The usefulness of genomic prediction in crop and livestock breeding programs has prompted efforts to develop new and improved genomic prediction algorithms, such as artificial neural networks and gradient tree boosting. However, the performance of these algorithms has not been compared in a systematic manner using a wide range of datasets and models. Using data of 18 traits across six plant species with different marker densities and training population sizes, we compared the performance of six linear and six non-linear algorithms. First, we found that hyperparameter selection was necessary for all non-linear algorithms and that feature selection prior to model training was critical for artificial neural networks when the markers greatly outnumbered the number of training lines. Across all species and trait combinations, no one algorithm performed best, however predictions based on a combination of results from multiple algorithms (i.e., ensemble predictions) performed consistently well. While linear and non-linear algorithms performed best for a similar number of traits, the performance of non-linear algorithms vary more between traits. Although artificial neural networks did not perform best for any trait, we identified strategies (i.e., feature selection, seeded starting weights) that boosted their performance to near the level of other algorithms. Our results highlight the importance of algorithm selection for the prediction of trait values.

Microservices architecture has gained significant traction, in part owing to its potential to deliver scalable, robust, agile, and failure-resilient software products. Consequently, many companies that use large and complex software systems are actively looking for automated solutions to decompose their monolith applications into microservices. This paper rigorously examines 35 research papers selected from well-known databases using a Systematic Literature Review (SLR) protocol and snowballing method, extracting data to answer the research questions, and presents the following four contributions. First, the Monolith to Microservices Decomposition Framework (M2MDF) which identifies the major phases and key elements of decomposition. Second, a detailed analysis of existing decomposition approaches, tools and methods. Third, we identify the metrics and datasets used to evaluate and validate monolith to microservice decomposition processes. Fourth, we propose areas for future research. Overall, the findings suggest that monolith decomposition into microservices remains at an early stage and there is an absence of methods for combining static, dynamic, and evolutionary data. Insufficient tool support is also in evidence. Furthermore, standardised metrics, datasets, and baselines have yet to be established. These findings can assist practitioners seeking to understand the various dimensions of monolith decomposition and the community's current capabilities in that endeavour. The findings are also of value to researchers looking to identify areas to further extend research in the monolith decomposition space.

The objective of this study was to identify performance indicators which differentiated between winning and losing elite Gaelic football teams. Eighty three technical and tactical performance variables were measured in 13 teams during 26 league and championship games throughout 2014–2015. Univariate analysis of full-games revealed that winners achieved a significantly higher total score, number of scores, shots, points, points from play and goals, resulting in superior shot efficiency, average attack per score, and scores per 10 possessions. Winners gained significantly more turnovers and completed significantly less unsuccessful hand passes. Winners also performed significantly less kick outs, resulting in fewer successful kick outs and successful dead ball kick passes overall. A principal component analysis, conducted on 18 variables produced 4 components, which explained 81.9% of the variance. Both logistic regression (8.00, χ²(1) = 16.00, p < 0.001) and discriminant analysis (Ʌ = 0.53, χ²(1) = 13.77, p < 0.001) revealed that one component, defensive counterattacking, significantly contributed to outcome and differentiated winners from losers with a cross-validation accuracy of 87.5%. Coaches can use this information to organise their defensive system to generate opposition turnovers and also incorporate sufficient flexibility to facilitate effective transitions to exploit their own offensive counterattacking opportunities.

Cullen, BD, Roantree, M, McCarren, A, Kelly, DT, O'Connor, PL, Hughes, SM, Daly, PG, and Moyna1, NM. Physiological profile and activity pattern of minor Gaelic football players. J Strength Cond Res 31(7): 1811–1820, 2017—The purpose of this study was to evaluate the physiological profile and activity pattern in club- and county-level under-18 (U-18) Gaelic football players relative to playing position. Participants (n = 85) were analyzed during 17 official 15-a-side matches using global positioning system technology (SPI Pro X II; GPSports Systems, Canberra, Australia) and heart rate (HR) telemetry. During the second part of this study, 63 participants underwent an incremental treadmill test to assess their maximal oxygen uptake (V̇o₂max) and peak HR (HRmax). Players covered a mean distance of 5,774 ± 737 m during a full 60-minute match. The mean %HRmax and %V̇o ₂ max observed during the match play were 81.6 ± 4.3% and 70.1 ± 7.75%, respectively. The playing level had no effect on the distance covered, player movement patterns, or %HRmax observed during match play. Midfield players covered significantly greater distance than defenders (p = 0.033). Playing position had no effect on %HRmax or the frequency of sprinting or high-intensity running during match play. The frequency of jogging, cruise running, striding (p = 0.000), and walking (p = 0.003) was greater in the midfield position than in the forward position. Time had a significant effect (F _(1,39) = 33.512, p-value = 0.000, and JOURNAL/jscr/04.03/00124278-201707000-00008/inline-formula1/v/2023-08-19T002737Z/r/image-tiff = 0.462) on distance covered and %HRmax, both of which showed a reduction between playing periods. Gaelic football is predominantly characterized by low-to-moderate intensity activity interspersed with periods of high-intensity running. The information provided may be used as a framework for coaches in the design and prescription of training strategies. Positional specific training may be warranted given the comparatively greater demands observed in the midfield playing position. Replicating the demands of match play in training may reduce the decline in distance covered and %HRmax observed during the second half of match play.