to present a smooth transition from inshore coastal areas to offshore zones. Further benefits of the techniques developed include an enhanced methodology for detecting datum discontinuities at permanent tide gauges. [ABSTRACT FROM AUTHOR]

Many spatial data such as those in climatology or environmental monitoring are collected over irregular geographical locations. Furthermore, it is common to have multivariate observations at each location. We propose a method of segmentation of a region of interest based on such data that can be carried out in two steps: (1) clustering or classification of irregularly sample points and (2) segmentation of the region based on the classified points. We develop a spatially-constrained clustering algorithm for segmentation of the sample points by incorporating a geographical-constraint into the standard clustering methods. Both hierarchical and nonhierarchical methods are considered. The latter is a modification of the seeded region growing method known in image analysis. Both algorithms work on a suitable neighbourhood structure, which can for example be defined by the Delaunay triangulation of the sample points. The number of clusters is estimated by testing the significance of successive change in the within-cluster sum-of-squares relative to a null permutation distribution. The methodology is validated on simulated data and used in construction of a climatology map of Ireland based on meteorological data of daily rainfall records from 1294 stations over the period of 37 years. [ABSTRACT FROM AUTHOR]