Competitive level differences in the activity profile of elite Gaelic football referees.

The purpose of this study was to compare the activity profile of elite Gaelic football referees (GFR) between the National Football League (NFL) and the All-Ireland Championship (AIC), and across the four divisions of the NFL and three phases of the AIC. Match activity data was collected during 125 NFL and 201 AIC games using 10-Hz global positioning system technology from 41 elite GFR. Game duration, total distance, very low-speed movement (<0.70 m·s-1), walking (≥0.70-1.65 m·s-1), low-speed running (≥1.66-3.27 m·s-1), moderate-speed running (≥3.28-4.86 m·s-1), high-speed running (≥4.87-6.48 m·s-1), very high-speed running (≥6.49 m·s-1) distance, and peak running speed were compared between competitions. Games in the AIC were longer than in the NFL (ES = 0.59) but the total distance was similar between the NFL (119.6 ± 9.5 m·min-1) and AIC (122.6 ± 8.4 m·min-1, ES = 0.11). No other differences were found between the NFL and AIC or across the four divisions of the NFL and three phases of the AIC, except for a higher peak running speed during the All-Ireland Series (6.93 ± 0.52 m·s-1) than the All-Ireland Qualifiers (6.65 ± 0.46 m·s-1, ES = 0.35). This information can be used to design specific conditioning programmes to ensure optimal physical development of GFR at all competitive levels.

An Ostensive Information Architecture to Enhance Semantic Interoperability for Healthcare Information Systems.

Semantic interoperability establishes intercommunications and enables data sharing across disparate systems. In this study, we propose an ostensive information architecture for healthcare information systems to decrease ambiguity caused by using signs in different contexts for different purposes. The ostensive information architecture adopts a consensus-based approach initiated from the perspective of information systems re-design and can be applied to other domains where information exchange is required between heterogeneous systems. Driven by the issues in FHIR (Fast Health Interoperability Resources) implementation, an ostensive approach that supplements the current lexical approach in semantic exchange is proposed. A Semantic Engine with an FHIR knowledge graph as the core is constructed using Neo4j to provide semantic interpretation and examples. The MIMIC III (Medical Information Mart for Intensive Care) datasets and diabetes datasets have been employed to demonstrate the effectiveness of the proposed information architecture. We further discuss the benefits of the separation of semantic interpretation and data storage from the perspective of information system design, and the semantic reasoning towards patient-centric care underpinned by the Semantic Engine.

Activity profile of elite Gaelic football referees during competitive match play.

The purpose of this study was to examine the activity profile of elite Gaelic football referees (GFR) and to examine temporal changes between the first and second half and across the four quarters. Global positioning systems technology (10-Hz) was used to collect activity data during 202 competitive games from 23 elite GFR. Relative distance, peak running speed and relative distance covered in six movement categories [very low-speed movement (VLSM) (<0.70 m·s-1), walking (≥0.70-1.65 m·s-1), low-speed running (LSR) (≥1.66-3.27 m·s-1), moderate-speed running (MSR) (≥3.28-4.86 m·s-1), high-speed running (HSR) (≥4.87-6.48 m·s-1), very high-speed running (VHSR) (≥6.49 m·s-1)] were examined during the full game, first and second half, and across the four quarters. The relative distance covered was 122.6 ± 8.4 m·min-1, with 13.1 ± 4.9 m·min-1 of HSR and VHSR. The peak running speed was 6.75 ± 0.49 m·s-1. The relative (ES=0.60), MSR (ES=0.50) and HSR (ES=0.14) distance was higher in the first half than the second half. A higher relative (ES=0.62-0.91) and HSR (ES=0.51-0.61) distance was found in the first quarter than any other period. No differences in HSR distance were found between the second, third and fourth quarters (ES=0.04-0.10). This study provides, for the first time, a detailed insight into the activity profile of elite GFR during competitive games and demonstrates the demanding, intermittent nature of elite refereeing in Gaelic football. This information may be used as a framework for coaches to design training programmes specific to GFR.

Using artificial intelligence to automate meat cut identification from the semimembranosus muscle on beef boning lines.

The identification of different meat cuts for labeling and quality control on production lines is still largely a manual process. As a result, it is a labor-intensive exercise with the potential for not only error but also bacterial cross-contamination. Artificial intelligence is used in many disciplines to identify objects within images, but these approaches usually require a considerable volume of images for training and validation. The objective of this study was to identify five different meat cuts from images and weights collected by a trained operator within the working environment of a commercial Irish beef plant. Individual cut images and weights from 7,987 meats cuts extracted from semimembranosus muscles (i.e., Topside muscle), post editing, were available. A variety of classical neural networks and a novel Ensemble machine learning approaches were then tasked with identifying each individual meat cut; performance of the approaches was dictated by accuracy (the percentage of correct predictions), precision (the ratio of correctly predicted objects relative to the number of objects identified as positive), and recall (also known as true positive rate or sensitivity). A novel Ensemble approach outperformed a selection of the classical neural networks including convolutional neural network and residual network. The accuracy, precision, and recall for the novel Ensemble method were 99.13%, 99.00%, and 98.00%, respectively, while that of the next best method were 98.00%, 98.00%, and 95.00%, respectively. The Ensemble approach, which requires relatively few gold-standard measures, can readily be deployed under normal abattoir conditions; the strategy could also be evaluated in the cuts from other primals or indeed other species.

A Methodology for Validating Diversity in Synthetic Time Series Generation.

In order for researchers to deliver robust evaluations of time series models, it often requires high volumes of data to ensure the appropriate level of rigor in testing. However, for many researchers, the lack of time series presents a barrier to a deeper evaluation. While researchers have developed and used synthetic datasets, the development of this data requires a methodological approach to testing the entire dataset against a set of metrics which capture the diversity of the dataset. Unless researchers are confident that their test datasets display a broad set of time series characteristics, it may favor one type of predictive model over another. This can have the effect of undermining the evaluation of new predictive methods. In this paper, we present a new approach to generating and evaluating a high number of time series data. The construction algorithm and validation framework are described in detail, together with an analysis of the level of diversity present in the synthetic dataset.