Moving well-being well: Using machine learning to explore the relationship between physical literacy and well-being in children.

Physical literacy provides a foundation for lifelong engagement in physical activity, resulting in positive health outcomes. Direct pathways between physical literacy and health have not yet been investigated thoroughly. Associations between physical literacy and well-being in children (n = 1073, mean age 10.86 ± 1.20 years) were analysed using machine learning. Motor competence (TGMD-3 and BOT-2) and health-related fitness (PACER and plank) were assessed in the physical competence domain. Motivation (adapted-Behavioural Regulation in Exercise Questionnaire) and confidence (modified-Physical Activity Self-Efficacy Scale) were assessed in the affective domain. Well-being was measured using the KIDSCREEN-27. Accuracy of predicting well-being from physical literacy was investigated using five machine learning classifiers (decision tree, random forest, XGBoost, AdaBoost, k-nearest neighbour) in the full sample and across subgroups (sex, socioeconomic status [SES], age). XGBoost predicted well-being from physical literacy with an accuracy of 87% in the full sample. Predictive accuracy was lowest in low SES participants. Contribution of physical literacy features differed substantially across subgroups. Physical literacy predicts well-being in children but the relative contribution of physical literacy features to well-being differs substantially between subgroups.

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.